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Intestinal parasitoses are a major concern for public health , especially in children from middle and low-income populations of tropical and subtropical areas . We examined the presence and co-infection of parasites in humans as well as parasitic environmental contamination in Puerto Iguazú , Argentina . We explored the environmental and socio-demographic characteristics of the persistence of parasites in children and their environment . This cross-section survey was conducted among children population comprised into the area of the public health care centers of Iguazú during June 2013 to May 2016 . Copro-parasitological status of 483 asymptomatic children was assessed . Simultaneously , a design-based sampling of 744 soil samples and 530 dog feces was used for characterize the environmental contamination . The 71 . 5% of these sites were contaminated . Sixteen genera were detected in the environment being hookworms ( 62 . 0% ) the main pathogens group detected followed by Toxocara spp ( 16 . 3% ) , Trichuris spp ( 15 . 2% ) and Giardia ( 6 . 5% ) . Total children prevalence raised 58 . 8% , detecting twelve genera of parasite with Giardia intestinalis as the most prevalent pathogen ( 29 . 0% ) followed by Enterobius vermicularis ( 23 . 0% ) , Hymenolepis nana ( 12 . 4% ) and hookworms ( 4 . 4% ) . Through questionnaires and census data , we characterized the socio-demographics conditions at an individual , family and neighborhood levels . A multi-level analysis including environmental contamination data showed that the ´presence of parasites´ was mostly determined by individual ( e . g . age , playing habits , previous treatment ) and household level ( e . g . UBN , WASH , mother's literacy ) determinants . Remarkably , to define the level of ‘parasite co-infection’ , besides individual and household characteristics , environmental factors at a neighborhood level were important . Our work represents the major survey of intestinal parasites in human and environmental samples developed in the region . High prevalence was detected in the children population as well as in their environment . This work shows the importance of considering and promoting multi-level actions over the identified determinants to face this public health problem from integrative approaches .
Infectious diseases associated with the gastrointestinal tract continue to be a major public health concern , especially in middle and low-income populations from tropical and subtropical areas of the developing world [1 , 2] . Gastrointestinal parasitoses caused by intestinal protozoan ( IP ) ( e . g . Giardia intestinalis , Cryptosporidium spp . and Entamoeba histolytica ) and Soil Transmitted Helminths ( STH , referring to Ascaris lumbricoides , Trichuris trichiura , and hookworms ) are endemic and the most prevalent parasitic infections in these regions [3] . Most of these gastrointestinal parasitoses are neglected tropical diseases ( NTDs ) and they are included in the WHO goals 2020 for the control or elimination of NTDs [3 , 4] . Among the reasons for this high prevalence are the multiple factors involved in the maintenance and propagation of these pathogens . There is agreement about the crucial role of the social and economic context in human health [2 , 5] , but also about the important role of environmental factors influencing the health of the population [6 , 7] . In the last decade , a multilevel theoretical platform for health has been established from an eco-social perspective , which emphasizes a systemic way in understanding determinants of health [8 , 9] . All these can be envisioned in the “One Health” paradigm which emphasizes integration among the disciplines from the environmental , human and animal health sectors , endorsing a pluralistic vision on public health issues [8–10] . The social and environmental factors underlying the transmission of intestinal pathogens are varied and different in nature . Enteroparasites infections are commonly associated with the age [11–13] , hygiene habits and nutritional or immunological conditions [14–18] of the affected children , indicating the importance of the characteristics at the individual level . Socio-environmental factors at the family or community levels also have been demonstrated as critical because of the relationship between intestinal parasites and WASH inequalities ( water , sanitation & hygiene ) [19–22] , parents’ education level [23 , 24] and sanitization and health of pets [25–28] among others . Given the characteristics of the life cycle of intestinal parasites , the environment is also a key player in the maintenance of these infections either as sites of maturation to their infective forms as well as dispersion vehicle [1 , 29] . Although there are studies that address these different factors [11 , 18 , 30 , 31] , they used to deal with few components or only at one level or scale preventing the possibility of weighing and interpreting all these factors and levels acting together . Puerto Iguazú is a border municipality located in the Misiones province at the northeastern extreme area of Argentina . Although socioeconomic and physical environments of this region are speculated in favor of high prevalence of intestinal parasites for Misiones province [32] , information is very limited and restricted to the central area of the province [18 , 33–35] . In Puerto Iguazú there is no systematic research on intestinal parasites despite sporadic information pointing to the possible importance of STHs as a public health issue in the area . The population of Iguazú is among those fastest growing in the nation in the last ten years [36] , promoting a rapid transformation of the urban and rural areas [37] . Furthermore , evidence from recent national data suggests that population of this region are among the poorest in the country [38] . In addition , the confluence of immigrants from several European countries , mestizo population , and aboriginal communities Mbyá-Guaraní , suggest a great socio-cultural diversity , heterogeneity of socio-economic conditions among the region’s residents and their inequalities in the access to the health system [39] . The epidemiologic importance increases since the municipality borders with Paraguay and Brazil , two countries with a high prevalence of STHs and IP [40–42] , and the city represents an important tourist center because of the Iguazú falls , one of the new Natural World Wonders . Taken into consideration all these aspects , the main goals of this study are to examine the human prevalence of enteroparasites and parasite environmental contamination in Puerto Iguazú and , through a multilevel approach , to explore environmental and socio-demographic characteristics of the persistence of parasites in children and their environment .
Ethical approval was obtained from Bioethical Committee of the Hospital Dr Ramón Madariaga from Posadas city at Misiones province , Argentina . We especially attend national regulation concerning personal data protection national law No . 25 . 326/2000 and the principles expressed in the Declaration of Helsinki . Written informed consent from parents or legal guardians was obtained during a household visit that included an explanation of study significance , participant requirements and rights , data about samples collection and socio-demographics questionnaires as well as the opportunity to ask questions . In order to manage illiteracy parents , besides oral explanations , illustrated instructions were also included with the collection kit . Each Public Health Care Center provided anti-parasitic treatment to each positive child under the consultation of a physician . In those cases which demand treatment for the family group , it was properly provided . The national drug policy throughout the REMEDIAR program ( http://www . remediar . msal . gov . ar/ ) manages and ensures drugs distribution and availability to each PHCC . No follow-up stool samples analysis after treatment was performed since it was beyond the scope of the project . It was not needed the Institutional Review Board approval for the collection and use of dogs´ feces samples since they were collected from public places and the individual characterization was not performed . Puerto Iguazú city is located in Misiones province ( 25°35‘52”S and 54° 34’ 55”W ) , a subtropical province of northeastern Argentina ( Fig 1 ) and it is part of the most biodiverse region in Argentina ( named the Upper Parana Atlantic Forest ecoregion ) [39] . The region is characterized by a subtropical climate with no dry season . The predominant soil type is lateritic of deep red color [39] . The municipality includes the city of Puerto Iguazú , peri-urban and rural areas , and a large area covered by native forests ( parks and reserves ) . There are also Mbyá Guaraní aboriginal villages at city periphery . Cross-sectional surveys on humans and environment were carried out during June 2013 and May 2016 in Puerto Iguazú city . It involved two main simultaneous study designs ( see below ) directed to assess the environmental contamination of intestinal parasites and entero-parasitic infections among children . Public areas were included for the environmental survey and for the children survey we assessed the neighborhoods around the Public Health Care Centers ( PHCC ) of the Municipality . All the areas of the Municipality were sampled simultaneously trying to maintain a uniform sampling effort along the study . Child population of aboriginal villages was not included in this study since they were involved in a specific program of aboriginal health . All samples were delivered to and processed at the laboratory of Parasitology and Zoonotic Diseases of the National Institute of Tropical Medicine ( PZD-INMeT ) of Puerto Iguazú . As the aim of this study was a general characterization of the occurrence and co-occurrence of parasites in the environment and in the children of Iguazú , samples were classified as positive if there was an egg or oo/cyst observed in one or more of any slide in any technique applied for its diagnosis . The analyses were conducted in duplicate by two experienced microscopists . As quality control , a random 10% of the total samples were re-examined by a senior laboratory researcher . Socio-environmental covariates were obtained from the analysis of local and landscape variables as well as socio-demographic data obtained from the last national census in 2010 and collected from questionnaires performed in the first visit to a household participating in the study ( see more details below ) .
The child population studied was 54 . 9% male , with a mean age ± SD of 5 . 8 ± 3 . 5 years old . Underweight affects 12 . 8% of the children , while the stunting involves 11 . 3% and wasting 12 . 7% of the sample . Overweight and obesity together affected 18 . 1% of the studied children . The mean age of the mothers was 30 . 8 ± 8 . 2 years old and 19 . 5% were single mothers . The employment situation of the families was precarious and most of the mothers ( 83 . 2% ) were homemakers . The 26 . 7% of the children has more than three siblings with the presence of overcrowding in 53% of the households . Sixty-three percent of the parents possess primary-level education only , and most of the children ( 88 . 6% ) reside in UBN households with no peri domiciliary hygiene ( 65 . 4% ) . Drinking water , excreta disposal and waste disposal were not safe in around 40% of the households ( 44 . 3 , 41 . 2 and 43 . 5% respectively ) . Most of the houses ( 75 . 8% ) were constructed with none or just one main component of cement , being land and wood other predominant materials . The 21 . 5% of the households possesses farm animals and almost 80% of the families have dogs ( 1 . 8 ± 1 . 8 dogs per family ) . Environmental contamination largely occurred in the most urbanized areas and increased where trash was present in the area . In the univariate analysis ( S6 Table ) , the presence and co-contamination of parasites ( i . e . the number of species found ) was higher in the areas in lower elevations , a higher density of streets , lower cover of trees , and higher surface temperature . At a local scale , both environmental contamination and co-contamination were positively associated with the presence of trash in the streets and negatively related to the presence of latrines in the area of sample collection ( S6 Table ) . Combining these results in a multivariate analysis ( S7 and S8 Tables ) suggested that the presence of trash ( local variable ) and the street density ( landscape variable ) were the most important determinants for the presence of parasite contamination ( Table 3 , models A1 –A3 ) . For co-contamination estimates , the presence of trash and the surface temperature ( landscape variable ) were the most important predictors ( Table 3 , models B1 –B3 ) . The model explaining the presence of contamination with landscape variables showed moderated to low prediction capabilities ( mean cvAUC = 0 . 667 , 95% CI 0 . 579–0 . 753 ) and therefore it was not used for predicting contamination in the study area . The selected co-contamination model with landscape variables ( Table 3 , B1 ) showed moderated but significant prediction capacity in cross validation ( mean cvSpearman Rank rho = 0 . 316 ± 0 . 02 , all p values <0 . 001 ) , and it did not show signals of spatial autocorrelation in its residuals ( Moran's Index: 0 . 009 , z-score: 0 . 302 , p = 0 . 763 ) . Therefore , this model was used for characterizing the contamination degree of the study area ( Fig 3 ) . The predicted co-contamination showed a heterogeneous spatial distribution along the Iguazú Municipality , but with higher values in the central areas of the city due to the association of elevated surface temperatures with the most urbanized areas , where higher co-contamination levels were also explicit in the predictions of the lower and upper limits of the 95% confidence interval ( S1 Fig ) . Parasite infection and co-infection of Iguazú children were determined by several factors that affect them at different levels ( Table 4 ) . At the individual level , the age was one of the most important factors in children between 5 and 9 years old showing higher probabilities of infection and co-infection . Playing with soil is a strong predictor of both infection and co-infection and previous anti-parasitic treatment was found as a significant risk factor for infection . Regarding nutritional status , there was no a clear association between childhood undernutrition ( stunting , wasting and underweight ) and parasites presence , although obese and overweight children showed lower probabilities of being infected . At a household level ( Table 4 ) , WASH and family variables were important to predict parasite infection and co-infection level , while the household characteristics ( e . g . UBN ) were significant only for predicting infection . Among the WASH determinants , the safe excretes disposal reduces the level of co-infection , but unexpectedly , the access to tap water increases the probability of infection . Family composition was also important for both response variables ( Table 4 ) . Families with single mothers have higher infection probabilities and larger families ( with more than 3 children ) showed higher co-infection intensities . Similarly , overcrowding was an important predictor of parasite infection , and mothers’ education showed an important role as well , where families with higher mother literacy had lower infection probabilities . The household UBN measured in our survey also showed an effect increasing infection probabilities at a household level . At a regional level ( PHCC level ) , the most important predictor was the co-contamination level in the neighborhood ( Fig 3 ) , evidencing higher co-infection of children which live in the more intensively contaminated areas ( Fig 4; Table 4 ) . The socioeconomic conditions described by the National Census at a regional level were not good predictors ( S9 , S10 and S11 Tables ) .
Our work represents the major survey of intestinal parasites in human and environmental samples developed in the region , providing useful benchmark information for prioritizing and enlightening targeting of interventions . One important finding of our work is the significance of considering multi-level determinants for understanding the maintenance and propagation of intestinal parasites in a sensitive population of Argentina . Our results show that environmental surveys could guide human surveys and interventions on a neighborhood level , but simultaneously , the attention of socio-economic conditions at the household level , and the individual child care are of great relevance . The capacity of combining environmental and human field surveys to identify key component acting in different levels enhances the potential of using the new understanding and tools to struggle these neglected tropical diseases .
|
Enteroparasites are among the main issues in public health arena , especially in children of vulnerable communities from developing countries . We performed a combined analysis of the factors that describe parasite prevalence in children and their environment . Our result evidenced a combined effect of socio-demographic and environmental factors acting at different scales ( individual , family , and neighborhood ) to determine the parasitic infections patterns and health risk of children at Iguazú Municipality in Argentina . Therefore , this work shed light on the multiple and multilevel factors involved in parasitic diseases , emphasizing the need for action in socio-health and environmental structural issues , not only from the decision-making authorities but also from the individuals , families and communities in favor of promoting healthy environments and healthy individuals .
|
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2017
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Environmental and socio-demographic individual, family and neighborhood factors associated with children intestinal parasitoses at Iguazú, in the subtropical northern border of Argentina
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The control of Mycobacterium tuberculosis ( Mtb ) infection is heavily dependent on the adaptive Th1 cellular immune response . Paradoxically , optimal priming of the Th1 response requires activation of priming dendritic cells with Th1 cytokine IFN-γ . At present , the innate cellular mechanisms required for the generation of an optimal Th1 T cell response remain poorly characterized . We hypothesized that innate Mtb-reactive T cells provide an early source of IFN-γ to fully activate Mtb-exposed dendritic cells . Here , we report the identification of a novel population of Mtb-reactive CD4− αβTCR+ innate thymocytes . These cells are present at high frequencies , respond to Mtb-infected cells by producing IFN-γ directly ex vivo , and display characteristics of effector memory T cells . This novel innate population of Mtb-reactive T cells will drive further investigation into the role of these cells in the containment of Mtb following infectious exposure . Furthermore , this is the first demonstration of a human innate pathogen-specific αβTCR+ T cell and is likely to inspire further investigation into innate T cells recognizing other important human pathogens .
Approximately one-third of the world's population is infected with Mycobacterium tuberculosis ( Mtb ) . Tuberculosis remains a leading cause of mortality worldwide and is responsible for 2–3 million deaths per year [1] . Household contact studies show that over 50% of exposed individuals never develop a positive tuberculin skin test ( TST ) , and are considered uninfected [2] . However , in those who have been detectably infected with Mtb , as assessed by a positive TST , the lifetime risk of tuberculosis is estimated at 5% to 10% [3] . In the remaining 90% of TST-positive individuals , protection is associated with the development of an effective adaptive immune response . Specifically , Th1-like cytokines , including IFN-γ , produced by CD4+ Th1 cells and CD8+ T cells , and TNF-α , produced by T cells and macrophages , are essential in the control of tuberculosis [4] . The priming of Mtb-specific naïve T cells requires that dendritic cells ( DC ) , either infected with Mtb or having engulfed Mtb-derived antigens , migrate to the lymph nodes [5 , 6] . There , to optimally prime pathogen-specific Th1 responses , DC require stimulation through Toll-like receptors ( TLRs ) [7] by the pathogen as well as host-derived factors such as type I and type II IFNs , cytokines , and chemokines [8] . Mtb-dependent TLR2 ligation can promote the maturation of DC via upregulation of costimulatory molecules and the production of IL-12 [9 , 10] . However , in comparison to potent LPS activation associated with TLR-4 ligation , Mtb induces relatively low levels of IL-12 production [11 , 12] . Nonetheless , IL-12 production by DC is essential to prime optimal Th1 responses [9 , 13–15] . IFN-γ , the prototypical Th1 cytokine , can directly augment the IL-12 production . In addition to NK cells , MHC Ib-restricted T cells are able to provide an early source of IFN-γ for enhanced IL-12 production by DC [9 , 14 , 15] . One example of these innate T cells comes from the study of the non-classical MHC-Ib murine molecule H2-M3 . Pamer and colleagues defined H2-M3 restricted , Listeria-specific , IFN-γ-producing T cells , whose response preceded that of the adaptive T cell response and was therefore consistent with an innate T cell population [16] . Subsequently , Urdahl et al . demonstrated the presence of MHC Ib-restricted cells in antigen-naïve mice and that found that they originated from the thymus and displayed properties associated with effector T cells [17] . In this study we sought to test the hypothesis that humans possess an innate population of Mtb-reactive T cells . From this hypothesis , we made the following predictions: 1 ) the innate T cell population would , unlike a naïve T cell precursor population , be present at high frequency in the circulating pool of lymphocytes; 2 ) the innate T cell population could respond directly ex-vivo to Mtb-infected cells in the absence of clonal expansion; 3 ) these cells would be prevalent throughout the human population irrespective of prior exposure to mycobacteria; and 4 ) these cells would be thymically derived . Here we show that Mtb-reactive thymocytes are present at frequencies ranging from 0 . 01% to 0 . 2% of CD4− thymocytes in humans . The majority of Mtb-reactive thymocytes express the αβTCR and an activated phenotype with cytolytic potential . Mtb-reactive thymocytes require cell contact with DC infected with live Mtb and respond through a mechanism most consistent with MHC class Ib–restricted T cells . Consistent with the hypothesis that humans contain innate T cells that are capable of responding to Mtb-infected DC , we have identified a population of Mtb-reactive cells in cord blood .
To assess whether or not humans contain a population of innate T cells that can respond to Mtb-infected cells we used thymocytes from infants undergoing cardiac surgery where thymectomy is standard procedure . Based on observations from Pamer and Urdahl , we speculated that such a population would be found in the CD4-negative population of thymocytes . Therefore , we used magnetic beads to deplete thymocytes of CD4+ cells . The CD4-depleted population was then incubated with autologous monocyte-derived Mtb-infected DC . We enumerated IFN-γ production from thymocytes by IFN-γ ELISPOT . Thymocytes from one donor produced IFN-γ in response to autologous DC infected with Mtb but not to uninfected DC while no IFN-γ was detected from Mtb-infected DC alone ( Figure 1A ) . We reasoned that these cells were not likely to be classically HLA-Ia restricted . HLA class Ib molecules , unlike the highly polymorphic HLA class Ia molecules , have limited polymorphism , such that most individuals will express a similar set of these molecules . As a result , we predicted that allogeneic DC would serve as antigen-presenting cells ( APC ) for these T cells . Allogeneic DC were tested for their ability to elicit IFN-γ production by thymocytes . CD4-depleted thymocytes incubated with allogeneic DC produced IFN-γ in response to Mtb-infected DC but not to uninfected DC from three separate allogeneic donors ( Figure 1B ) . Further evaluation of over 40 thymocyte donors using allogeneic DC revealed detectable IFN-γ responses to the Mtb-infected allogeneic DC but not to the uninfected DC ( data not shown ) . The finding that thymocytes are consistently unresponsive to uninfected allogeneic DC is in sharp contrast to the predictable response by peripheral T cells that exhibit strong alloreactivity in response to MHC mismatched DC . Fortuitously , the absence of alloreactivity provided us with the opportunity to employ allogeneic DC given the limited quantities of PBMC available from the thymocyte donors . We confirmed that in response to allogeneic Mtb-infected DC the Mtb-reactive thymocytes are present in the CD4-negative but not in the CD4+ fraction of thymocytes ( Figure 1C ) . CD8+ T cells from peripheral blood are distinct from Mtb-specific CD4+ T cells in that CD8+ T cells preferentially recognize APC in direct proportion to the degree of intracellular infection [18] . Similarly , Mtb-reactive thymocytes also preferentially recognize DC infected with Mtb at multiplicities of infection ( MOI ) of 30 or higher ( Figure S1 ) . Nevertheless , greater than 90% of DC infected at an MOI of 30 for 18 h of infection , remain viable , as assessed by trypan blue exclusion ( not shown ) and a minority ( less than 20% ) are apoptotic as assessed by Annexin V staining ( Figure S2 ) . We next sought to establish the frequency and prevalence of Mtb-reactive thymocytes . Ex vivo frequencies of Mtb-reactive thymocytes ( n = 18 ) ranged from 16 . 5 to 381 sfu/250 , 000 CD4− thymocytes ( mean = 115 . 9 ± SD 101 . 2 ) ( Figure 1D ) . Furthermore , we have detected Mtb-reactive thymocytes from 58 of 60 donors tested ( not shown ) demonstrating that Mtb-reactive thymocytes are prevalent in humans . In human peripheral blood , 50% to 90% of all γδ T cells are Mtb-reactive . These T cells primarily express the Vγ9Vδ2 TCR and can respond to the mycobacterial non-peptide antigen isopentenyl pyrophosphate ( IPP ) [19] . Furthermore , Kabelitz et al . previously described the presence of Vγ9-Mtb-reactive thymocytes in humans [20] . Thus , it was conceivable that γδTCR-expressing cells constituted the majority of Mtb-reactive thymocyte responses . Therefore , thymocytes were sorted by FACS by selecting CD4-negative cells that expressed CD8 and/or the γδTCR . The subsets were tested on Mtb-infected and uninfected DC . None of the sorted cells produced IFN-γ in response to uninfected DC ( not shown ) . Figure 2A shows that both γδTCR− and γδTCR+ thymocytes respond to Mtb-infected DC and that as expected , a high frequency of thymocytes that express the γδTCR can respond to Mtb-infected DC . However , both CD8 single positive ( SP ) and CD4−CD8− double negative ( DN ) thymocytes in the γδTCR-depleted subset also contained Mtb-reactive thymocytes . Therefore , we used the intracellular cytokine staining ( ICS ) assay with unfractionated thymocytes to determine if the γδTCR-negative cells expressed the αβTCR . Figure 2B shows that CD3+ IFN-γ+ Mtb-reactive thymocytes detected by ICS express the αβTCR and not the γδTCR . The fact that we did not detect γδTCR+ IFN-γ+ Mtb–reactive cells by ICS is consistent with the observation that less than 1% of all thymocytes express the γδTCR [21] . Furthermore , these results indicate that although Mtb-reactive T cells expressing either the γδ or αβ TCR are both present in the thymus , the vast majority of Mtb-reactive thymocytes are αβTCR+ CD4− T cells that are either CD8+ SP or DN . Mtb-reactive thymocytes , by virtue of producing IFN-γ directly ex vivo inherently display a Th1-type effector phenotype . To further characterize the phenotype of Mtb-reactive thymocytes we used the ICS assay to assess the expression of molecules associated with effector memory T cells . As a positive control we used PBMC from an adult donor with known T cell reactivity to mycobacterial antigens . PBMC or Mtb-reactive thymocytes were incubated with allogeneic DC infected with Mtb or left uninfected . As expected , uninfected allogeneic DC induced a detectable frequency of PBMC to produce IFN-γ while a much lower frequency was detected from thymocytes . Mtb-reactive cells were defined by the specific production of IFN-γ in response to Mtb-infected DC . All Mtb-reactive thymocytes expressed CD3 . This confirms the commitment of these cells to the T cell lineage ( Figure 3 ) [22] . Furthermore , no Mtb-reactive cells expressed CD161 ( data not shown ) , a marker expressed on both immature and mature NK cell cells in the thymus [23] further confirming that these cells are not NK cells . Consistent with results shown in Figure 2A , a proportion of the Mtb-reactive thymocytes expressed CD8 . The large majority of Mtb-reactive thymocytes expressed CD25 ( Figure 3A , right ) , and granzyme ( Figure 3B ) , as well as TNF-α ( not shown ) consistent with an effector memory phenotype with cytolytic potential [24] . Furthermore , a proportion of CD4− thymocytes secreted granzyme in response to Mtb-infected DC ( Figure 3C ) . Finally , Mtb-reactive thymocytes expressed higher levels of Bcl-2 ( Figure 3B ) suggesting these cells are likely to survive and egress to the periphery [25] . Next , we tested the hypothesis that TLR-activation of DC , or other stimuli , may be sufficient to stimulate thymocytes . Mtb principally acts through TLR2 [26–29] , TLR9 [29] , and to a lesser extent TLR4 [30] . Therefore , we tested IFN-γ production by thymocytes incubated with DC pre-treated with agonists to TLR2 ( γ-irradiated Mtb ) , TLR4 ( LPS ) , TLR9 ( CpG DNA ) , and TLR3 ( poly I:C ) . None of these TLR stimuli induced a comparable response to that elicited by live Mtb infection of DC ( Figure 4 ) . We confirmed that treatment of DC with the γ-irradiated Mtb and LPS indeed functioned as TLR agonists by inducing IL-10 production from the DC ( Figure S3 ) . Furthermore , an additional TLR2 agonist , lipoteichoic acid , did not induce IFN-γ production by thymocytes ( not shown ) . To test whether or not infection of DC may be triggering the expression of molecules associated with cellular stress , we incubated DC with IFN-γ , actinomycin D , or heat shock-treated the DC . CD4− thymocytes did not respond to the stress-induced DC . We confirmed that actinomycin D indeed induced apoptosis in over 55% of the DC as assessed by Annexin-V ( Figure S2 ) . To help elucidate the mechanism by which Mtb-reactive thymocytes respond to Mtb-infected DC , we asked if thymocytes require direct contact with Mtb-infected targets . The presence of a transwell between Mtb-infected DC and thymocytes prevented IFN-γ release ( Figure 5 ) , demonstrating that direct contact of thymocytes with Mtb-infected DC is required . In addition , cell supernatants from Mtb-infected DC did not induce IFN-γ production by thymocytes suggesting that cytokines produced from Mtb-infected DC are not sufficient for this response . To further evaluate the possibility that Mtb-reactive thymocytes respond to an antigen processed and presented through the HLA-I pathway versus to a cell surface ligand upregulated by live infection with Mtb , we used blocking antibodies to molecules induced by infection with Mtb . We were unable to block responses by Mtb-reactive thymocytes ( n = 5 ) using antibodies against MICA or ULBP1 [31] or the receptor NKG2D [32] ( not shown ) . We then blocked proteasomal function to prevent production of HLA class I–restricted Mtb epitopes . Addition of the proteasomal blocker , epoxomycin , blocked 88% of the response by Mtb-reactive thymocytes from donor Th30 and 57% of the response from donor Th42 ( Figure 6A ) . As expected , the CD8+ T cell clones H1–2 ( restricted by HLA-B1501 ) and 1–23 ( restricted by HLA-E ) were also blocked by epoxomycin ( 62% and 97% respectively ) while the CD4+ T cell clone E12 ( restricted by HLA-II ) was not blocked . Nevertheless , we have been unsuccessful in blocking the response by Mtb-reactive thymocytes using a variety of blocking antibodies to HLA-Ia and HLA-Ib molecules . Addition of the pan–HLA-I blocking antibody W6/32 did not inhibit responses by the thymocytes ( Figure 6B ) . In contrast , the HLA-B44– and HLA-E–restricted CD8 T cell clones were effectively blocked . Addition of blocking antibodies to the nonclassical CD1a , b , c , and d molecules also did not prevent responses by Mtb-reactive thymocytes ( data not shown ) . Thus , our data suggest that antigen processing is likely required for activation of at least a subset of thymocytes . As paraformaldehyde-fixed cells were used as the APCs in these experiments , an additional conclusion from these data , in combination with results from Figure 5 , is that a cell surface ligand and not a soluble factor is required to stimulate Mtb-reactive thymocytes . Thus , while ligand interaction is required , the cells are not restricted by HLA-Ia , HLA-E , or CD1 molecules . Physiologically , the relevance of Mtb-reactive thymocytes rests in their ability to egress the thymus , and serve as innate effectors . To address this , we used cord blood mononuclear cells ( CBMC ) isolated from healthy neonates , as a source of T cells that are naïve to exposure to mycobacterial antigens . The majority of CBMC samples , depleted of γδTCR-positive cells using magnetic beads , produced IFN-γ in response to Mtb-infected DC in an ELISPOT assay ( n = 8; range , 0–80 sfu/250 , 000 γδTCR-depleted CBMC; mean = 26 . 75 ± S . D . 33 . 75 ) ( Figure 7 ) .
In this study , we find that the human thymus contains cells that recognize Mtb-infected cells . As such , we postulate that these cells comprise an innate defense against mycobacterial infection . Several observations support this hypothesis . The frequencies of Mtb-reactive thymocytes are substantial ( 0 . 01% to 0 . 2% of CD4− thymocytes ) and appear similar to frequencies of other innate T cells that mediate immediate responses [33] . For example , MHC class Ib T22-restricted γδTCR+ T cells are present at frequencies as high as 0 . 5% of γδTCR+ splenocytes in uninfected mice [34] . In this regard , we note that corticosteroids are frequently administered to the thymus donors in the peri-operative period . As a result , we believe that our results likely underestimate the prevalence of these cells in the thymus . In contrast to the delayed responses inherent in the requisite proliferation , differentiation , and clonal expansion of adaptively acquired immunity , the thymocytes described herein exhibit rapid effector function characterized by the release of IFN-γ , TNF-α , and constituents of the cytolytic granule . Mtb-reactive thymocytes , by virtue of their ability to produce IFN-γ directly ex vivo , display the phenotype of pre-armed effector Th1-like cells [24] . The rapid production of IFN-γ by T cells is normally induced as a consequence of cell division and differentiation , and is associated with effector and memory , but not naïve T cells [35] . The phenotype of Mtb-reactive thymocytes is reminiscent of cells described by Urdahl et al . who showed that MHC class Ib–restricted T cells with an activated effector phenotype could be isolated from the thymus of naïve mice [16 , 17] . Therefore , Mtb-reactive thymocytes may represent a subset of innate T cells with direct ex vivo effector function in humans . Mtb-reactive thymocyte responses are present in the absence of prior antigenic exposure . In children , exposure to environmental mycobacteria occurs as children begin to explore their environment [36] . As most of our thymus donors are very young ( all are <4 mo old and many are less than 1 wk old ) it is unlikely they have been exposed to environmental mycobacteria . Furthermore , exposure to tuberculosis is very unlikely in our patient population . In Oregon , the 2006 case rate of tuberculosis was 2 . 2/100 , 000 individuals ( Oregon DHS ) . Moreover , in our limited experience , we have not found evidence for reactivity to Mtb-specific antigens such as CFP-10 and ESAT-6 ( not shown ) , arguing against the possibility of prior exposure to Mtb . We find it unlikely that Mtb-reactive T cells in the thymus reflect mature peripheral T cells that have recirculated back to the thymus . While mouse studies have demonstrated the capability of peripheral T cells to recirculate to the thymus [37 , 38] the injection of substantial numbers of T cells was required to detect this phenomenon [37 , 39] . Moreover , studies by Fink et al . showed that this required exposure to antigen [38] . As discussed above , it is unlikely that the very young donors described in this report have had prior mycobacterial exposure . The expression of CD3 , and the absence of CD161 , demonstrate it is unlikely that the Mtb-reactive thymocytes are NK cells [23] . Furthermore , Mtb-reactive thymocytes do not express Vα24 ( not shown ) and are therefore not the well-defined subset of invariant NKT cells [40] . However , it is possible that these innate cells are non-invariant TCR NKT cells or those restricted by an HLA-Ib molecule . These hypotheses are not mutually exclusive . Innate-like T cells often recognize a signature antigen in pathogen-infected cells . With regard to the possibility that these cells are NKT cells , it is possible that they recognize a danger signal induced by Mtb in the infected cell that would allow for NK-like recognition . In this regard , we note that blocking of the known NK receptor NKG2D and NK and γδ T cell ligands MICA [32] , and ULBP1 [31] , did not abrogate the recognition by Mtb-infected cells ( data not shown ) . Alternately , antigen presented in the context of an MHC class Ib-molecule may result in T cell activation . Our data support the hypothesis that Mtb-reactive thymocytes are MHC class Ib-restricted . Mtb-reactive thymocytes are activated by allogeneic Mtb-infected DC and activation requires live infection of DC with Mtb as well as proteasomal processing . As a result , we conclude that Mtb-reactive thymocytes represent a subset of T cells that are most likely MHC class Ib-restricted but may utilize a novel mechanism to detect Mtb-infected DC . This report provides the first demonstration of a human innate pathogen-reactive αβTCR+ T cell . In preliminary experiments we have begun to assess the reactivity of thymocytes to other pathogens ( not shown ) . We have detected modest responses to Staphylococcus aureus– , E . coli– , and Mycobacterium smegmatis–infected DC but did not detect any responses to Listeria monocytogenes– or vaccinia virus–infected DC . Thus , it is possible that innate thymocytes provide early and innate Th1-like immunity at the site of infection with Mtb and perhaps other pathogens . Furthermore , we have identified Mtb-reactive cells in cord blood . This finding is consistent with the potential egress from the thymus of αβTCR+ Mtb-reactive cells . Through the production of IFN-γ , Mtb-reactive cells may act on Mtb-infected macrophages early in infection and as such control the spread of Mtb . It is known that over half of exposed individuals never convert their TST [2] . Therefore , perhaps innate responses allow the clearance of the bacterium and obviate the need for adaptive immunity . Furthermore , IFN-γ from Mtb-reactive cells may provide help to DC to augment the production of IL-12 resulting in an enhanced Th1 response . As such , innate Mtb-reactive cells could act as a bridge between the innate and adaptive responses to Mycobacterium tuberculosis . These findings may inspire further investigation into innate T cells recognizing other important human pathogens .
All tissue and blood were obtained under protocols approved by the Institutional Review Board at Oregon Health and Science University . Human thymuses were obtained from children undergoing cardiac surgery at Doernbecher Children's Hospital . The majority of children were less than 1 mo of age and all were less than 4 mo old . However , due to the fact that thymuses were obtained as de-identified medical waste under an exempt IRB protocol no additional information is available on the status of the donors . PBMC were obtained by aphaeresis from normal adult donors with informed consent . Umbilical cord blood was obtained from healthy full-term neonates , collected into CPT tubes ( BD ) and CBMC were obtained after centrifugation . The H37Rv strain of Mycobacterium tuberculosis was used for all live Mtb infections and for experiments using γ-irradiated Mtb ( Mycobacteria Research Laboratories at Colorado State University ) . Thymocytes: Thymus tissue was cut into 3-mm3 pieces . Each piece was ground in a Medimixer with 1 ml of DMEM plus 10% FBS to form a single cell suspension . The suspension was cryopreserved at 2 × 108 cells/ml in a 90% FBS/10% DMSO freezing solution with a post-thaw viability of approximately 50% . To deplete CD4+ thymocytes we positively selected CD4+ cells using magnetic bead separation according to the manufacturer's instructions ( Miltenyi ) and used the remaining untouched cells that contain CD8+ ( SP ) and CD8−CD4− ( DN ) cells . The CD4+ selection procedure resulted in a population of cells with a mean purity of 80% CD4-negative cells ( range , 60% to 95% CD4-negative cells; not shown ) . Monocyte-derived DC: Monocyte-derived DCs were prepared according to the method by Romani et al . [41] . Briefly , PBMC or CBMC were resuspended in 2% human serum ( HS ) medium and allowed to adhere to a T-75 ( Costar ) flask at 37°C for 1 h . After gentle rocking , nonadherent cells were removed and 10% HS medium containing 10 ng/ml of IL-4 ( Immunex ) and 30 ng/ml of GM-CSF ( Immunex ) was added to the adherent cells . After 5 d , cells were harvested with cell-dissociation medium ( Sigma-Aldrich ) and used as APC in assays . IFN-γ ELISPOT assay: All IFN-γ ELISPOT assays were performed as previously described [42] . Thymocytes were incubated for 24 h with DC that were previously infected overnight with Mtb H37Rv . A range of multiplicity of infection of 25 to 50 was used throughout our studies . Estimation of the frequency of Mtb-reactive thymocytes using the IFN-γ ELISPOT: Thymocytes were added to ELISPOT plates in duplicate over a range of concentrations ( 5 × 105 , 2 . 5 × 105 , 1 . 25 × 105 , 6 . 25 × 104 cells/well ) with DC ( 50 , 000 cells/well ) infected with Mtb or left uninfected . For determination of effector cell frequencies , the general estimating equation in the GraphPad Prism 3 . 0 software package was used . If the control frequencies were determined to be significantly different ( p < 0 . 05 ) from the treatment group , control values were subtracted out to determine the frequencies of Mtb-reactive thymocytes . Intracellular cytokine staining assay: Thymocytes ( 500 , 000/well ) were added to DC ( 50 , 000/well ) that were either Mtb-infected or uninfected and incubated for 48 h in the presence of anti-CD28 ( 1 μg/ml ) and CD49d ( 1 μg/ml ) . GolgiStop ( BD Pharmingen ) was added for the final 6 h of the assay . Cells were fixed with paraformaldehyde ( final 1% ) , permeabilized with Perm/Wash ( BD Pharmingen ) , and stained with fluorochrome-conjugated antibodies to both IFN-γ and cell surface receptors . Acquisition was performed with an LSRII flow cytometer with FACS Diva software . All analyses were performed using FlowJo software ( TreeStar ) . IL-10 ELISPOT assay: The Human IL-10 ELISpot PLUS kit ( ALP ) was used to detect IL-10 and performed according to the manufacturer's instructions ( Mabtech ) . Granzyme ELISPOT assay: The BD ELISpot human granzyme B kit was used to detect human granzyme and performed according to the manufacturer's instructions ( BD Biosciences Pharmingen , San Diego , CA ) . Reagents . TLR agonists: γ-irradiated Mtb ( moi equivalent 500; Mycobacteria Research Laboratories at Colorado State University ) ; Lipoteichoic Acid ( 10 μg/ml; Sigma ) ; LPS ( 100 ng/ml; Sigma ) ; CpG DNA ( 6 μg/ml; Coley Pharmaceuticals ) ; poly I:C ( 50 μg/ml; Sigma ) . IFN-γ ( Sigma ) was used at 10ng/ml . Actinomycin D was used at 10 μM ( Sigma ) . The pan HLA antibody W6/32 ( Serotec ) and the mouse IgG2a isotype control ( Biolegend ) were used at 2 μg/ml . Annexin V-APC ( BD Biosciences ) was used to evaluate apoptosis using flow cytometry according to the manufacturer's instructions .
|
Mycobacterium tuberculosis ( Mtb ) infects about one-third of the world's population . Most people who are exposed remain healthy , but control of this intracellular bacterium requires a robust cellular immune response . Production of the pro-inflammatory cytokine IFN-γ from cells in the adaptive immune response is critically important in the immune control of Mtb . However , this cytokine is also essential in initiating an optimal adaptive immune response . We hypothesized that innate cells could provide an early source of IFN-γ to aid in generation of an optimal adaptive immune response . We looked for IFN-γ producing cells in human neonates that were unlikely to have been previously exposed to either Mtb or other environmental mycobacteria . Here , we report the identification of a novel T cell population from the thymus that produces IFN-γ in response to Mtb-infected cells . Mtb-reactive thymocytes are present at high frequencies , are present in nearly all newborns tested , and display characteristics of T cells normally associated with a memory response . This novel innate population of Mtb-reactive cells will drive further investigation into the role of these cells in the containment of Mtb following infectious exposure and is likely to inspire further investigation into innate T cells recognizing other important human pathogens .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"infectious",
"diseases",
"immunology",
"homo",
"(human)",
"eubacteria"
] |
2008
|
Human Innate Mycobacterium tuberculosis–Reactive αβTCR+ Thymocytes
|
To establish correlates of human immunity to the live plague vaccine ( LPV ) , we analyzed parameters of cellular and antibody response to the plasminogen activator Pla of Y . pestis . This outer membrane protease is an essential virulence factor that is steadily expressed by Y . pestis . PBMCs and sera were obtained from a cohort of naïve ( n = 17 ) and LPV-vaccinated ( n = 34 ) donors . Anti-Pla antibodies of different classes and IgG subclasses were determined by ELISA and immunoblotting . The analysis of antibody response was complicated with a strong reactivity of Pla with normal human sera . The linear Pla B-cell epitopes were mapped using a library of 15-mer overlapping peptides . Twelve peptides that reacted specifically with sera of vaccinated donors were found together with a major cross-reacting peptide IPNISPDSFTVAAST located at the N-terminus . PBMCs were stimulated with recombinant Pla followed by proliferative analysis and cytokine profiling . The T-cell recall response was pronounced in vaccinees less than a year post-immunization , and became Th17-polarized over time after many rounds of vaccination . The Pla protein can serve as a biomarker of successful vaccination with LPV . The diagnostic use of Pla will require elimination of cross-reactive parts of the antigen .
Plague is known as a primary natural zoonosis but is an extremely deadly infection for humans . The disease is caused by Yersinia pestis , a gram-negative bacterium , which upon entry in the body of mammalian host is capable of establishing three major forms of plague: bubonic , septicemic , and pneumonic [1 , 2] . The plasminogen activator ( Pla ) of Y . pestis is an outer membrane protease involved in dissemination of Y . pestis into circulation , and is one of the major virulence determinants of this pathogen [3–5] . The Pla protein is the surface-exposed trans-membrane β-barrel protease of the Omptin family with homologs found among many bacteria across family Enterobacteriacea [6] . Nevertheless , only Pla can convert plasminogen to plasmin by limited proteolysis , and this activity was likely crucial for the increased lethality of Y . pestis that developed during the course of evolution [7–9] . Detectable levels of relevant antibodies to Pla ( anti-Pla Abs ) have been measured in the convalescent sera of human patients who survived plague infection , as well as in mice that survived experimental plague infection [10 , 11] . Moreover , anti-Pla Abs of IgG class were detected in the sera of animals and humans vaccinated with live plague vaccine ( LPV ) indicating immunogenicity of this outer membrane protein [12] . Immunization with purified recombinant Pla or its use in a DNA vaccine formulation provided no protection against plague in a murine model [13] . Nevertheless , partial protection was seen in mice and rats against strain of Y . pestis lacking capsular antigen F1 [14] . Besides the testing of Pla as a potential protective antigen for plague subunit vaccine formulation , there were attempts to use this outer membrane protein for immuno-diagnostic purposes . A panel of monoclonal antibodies ( MAbs ) to Pla was created to different epitopes that were either species-specific for Y . pestis or able to recognize other bacteria [15] . Similar studies resulted in selection of anti-Pla MAbs capable of detecting natural Y . pestis isolates , as well as modified strains of plague microbe like capsule-negative variants [16 , 17] . The live plague vaccine created almost a century ago is still widely used in the former Soviet Union and China to immunize plague researchers and people at risk living in plague endemic territories [12 , 18] . The advantage of the LPV over a killed plague vaccine is its ability to defend against all forms of plague , as well its ability to mimic to the plague infectious process to a certain extent , resulting in a robust protection [19] . However , this vaccine is not approved for human use in the Western countries due to the safety concerns [20] . Nevertheless , construction of rationally attenuated vaccine strains of Y . pestis has garnered attention in recent years [21] , especially because the LPVs can induce both humoral and cellular immunity against plague [22–24] . Therefore , a detailed study of human immunity elicited by LPV is beneficial for both understanding the mechanism underlying the immune response to this vaccine and for future evaluation of efficacy of the next generation of plague vaccines . In this study , we investigated antibody and cell-mediated immunity in individuals vaccinated with the live plague vaccine line EV NIIEG , which is a derivative of the well-known vaccine strain Y . pestis EV76 [12] . Here , the Pla protein was used as a model antigen , which we intended to utilize in the future as a tool for evaluation of vaccine efficacy of vaccination and as a marker of exposure to plague .
Each human volunteer provided written informed consent for blood donation . The patients in this manuscript have given written informed consent ( as outlined in the PLOS consent form ) to publication of their case details . This study was approved by the Human Bioethics Committee of the Saratov Scientific and Research Veterinary Institute . The Institutional Review Board ( IRB ) was registered with the Office for Human Research Protections ( OHRP ) , registration number IRB00008288 ( https://ohrp . cit . nih . gov/search/irbsearch . aspx ? styp=bsc ) . Sera from healthy 26–72 years old volunteers ( n = 34 , group A ) of both genders who received multiple annual immunizations ( 2–51 injections ) with the live plague vaccine line EV NIIEG ( LPV ) , as well as from healthy individuals ( n = 17 , group B ) who had no history of contact with either Y . pestis microbe or its antigens , were tested . We further divided group A of immunized donors into subgroups of recently vaccinated ( A-RV , less than one year post-vaccination , n = 13 ) and early vaccinated ( A-EV , more than one year post-vaccination , n = 21 ) . The vaccination was performed by intradermal immunization ( scarification ) , which is a standard way to immunize people with LPV in Russia [12] . This immunization was done to plague researchers in their respectful institutions , and was not performed by us . The sera were aliquoted and stored at -80°C . Peripheral blood mononuclear cells ( PBMCs ) were isolated from heparinized blood by density gradient centrifugation in Histopaque ( Sigma , St . Louis , MO ) according to standard protocol . Cells were cultured in DMEM/F12 medium containing 10% FBS and antibiotic-antimycotic supplement for six days with or without stimulatory agent in 96 well plates ( 105 cells per well ) . The Hig-Tag-labeled Y . pestis recombinant proteins were purified as described previously for the panel of five antigens [25] . The quality of purification was evaluated with the silver stained PAGE . Soluble antigens , such as F1 , were treated with AffiPrep Polymyxin resin ( BioRad , Herciles , CA ) to remove the traces of LPS , while partially soluble Pla was isolated in two steps . First , we isolated Pla-containing inclusion bodies , and then purified Pla using Ni2+-chromatography under denaturing conditions . The level of contaminating LPS was measured with QCL-1000 Chromogenic LAL Assay kit ( Fisher Scientific ) . Both antigens were essentially LPS-free , as the LPS contamination was below the sensitivity level of the kit ( 0 . 1–1 . 0 EU/ml ) . Unstimulated PBMCs served as negative controls , and Concanavalin A from Canavalia ensiformis Type IV-S ( ConA ) ( Sigma ) was used as a positive control . The proliferative response was measured in quadruplicate by detection of BrdU incorporation using Cell Proliferation ELISA , BrdU chemiluminescent kit ( Roche Applied Science , Indianapolis , IN ) according to manufacturer’s protocol . The chemiluminescence was measured by using a BioTek Synergy HT reader ( BioTek Instruments Inc . , Winooski , VT ) . The proliferative response was expressed as a stimulation index ( SI ) calculated by dividing the mean relative light units per second ( rlu/s ) obtained for the cultured cells with a stimulant by the rlu/s of non-stimulated wells . Culture supernatants were collected on day 5 and preserved at -80°C until further use . The levels of IFN-γ , TNF-α , IL-4 , IL-10 , and IL-17A were measured by using commercial ELISA kits ( Vector-Best , Cytokine , Russia ) according to the manufacturer’s instructions . The reaction was developed using streptavidin-horseradish peroxidase with the tetra-methyl benzidine chromogen ( TMB ) , and the optical density was measured at 450 nm . Immulon 2 HB plates ( Thermo Scientific , USA ) were coated overnight at 4°C with recombinant Pla at concentration 5 μg/ml dissolved in 0 . 1 M carbonate buffer , pH 9 . 5 with 8 M urea . The remaining binding sites were blocked with 20% Newborn Calf Serum ( Sigma ) in Phosphate Buffered Saline ( PBS ) . Each serum sample was two-fold serially diluted in the range of 1:50 to 1:800 . Goat anti-human IgG ( Fab-specific ) -peroxidase ( HRP ) antibody ( Sigma ) was used as secondary antibody . The reaction was developed with the TMB substrate ( Sigma ) . The bacterial suspension of LPV was used as a control coating antigen in ELISA . The titers were calculated as the last dilution giving values above the cut-off level that was the mean value of the blank wells ( sera without antigen ) . Human antibody isotyping was performed by immunoblotting technique using relevant commercial murine monoclonal subtyping IgG subclass antibodies ( IgG1 , IgG2 , IgG3 , and IgG4 ) , as well as anti-human IgA , IgM , and IgE class specific antibodies ( Rosmedbio Ltd . , St . -Petersburg , Russia ) . The recombinant Pla antigen was separated by 12 . 5% SDS-PAGE , transferred to a nitrocellulose membrane , incubated with serially diluted human sera , and then probed with corresponding anti-human MAbs . Goat anti-mouse IgG ( Fab-specific ) -HRP Ab ( Sigma ) was used as secondary antibody . The substrate was TMB for the membranes ( Sigma ) . The endpoints were determined visually with the signal considered positive when the intensity was twice over the background . B-cell immune-reactive epitope mapping of the target antigen was performed in ELISA by using a library of 61 peptides generated from the sequence for Pla of Y . pestis CO92 ( accession no . CAB53170 . 1 ) and consisting of 15-mer peptides overlapping by 10 amino acids ( S1 Table ) . Nunc Immobilizer , Amino Modules Plates ( Thermo Scientific ) were coated with 20 μg of individual peptides in 0 . 1 M carbonate buffer , pH 9 . 5 , overnight . ELISA was then performed as described above . The dilution of tested sera was 1:100 . The interpretation of data was performed as described in a previous study with a similar design [26 , 27] . Briefly , optical density ( OD ) values were read with a BioTek Synergy HT reader at a wavelength of 450 nm ( reference wavelength , 630 nm ) . A signal was assigned as positive when it reached the cutoff value of twice the background OD . The background OD was the mean of the lowest 50% of all OD values obtained with that particular serum . The wells containing no peptides were used as negative controls , and recombinant Pla was used as a positive control . GraphPad Prism 6 software was used for data handling , analysis , and graphic representation . Non-parametric tests , i . e . the Mann–Whitney test for continuous unpaired data and the Chi-square test or the Fisher’s exact test for dichotomous variables , were performed for statistical analysis . Associations were assessed using Spearman’s Rank Correlation coefficient . A P value <0 . 05 was considered statistically significant .
To assess the in vitro proliferative response , PBMCs isolated from study subjects were stimulated with 5 μg/ml Pla or 2 μg/ml F1 ( control antigen ) . The SI induced by Pla was noticeably higher than that obtained in response to the control F1 antigen in both relevant vaccinated groups , such as group A-RV and group A-EV ( p<0 . 05 , p<0 . 0001 , respectively ) , as well as in the group B of unvaccinated individuals ( p<0 . 01 ) ( Fig 1A ) . Although the proliferative response to Pla was pronounced , there was no significant difference between both A-RV and A-EV groups of vaccinees and control donors in the group B . Nevertheless , a moderate trend of slightly higher stimulation indexes was observed in the cohort of recently vaccinated individuals ( A-RV group ) compared with the donors in the A-EV group with the last vaccination occurring more than one year ago ( p = 0 . 117 ) . The in vitro proliferative response to Pla was accompanied by a marked but nonspecific ( p>0 . 05 ) release of a number of cytokines , such as IFN-γ , TNF-α , and IL-10 by PBMCs derived from donors of both vaccinated ( A-RV and A-EV ) and unvaccinated groups ( Fig 1 ) . Surprisingly , we found that production of IL-4 was significantly greater in group B than in vaccinated donors . Although there was no significant difference between stimulated and control PBMCs , the level of IL-4 was reduced by 3 . 4-fold in group A ( ARV and A-EV ) of vaccinated donors in comparison with group B of naïve donors ( Fig 1B ) . In contrast , PBMCs obtained from donors of the A-EV group , who received multiple immunizations in the past , responded to stimulation with Pla by 14 . 7-fold increase ( p<0 . 05 ) in making IL-17A over the naïve donors of the group B ( Fig 1A , S1 Fig ) . This remarkable contribution of IL-17A production from the donors of the A-EV group resulted in the overall significant difference between groups A and B vaccinated and naïve donors ( p = 0 . 043 ) , while there was no statistical significance for the group A-RV in this category ( p>0 . 05 ) . The observed IL-17A release may indicate that the immune response to LPV becomes Th17-polarized over time multiple rounds of vaccination . There was a significant modest negative correlation between number of immunizations ( r = -0 . 475 , p<0 . 05 ) and the IL-4 response in vaccinees , although corresponding correlation with post-vaccination time was negligible ( r = -0 . 196 , p>0 . 05 ) ( S2 Fig and S3 Fig ) . Also , there was a slight positive correlation in the levels of IFN-γ ( r = 0 . 018 , p = 0 . 943 ) , IL-17A ( r = 0 . 018 , p = 0 . 943 ) and TNF-α ( r = 0 . 229 , p = 0 . 361 ) , and negative correlation of IL-10 ( r = -0 . 297 , p = 0 . 231 ) , with the number of LPV injections . The increase in the level of IFN-γ ( r = 0 . 079 , p = 0 . 756 ) , IL-17A ( r = 0 . 147 , p = 0 . 561 ) , and IL-10 ( r = 0 . 116 , p = 0 . 646 ) but not TNF-α ( r = -0 . 126 , p = 0 . 620 ) may potentially correlate with the post-vaccination time ( S3 Fig ) , although all latter cases were not statistically significant ( p>0 . 05 ) . Overall , we found significant association for the IL-4 cytokine , whose levels decreased in donors after an increasing number of vaccinations . The serological immune response to Pla elicited by the LPV was investigated in the vaccinated donors of group A in comparison with the naïve donors of group B by ELISA . We detected IgG class Abs to Pla , with titers ranging from 1:50 to 1:400 , in approximately half of the group A individuals . Moreover , all recently vaccinated donors in the A-RV subgroup were found to be anti-Pla positive . To our surprise , there was a significant difference in both the titers and percent of positive individuals between the subgroups A-RV and A-EV . Donors that received multiple repetitive immunizations ( A-EV group ) displayed a suppression of the antibody response to Pla . This observation correlates with the negative association between the level of IL-4 and number of LPV immunizations . On the other hand , 100% of the sera collected from donors in the naïve control group B reacted with the Pla antigen and exhibited titers similar to those found in the vaccinated donors of group A-RV , indicating Pla cross-reactivity ( Fig 2 ) . We next determined the reactivity of the sera for anti-Pla antibody classes and IgG subclasses ( Fig 3 ) . Based on the ELISA results , we separated the group A donors into responders ( A-Res ) and non-responders ( A-Non ) . All responders of group A and the majority of positive donors of group B demonstrated immunoreactivity with the IgG1 subclass of immunoglobulins . Only a single individual in each A and B groups showed the reaction with IgG2 subclass ( Fig 3A ) . This one donor from the group A-Res was positive for both IgG1 and IgG2 types . Among donors of the A-Non subgroup the negative reaction was observed for anti-Pla Abs of IgG1 ( p<0 . 05 ) , IgG2 and IgG4 subclasses ( p>0 . 05 ) . Moreover , all vaccinees ( group A ) possessed anti-Pla Abs for IgG3 while naïve donors of the group B did not have Pla-specific Abs of this subclass ( p<0 . 01 ) . In contrast , anti-Pla Abs of the IgG4 subclass was found exclusively in the sera of the group B donors . In addition to Pla-specific IgG , we also detected anti-Pla Abs of the IgA class in the sera of vaccinees but not in naïve donors . This corresponded with the increased level of IL-17A released by PBMCs from the group A donors ( see Discussion ) . Also , we observed the presence of anti-Pla Abs of the IgM subclass in both A and B groups of donors ( Fig 3B ) . Finally , IgE class antibodies to Pla antigen were found in sera of about one third of the vaccinated donors , both A-Res and A-Non , and only in a single unvaccinated individual . This result may be indicative of the putative allergenic potential of this antigen and the LPV vaccine in general . A library of 61 overlapping peptides , each 15 amino acid residues in length ( offset by 5 residues at a time ) deduced from the entire Pla sequence was probed with 12 sera samples that exhibited the highest anti-Pla IgG titers determined by ELISA . This set included sera from eight and four donors of the vaccinated A and naïve control B groups , respectively . The results of the screening for each serum are shown in the S2 Table . We found that most of the reactive peptides interacted with Abs from both groups of donors . Nevertheless , the peptides 9 , 11 , 18 , 30 , 34 , 36 , 49 , 52 , 54 , 56 , 58 , and 60 were specific for the donors of the A group , while peptides 19 , 33 , 35 , 50 , and 61 belonged exclusively to the group B donors . The frequency of appearance of each peptide for donor groups A and B is illustrated on Fig 4 . Among the group A specific peptides , none reacted with Abs of all eight donors tested indicating the absence of the wide-range immunodominant linear epitope . Only peptide 52 reacted with 50% sera of vaccinated donors , while most of peptides reacted with one or two sera . Therefore , the formation of the antibody response to the Pla antigen with respect to the linear B epitopes might be donor-specific . The B group specific peptides appeared just once in each corresponding serum . Two peptides , number 6 and 24 , were of particular interest , because of their strong cross-reactivity with sera from both vaccinated and naïve donors . Peptide 6 showed the remarkable ability to interact with Abs from any donor of both groups , while peptide 24 reacted 100% with sera from naïve and 50% with sera from vaccinated donors ( Fig 4 ) . The existence of two broadly-reactive Pla epitopes may explain the ELISA results shown on Fig 2 in which sera of all naïve donors reacted with the entire Pla antigen immobilized in the wells of the microtiter plate .
In the current study , we investigated for the first time the T-cell recall response to the Pla antigen in human donors vaccinated with LPV . Our data indicate that the proliferative response of human PBMCs to Pla stimulus was strong in nature and even exceeded that induced by capsular F1 antigen , which is known for its pronounced immunogenic characteristics [18 , 19] . However , we did not detect a statistical difference in this respect between vaccinated and naïve control donors , suggesting a nonspecific reaction likely due to the presence of cross-reacting T-cell epitope ( s ) within the Pla antigen . Nevertheless , there was a moderate trend ( p = 0 . 117 ) in observing a slightly high stimulation index in recently vaccinated individuals ( less than one year post-immunization ) . Therefore , we speculate that the specific T-cell response to Pla did occur in this group of vaccinees; however , it was masked by the pronounced cross-reactivity . Also , we report the presence of Pla cross-reactive linear B-cell epitopes that resulted in a strong reaction with this antigen of sera from naïve donors in ELISA . This was not totally surprising to us , since we saw an indication of this antibody cross-reactivity in our previous studies after probing Pla antigen with the panel of monoclonal antibodies [15] , and also observed the Pla-reactive band on immunoblot with naïve human sera [25] . Interestingly , multiple vaccinations with LPV suppressed the antibody titers to Pla that were observed when recently ( A-RV ) and early ( A-EV ) groups of vaccinated donors were compared ( Fig 2 ) . This suppression of the antibody response to the Pla antigen is likely due to the development of a dominant immune response to other competing and more potent antigen ( s ) of the live Y . pestis vaccine that became enhanced overtime . If true , this may mean that multiple booster immunizations with LPV may select for the response to a few dominant antigens . These antigens may not even be protective while presenting a threat of developing an allergic reaction instead ( see IgE response in Fig 3B ) . The Pla protein is considered to be a good candidate for Y . pestis specific diagnostic antigen [15–17] that is expressed well at both ambient and mammalian host temperatures [28] . However , the observed Pla cross-reactivity may result in certain limitations on its use for diagnostic purposes . Therefore , we mapped the cross-reactive regions of Pla using a library of 15-mer overlapping peptides . Comparison of peptide-ELISA results with sera from eight vaccinated and four naïve donors revealed two major cross-reactive peptides , peptides 6 ( IPNISPDSFTVAAST ) and 24 ( TDHSSHPATNVNHAN ) . Peptide 6 showed a particularly strong reaction for all sera tested and far exceeded the signal from any other reactive peptide in the library . There were other reacting peptides common for vaccinated and naïve donors; however , they were random and reactive with only one or two sera per group . Importantly , we found 12 peptides that specifically reacted with sera of vaccinated individuals and did not react with sera from naïve donors . Among them , only peptide 52 ( TPNAKVFAEFTYSKY ) reacted with 50% of sera from vaccinees suggesting that this region can potentially contain an immunodominant linear B-cell epitope recognized by the immune system of humans with different genetic backgrounds . This region may represent a good candidate to test for the purpose of creating a novel plague peptide vaccine . We determined the distribution of Pla-reacting immunoglobulins within the IgM , IgA , and IgE classes , as well as IgG subclasses ( IgG1 , IgG2 , IgG3 , and IgG4 ) in the sera of vaccinated and naïve donors ( Fig 3 ) . The anti-Pla Abs of the IgM class were found in all donors tested . Generally , natural human IgM antibodies or autoantibodies play a role in maintaining the physiological homeostasis and preventing a wide range of different infections [29] . The presence of anti-Pla Abs of IgM class in naïve donors and those who received LPV immunization many years ago suggests that they derived from a constant stimulation of the immune system with cross-reacting antigens rather than from the LPV vaccination . The suspected candidates for these stimulants could be Pla-homologous proteins of the Omptin group found in many Enterobacteriaceae [6] . In contrast , vaccinated , but not naïve , donors contained anti-Pla Abs of IgA class ( p<0 . 05 ) suggesting their origination from LPV immunization by dermal scarification . The existence of Pla-specific IgA correlated with our observation of marked production of IL-17A found after stimulation of PBMCs of vaccinated donors with the Pla antigen ( Fig 1A ) , which was absent in the naïve group of donors . It was shown previously that vaccine-specific Th17 cells formed by parenteral immunization were involved in eliciting a long-term detectible level of secreted IgA [30] . Moreover , subcutaneous priming with recombinant antigen in a Th17-inducing adjuvant followed by boosting promoted high and sustained levels of IgA in the lungs . This response was proven to be associated with germinal center formation in the lung-draining lymph nodes [31] . This may comprehensively explain the high efficiency of LPV against both bubonic and pneumonic plague [12 , 18 , 24] . Overall , these immune response characteristics to Pla antigen suggest that Th17 polarization of the immunity to LPV can be beneficial to the host during infection [32] . The release of IL-17A in response to stimulation of PBMCs of immunized individuals could also serve as an indicative marker of successful vaccination with LPV . Nevertheless , we would like to speculate that the presence of Pla-specific antibodies of the IgE subclass in vaccinated donors only ( Fig 3B ) may highlight the danger of a vaccine-related trigger of an allergic response and autoimmune disease . Further studies are needed to shed light on this important issue . It was reported previously that human immunization with killed plague vaccine induced long-lasting and mixed Th1/Th2 responses that were more polarized towards Th1 [33] . In our study , slightly elevated production of IFN-γ and diminished IL-4 in response to stimulation with Pla in the group of recently vaccinated donors ( Fig 1B ) also points to a Th1-biased immune response after administration of the live vaccine . This observation is supported by detection of anti-Pla Abs of IgG1 and IgG3 , and absence of IgG4 subclasses in the sera of these donors [34–37] . In summary , we found that despite complications with cross-reactivity , human immunity elicited by LPV could be assessed based on analysis of the immune response to Pla antigen . Our analysis showed that LPV vaccination resulted in the response being skewed towards Th1 and Th17 , while production of IL-17A by PBMCs of immunized donors in response to Pla antigen stimulation could be a good indicator of the induced immunity . Additionally , we mapped cross-reacting linear B-epitope candidates within the Pla antigen that should be helpful in developing Pla-based diagnostics for Y . pestis .
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Yersinia pestis , the causative agent of plague , has been recognized as one of the most devastating pathogen experienced by mankind . It remains endemic in many parts of the world , and is considered emerging pathogen . A live attenuated Y . pestis strain EV line NIIEG has been used for decades in the former Soviet Union for human vaccination and has proven effective against all forms of plague . We began characterizing the Y . pestis-specific antibody and T cell-mediated immune responses in people immunized with live plague vaccine . The long term goal of our research is to understand the protective mechanisms underlying immunity to plague in humans and to discover novel protective antigens for their incorporation into a subunit vaccine . Here , we describe our study on immune responses in vaccinees to one of the essential virulence factors of Y . pestis , namely Pla antigen . The results of the study shed light on the development of the optimal markers to assess the correlation with vaccine-induced protection .
|
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2018
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Humoral and cellular immune responses to Yersinia pestis Pla antigen in humans immunized with live plague vaccine
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Recent studies have established that the highly condensed and transcriptionally silent heterochromatic domains in budding yeast are virtually dynamic structures . The underlying mechanisms for heterochromatin dynamics , however , remain obscure . In this study , we show that histones are dynamically acetylated on H4K12 at telomeric heterochromatin , and this acetylation regulates several of the dynamic telomere properties . Using a de novo heterochromatin formation assay , we surprisingly found that acetylated H4K12 survived the formation of telomeric heterochromatin . Consistently , the histone acetyltransferase complex NuA4 bound to silenced telomeric regions and acetylated H4K12 . H4K12 acetylation prevented the over-accumulation of Sir proteins at telomeric heterochromatin and elimination of this acetylation caused defects in multiple telomere-related processes , including transcription , telomere replication , and recombination . Together , these data shed light on a potential histone acetylation mark within telomeric heterochromatin that contributes to telomere plasticity .
Saccharomyces cerevisiae telomeres and mating type loci ( HMR and HML ) are well-characterized transcriptionally silenced domains . Heterochromatin tends to initiate at a specific DNA element and propagates along a chromatin fiber to repress the expression of nearby genes [1] . The major structural components of heterochromatin in yeast are known as Sir ( silent information regulators ) proteins , including Sir2 , Sir3 and Sir4 [1] . For telomeric heterochromatin , the double-stranded telomeric DNA-binding protein Rap1 interacts with Sir4 and thereby recruits the whole Sir complex to the telomeres [2] . Once recruited to telomeres , Sir2 deacetylates a critical K16 acetyl mark on histone H4 [3] , a process required for Sir proteins to bind throughout the subtelomeric regions of ∼3 kb proximal to the terminal ∼350-bp TG-tracts [4]–[8] . The spreading of heterochromatin must be restricted by boundaries between silent and active chromatin [9] . Since histone H4 amino acid residues 16 to 29 are primarily required for Sir3 binding [4] , histone H4K16 acetylation dominantly prevents Sir-mediated heterochromatin spreading [6] , [7] . Genetic and biochemical data also show that unacetylated H4 K5 , 8 , 12 together can substitute for the mutation of H4K16 in Sir3 binding [4] , [5] . Therefore , it has been generally believed that the acetylation of histone H4 at K5 , 8 and 12 , catalyzed by NuA4 complex [6] , [10] , cumulatively antagonizes heterochromatin spreading . However , it remains unclear whether H4 K5 , 8 and 12 contribute equally to this process . Yeast heterochromatin possesses the ability to exchange chromatin-bound Sir3 for soluble unbound protein throughout the cell cycle [11] . In addition , heterochromatin is surprisingly permissive to activators , co-activators and transcriptional preinitiation-complex ( PIC ) as well [12]–[14] . Moreover , heterochromatin doesn't prohibit active base-pair substitutions [15] . These lines of evidence have revealed the dynamic aspect of silent- and hetero-chromatin . One outstanding question of concern is what contributes to the dynamics of heterochromatin . Accordingly , it would be beneficial to know if altering the dynamics of heterochromatin structure has biological consequences . In this study , we show that the heterochromatin in yeast harbors a dynamic H4K12 acetylation mark which suppresses Sir-mediated aberrant condensation of telomeric heterochromatin and promotes telomere-related processes , including telomere transcription , replication and recombination . Thus , histone acetylation may provide a scheme for yeast cells to maintain partially flexible telomeric heterochromatin that allows for normal changes in DNA metabolism .
To better understand the orchestrated events that are involved in establishing telomeric heterochromatin , we developed a new telomeric heterochromatin formation assay , building upon the de novo telomere addition system originally established by Gottschling's lab [16] . This new assay allowed us to study telomeric heterochromatin formation under conditions where global gene expression , especially the expression of the silencing-related genes , was not affected . Technically , we used a galactose-inducible HO endonuclease to cut a chromosome to expose a pre-inserted 81-bp telomeric “seed” for further telomere addition [16] ( Figure 1A ) . Chromosome “healing” was hardly observed in the first 6 hours upon HO induction . Later , the telomere seed was gradually extended in a telomerase dependent manner ( [16] and Figure 1B ) . The recruitment of Rap1 and Sir proteins onto regions 100-bp to 5 . 0-kb from the telomere seed upon HO induction was then monitored by chromatin immunoprecipitation ( ChIP ) . High occupancy of Rap1 was observed at the 81-bp telomere seed prior to HO induction ( Figure 1C ) , indicating that the 81-bp telomeric DNA tract is sufficient for Rap1 recruitment . Upon HO induction , the Rap1 occupancy at the newly formed telomere was barely altered in the first 6 hours and then gradually increased along with telomere elongation ( Figure 1B and 1C ) . Notably , upon HO induction , Sir proteins were recruited to the new telomere within the first 6 hours in an extremely efficient manner ( Figure 1D–1F ) . Once occupying a newly formed telomere , Sir proteins began to spread into the lateral regions up to 3 kb proximal to the newly formed telomeres ( Figure 1D–1F ) . Consistently , the expression of ADE2 , a gene near the telomere seed , was gradually reduced upon HO induction ( Figure 1G ) . Therefore , telomeric heterochromatin was quickly assembled at the 81-bp telomeric seed after HO induction . Since histone H4 hypoacetylation was required for the onset of telomere silencing [1] , [4] , [5] and K16 seemed to be the unique deacetylation target for Sir2 on H4 [3] , we were curious about the deacetylation kinetics of different lysines on H4 ( K5 , K8 , K12 and K16 ) during heterochromatin formation . To avoid recombination-induced histone acetylation , we deleted the RAD52 gene in our strains [17] . The result showed that , prior to HO cleavage , all lysine acetylation was robust at the subtelomeric region ( indicated as SubTel in Figure 1 ) ( Figure 2A ) . Upon HO induction , H4K8 and K16 acetylation levels decreased most substantially and were largely eliminated by 6 hours ( Figure 2A ) , a time point when Sir proteins saturated SubTel ( Figure 1D–1F ) . In contrast , H4K5 and K12 acetylation , especially K12 acetylation , decayed more slowly at SubTel during HO induction , and were still detectable after 24-hour HO induction ( Figure 2A ) . These observations raise a possibility that H4K5 and K12 acetylation may partially survive the formation of new telomeric heterochromatin . To exclude the possibility that anti-acetyl-lysine antibodies cross-reacted with other subtelomeric factors , we compared the levels of H4 lysine acetylation in a wild-type strain to that of “unacetylated strains” where the corresponding lysines were mutated to arginines , respectively , thereby preventing anti-acetyl lysine antibodies from recognizing corresponding residues . In the western-blot experiment , we found that chromatin derived from strains with the lysine to arginine mutation was not detected by the corresponding anti-acetyl lysine antibodies ( Figure 2B ) . In the ChIP experiment , we found that in the wild-type strain , all antibodies enriched GIT1 , a typical hyperacetylated region [18] , [19] ( Figure 2C ) . In contrast , lysine to arginine mutations eliminated corresponding anti-acetyl lysine antibodies from enriching any chromatin fragments ( Figure 2C ) . Additionally , increasing the amount of anti-acetyl-H4K12 antibody in the immunoprecipitation ( IP ) procedure couldn't recover chromatin in the H4K12R strain ( Figure S1 ) . These data are consistent with previous report [20] , and indicate that all anti-acetyl-H4 lysine antibodies perform appropriately in our ChIP experiments . To determine if the acetylation of H4K5 and K12 was generally associated with native telomeres that were not undergoing a double-strand break response [21] , we performed ChIP to examine the acetylation profile of H4 lysines at all yeast telomeres . Sir2 binding was firstly mapped ( Figure 3 , pink bars ) so as to mark the silencing status of the individual telomeres . We found that the abundance of Sir2 at different telomeres varied ( Figure 3 ) . Low amounts of Sir2 were mostly observed at telomeres that contain Y' elements ( http://www . yeastgenome . org/ and Figure 3 ) , supporting an anti-silencing role for these Y' elements [22] . The acetylation levels at subtelomeric regions were normalized to an HMR region , where we detected lowest level of acetylation ( Figure S3 , region D ) . Figure 3 showed that H4K12 acetylation was observed at nearly all telomeres in a manner that was independent of Sir2 binding . In contrast , the relative amount of H4K5 , K8 or K16 acetylation at telomeres seemed to correlate in an opposing manner to the level of Sir2 binding ( Figure 3 ) . For example , at the end of left arm of chromosome II where there was little Sir2 binding and all lysine acetylations were robust . In contrast , at the end of right arm of chromosome II where there was abundant Sir2 , only H4K12 acetylation was detectable ( Figure 3 ) . Essentially identical results were also obtained when yeast were grown in galactose culture ( Figure S2 ) . Furthermore , H4K12 acetylation was also detected at some regions of yeast HM heterochromatin ( Figure S3 ) . Taken together , these data suggest that H4K12 acetylation exists within yeast heterochromatin . We next set out to ascertain how the subtelomeric H4K12 acetylation pattern was established . Genome-wide nucleosome H4K5 , K8 and K12 acetylation in budding yeast is catalyzed by Piccolo NuA4 in a non-targeted manner [23] . We found that the subtelomeric H4K12 acetylation was sensitive to esa1-338 mutation ( Figure 3 , white bars ) . Since Esa1 is the catalytic subunit of NuA4 complex , these results support the idea that NuA4 is responsible for heterochromatic H4K12 acetylation . Interestingly , ChIP analysis revealed that Esa1 was specifically enriched at Sir2-abundant subtelomeres ( Figure 3 , red bars ) . Thus , heterochromatic H4K12 was acetylated by NuA4 in a targeted manner while the euchromatic H4K12 , as the case of other H4 lysines , was acetylated by Piccolo NuA4 in a non-targeted manner [23] . We found that the binding of Esa1 at subtelomeric region was restricted to the distal region because there was no Esa1 binding in the subtelomeric domain 3 . 2 kb∼7 . 6 kb from the end of chromosome XIV-R ( Figure S4A and S4B ) . A previous study showed that Hat1 , another lysine acetyltransferase in yeast , also possesses H4K12 acetylation activity [24] , however it is predominantly localized to the cytoplasm and thought to specifically acetylate free histone H4 [25] . Western-blot results showed that HAT1 deletion did not affect chromatic H4K12 acetylation ( Figure S4D ) . ChIP analyses revealed that the H4K12 acetylation level at the subtelomeric region of ChrXIV-R was not reduced , but rather modestly increased in hat1Δ cells ( Figure S4E ) . Therefore , these data suggest that Hat1 is likely not needed for dynamic H4K12 acetylation at telomeric heterochromatin . Histone acetylation is thought to regulate chromatin assembly [26] . To address whether H4K12R mutation affects subtelomeric nucleosomal organization , the chromatin from wild-type and H4K12R mutant cells was digested with increasing concentrations of micrococcal nuclease ( MNase ) and the chromatin structure of subtelomere III-L was analyzed by Southern-blot as previously reported [27] . The MNase digestion of the subtelomeric region at specific sites indicated the presence of a regular array of nucleosomes ( Figure 4A ) . There was little difference in subtelomeric nucleosome organization when comparing wild-type to H4K12R cells ( Figure 4A ) , indicating that H4K12 does not influence the nucleosome organization of subtelomeric DNA on chromosome III-L . Surprisingly , the upper bands representing the undigested telomere-containing DNA , were much shorter in H4K12R cells when compared to wild-type cells ( Figure . 4A ) , indicating that H4K12 acetylation affects the telomere length of chromosome III-L . To investigate whether H4K12 acetylation regulates telomere length in a universal manner , we examined the average telomere length of wild-type and H4K12R mutant cells . Strikingly , Southern-blot analysis revealed that telomeres in H4K12R mutant cells were much shorter than that in wild-type cells ( Figure 4B ) . Consistently , the ESA1 mutant esa1-338 , which lost telomeric H4K12 acetylation ( Figure 3 ) , also had shorter telomeres ( Figure 4B ) . However , the telomere length in H4K12R esa1-338 double mutation cells resembled that in esa1-338 cells , rather than that in H4K12R cells ( Figure 4B ) . Because H4K12R mutation didn't affect Esa1-telomere association ( Figure S4B ) , it remains possible that the shorter telomeres observed in esa1-338 mutant could not be solely attributed to a defect in H4K12 acetylation . The major structural components of telomere heterochromatin are Sir2 , Sir3 and Sir4 [1] . Previous studies have shown that deletion of SIR3 or SIR4 caused telomere shortening , suggesting that disturbing telomere heterochromatin structure affects telomere replication [28] . Because the distal telomeres are devoid of nucleosome structure [29] , we proposed that the telomere length defect observed in H4K12R mutant was attributable to a change in telomere heterochromatin structure . To test this possibility and to elucidate the molecular mechanism by which histone H4K12 acetylation affects telomere length , we examined the structural change ( s ) of telomere heterochromatin in H4K12R mutant cells . Immuno-staining analysis of myc-tagged Sir3 ( Sir3-myc ) with monoclonal anti-myc antibody revealed that H4K12R mutation did not affect the sub-cellular localization of telomeres ( Figure S5 ) , excluding the possibility that H4K12 affects the perinuclear localization of telomeres , which potentially regulates telomerase activity [30] . A distinctive feature of telomeric heterochromatin involves high-order structure , such as a fold-back loop-like structure [31]–[33] and such structure plays a role in telomere length homeostasis [28] . It was possible that H4K12R mutation affected the access of telomerase machinery to telomere end and thereby impaired telomere replication . To test this idea , we performed a ChIP experiment to compare the telomere binding of Est2 , the catalytic subunit of telomerase , in wild-type and H4K12R cells . Interestingly , the telomere association of Est2 was greatly reduced by H4K12R mutation ( Figure 4C ) , supporting our idea that H4K12 influences the recruitment of telomerase to telomeres . The formation of heterochromatin in yeast is dependent on Sir proteins [1] . To determine if H4K12 acetylation directly modulated the binding of Sir proteins , we carried out ChIP to compare the chromatin binding of Sir2 and Sir3 in wild-type and histone mutant cells at regions 0 . 7 kb∼12 kb from the end of chromosome XIV-R ( TELXIV-R ) ( Figure 5A–5C ) . We found that Sir proteins in wild-type cells were present only in telomeric heterochromatin as far as 3 kb from the end of the chromosome . In H4K16R mutant cells , the abundance of Sir proteins was reduced within heterochromatin but was greatly increased at heterochromatin adjacent regions ( Figure 5B and 5C ) , a result that is representative of a typical heterochromatin over-spreading phenotype [6] , [7] . Notably , compared to a H4K5R or H4K8R mutation that did not alter the binding of Sir proteins at TELXIV-R telomere ( Figure 5B and 5C ) , the H4K12R mutation resulted in 2 . 52-fold more Sir2 binding and 3 . 09-fold more Sir3 binding 0 . 7 kb from the telomere end , but not at regions farther from the chromosome end . We also examined Sir2 binding at all other telomeres and found that the H4K12R mutation resulted in enhancement of Sir2 binding at most telomeres , especially at those where Sir2 was already abundant ( Figure S6A ) . Because the H4K12R mutation does not perturb the transcriptional profile of the genome [34] , and the expression and nuclear distribution of Sir proteins were not affected by the H4K12R mutation ( Figure S5 and Figure S7 ) , we therefore favor a model where the acetylation status of H4K12 directly affects Sir protein binding . We also analyzed the heterochromatin structure in H4K12Q mutant . Presumably , a lysine to glutamine mimicked a hyperacetylated state of the corresponding lysine . However , we found that the abundance and distribution of Sir2 and Sir3 in H4K12Q cells were largely similar to that in H4K12R cells ( Figure S6B and S6C ) . We proposed that glutamine does not accurately represent the hyperacetylated state of H4K12 in this case . Together , these data suggest that the H4K12 acetylation suppresses the over-congregation of Sir proteins at telomeric heterochromatin . To investigate if the regulation of telomere replication by H4K12 acetylation is dependent on the telomeric heterochromatin structure , we detected the effect of SIR2 deletion on the telomere length in H4K12R mutant cells . As shown in Figure 5D , sir2Δ cells exhibited a modest reduction of telomere length , which is much longer than that observed in H4K12R mutant cells . Interestingly , deletion of SIR2 efficiently rescued the severe telomere-shortening phenotype in H4K12R mutant cells ( Figure 5D ) , indicating that H4K12 acetylation regulates telomere length though the Sir2 pathway . In addition , telomere length in H4K5R , K8R or K16R cells was indistinguishable from that in wild-type cells ( Figure 5D ) , consistent with their already defined effect on the telomeric association of Sir proteins . Taken together , these data strongly supported the notion that H4K12 acetylation regulates telomere replication directly via modulating telomeric heterochromatin structure . Telomeres are hotspots for recombination when they are deprotected . Telomerase-negative yeast cells could undergo homologous recombination on Y' or TG1–3 telomeric sequences , thus generating Type I or Type II post-senescence survivors , respectively [35] . Since Type II survivors grew much faster than Type I , liquid culturing of post-senescent est2Δ cells yielded primarily Type II survivor cells [35] ( Figure 6A ) . Interestingly , deletion of the catalytic subunit of telomerase EST2 in the H4K12R mutant background eventually led to the generation of a population of cells that contained amplified Y' telomeric sequence ( Figure 6A ) , a hallmark of Type I survivor cells . Therefore , we conclude that H4K12 acetylation suppresses homologous recombination of TG1–3 tracts during the creation of telomerase-null post-senescent survivors . Since histone H4K12 acetylation affected both telomere length and recombination , we wondered whether it regulated the senescence rate of telomerase inactive cells . Therefore , we compared the growth potential of est2Δand H4K12R est2Δ cells . The result showed that H4K12R mutation greatly accelerated the senescence rate of est2Δ cells ( Figure 6B ) . Further deletion of SIR2 could suppress the accelerated senescence rate of H4K12R est2Δ cells ( Figure 6B ) . These data suggested that H4K12 acetylation delays senescence driven by Sir-dependent telomere dysfunction . Previous work had shown that histone H4 mutations that led to increased telomere position effect ( TPE ) were usually associated with a dramatic decrease of K12 acetylation [36] . To further address this point , we carried out TPE assay [37] to evaluate the effects of histone mutations on the transcriptional state at heterochromatin . A URA3 gene was inserted into a locus that was proximal to the right telomeric TG-tracts of chromosome XIV . Strains were then tested for the relative URA3 expression in histone mutant strains compared with wild-type level . In agreement with a previous report [38] , H4K16R mutation markedly reduced telomere silencing ( Figure 7 ) . Notably , H4K12R , but not K5R or K8R cells , had less URA3 expression than wild-type cells ( Figure 7 ) , suggesting that H4K12R mutation enhances telomere silencing . H4K12R mutation in sir2Δ background had similar URA3 expression to that in sir2Δcells ( Figure 7 ) , indicating that the increased silencing by H4K12R mutation is Sir-dependent . Therefore , we concluded that H4K12 acetylation contributes to the basal transcription within telomeric heterochromatin .
In this study , we have employed the de novo telomere addition assay [16] as a de novo telomeric heterochromatin formation system , to monitor chromatin dynamics occurred on a newly-formed telomere ( Figure 1 ) . Compared with the traditional Sir3-induction system [39]–[41] , this system works in a more physiological condition , with normal Sir proteins level and much less transcriptional changes across the genome . Hence , the kinetics of multiple events in the course of telomeric heterochromatin formation can be more accurately followed . The experimental observations we have made in this study establish histone H4K12 acetylation as an important component of yeast telomeric heterochromatin . We have provided phenotypic , genetic and mechanistic evidence to support the presence of H4K12 acetylation inside telomeric heterochromatin . By using stringently controlled ChIP analyses , we detected H4K12 acetylation at most telomeres ( Figure 3 ) . Compared with euchromatic H4K12 acetylation , the level of heterochromatic H4K12 acetylation was relatively low but was greatly elevated by SIR2 deletion ( Figure S3 ) , a phenomenon also observed in earlier reports [20] , [42] . Therefore , H4K12 acetylation , as is the case of acetylation of other H4 lysines , is suppressed by heterochromatin structure . Genetically , the H4K12R mutation , which mimicked an unacetylated state of K12 , increased Sir protein binding at telomeric heterochromatin and altered several dynamic telomere-related chromosomal processes ( Figure 4 , Figure 5 , Figure 6 , Figure 7 ) . Heterochromatic H4K12 acetylation coincides with H4K12 as a memory mark for the heritable chromatin structure in yeast [36] . Mechanistically , Esa1 , the catalytic subunit of NuA4 HAT , bound to silenced telomeres and was responsible for H4K12 acetylation ( Figure 3 ) . Since Arp4 , another subunit of NuA4 complex , is also enriched at heterochromatic domains [43] , it is possible that the whole NuA4 complex binds to telomeric heterochromatin and plays a direct role in the acetylation of H4K12 . Earlier work suggested a phosphorylated H2A-dependent mechanism for the recruitment of NuA4 during DNA damage repair [44] . However , due to the fact that normal telomeres are protected from being recognized as double-strand DNA break and phosphorylated H2A is absent from normal telomeres , we propose that yeast cells take a distinct strategy such as a Rap1-dependent mechanism to recruit NuA4 onto telomeric heterochromatin [45] . Esa1 preferentially acetylated nucleosome on both H4K5 and K12 in vivo [46] and in vitro ( Figure S4C ) . This raised a question of why H4K5 was hypoacetylated at telomeric heterochromatin . We suspected that another histone deacetylase besides Sir2 was involved in the establishment of the acetylation pattern at telomeric heterochromatin . Two candidates are Rpd3 and Hda1 , which displayed the highest in vivo activity toward acetylated H4K5 from among H4K5 , K8 , K12 and K16 [47] . Indeed , our recent study did reveal a genetic interaction between Rpd3L and H4K5 at subtelomeric regions [48] . Moreover , analysis of the CIDMS/MS spectra shows K12 to be the most highly acetylated site ( 54% ) from among H4K5 , K8 and K12 , followed by K5 ( 32% ) , and K8 ( 24% ) [49] , suggesting that K12 acetylation covers a much wider range of yeast chromatin than that of K5 or K8 acetylation . NuA4 complex also functions to prevent the Sir complex from spreading out of heterochromatic domains [19] , [50]–[52] . Therefore , mutation of ESA1 or other key subunits of the NuA4 complex resulted in gene silencing near heterochromatin [19] , [50] , [51] and a modest reduction of silencing within telomeric heterochromatin [53] , [54] . The histone H4K12 acetylation per se has little effect on heterochromatin boundary activity ( Figure 5B and 5C ) , however , since it is the uniquely acetylated site within heterochromatin , abolishment of histone H4K12 caused a increase of heterochromatin silencing ( Figure 7 and [36] ) . Chromatin modifications have been implicated in telomere elongation in several organisms [55] , [56] . Recent study on H4K16 demonstrated the relationship between histone acetylation and telomere regulation [57] . The natural presence of H4K12 acetylation at telomeres and Sir-dependent regulation of telomere replication via H4K12 have provided additional direct evidence supporting the proposal that chromatin modifications affect telomere homeostasis . Elimination of H4K12 acetylation increased subtelomeric binding of Sir proteins ( Figure 5B and 5C ) , accelerated senescence in est2Δ cells ( Figure 6B ) and suppressed homologous recombination within TG-tracts ( Figure 6A ) . By contrast , inactivation of Sas2 , the acetyltransferase of H4K16 [38] , decreased Sir3 binding at telomere ends and thereby delayed senescence in tlc1Δ cells through homologous recombination-dependent mechanism [57] . Therefore , H4K12 acetylation and K16 acetylation seem to play opposite roles in the Sir-dependent regulation of homologous recombination at telomeric heterochromatin . It is quite interesting that H4K12 and K16 are in close proximity to each other but have opposing roles in regulating telomere dynamics through Sir-dependent mechanisms . Finally , a recent paper showed that Esa1 and Rpd3L controlled H4K12 acetylation , which is necessary for cell growth and viability [46] . Although H4K12R does not change the genome-wide transcription profile [34] , it is still possible that H4K12 also fine-tunes the chromatin structure at sequences other than the telomeric heterochromatin . In conclusion , heterochromatin is known as a highly condensed chromatin domain that is transcriptionally silent [1] . However , pioneering studies have recently revealed the dynamic aspect of yeast heterochromatin [11]–[13] , [15] . In this study , we have built on these pioneering studies and shown that the H4K12R mutation led to a more condensed telomeric heterochromatin structure ( Figure 5 ) and more static telomere metabolism ( Figure 4 , Figure 5 , Figure 6 , Figure 7 ) . Therefore , we propose that H4K12 provides a mechanism for yeast cells to maintain partial plasticity of their telomeric heterochromatin ( Figure 8 ) . Interestingly , histone H4K12 acetylation has also been observed at the chromocenter in fly [58] . Mst1 , the orthologue of Esa1 in fission yeast , acetylates H3K4 at pericentric heterochromatin to regulate heterochromatin reassembly [59] . TIP60 , the orthologue of NuA4 in mammals , physically interacts with Sirt1 , the mammalian homologue of Sir2 [60] , and associates with tri-methylated H3K9 , a hallmark of heterochromatin [61] . Hence , it will be of great interest to determine if histone acetylation also plays a general role in heterochromatin dynamics in other eukaryotes .
Antibodies , yeast strains and primers used in this study are listed in Tables S1 , S2 , S3 , respectively . ChIP assays were performed as described [18] , [19] . Most ChIP products were directly analyzed by real-time Q-PCR using SYBR green as a label ( TOYOBO ) . Alternatively , pellet and whole-cell extract DNAs were analyzed by Q-PCR performed in a linear range with 32P-dATP , electrophoresis through 6% PAGE in Tris-Borate-EDTA buffer , and phosphorimager quantification of radioactive bands in dried gels . The relative enrichment value represented the ratio ( IPs/Input ) at indicated loci relative to internal control . All ChIP experiments were performed in triplicate on paired isogenic wild-type and mutant strains . After protein expression was induced by galactose for indicated time , total DNA was isolated from yeast cells by glass beads lysis , proteinase K digestion and extraction with QIAGEN Genomic DNA Kit , followed by RNaseA and RNaseT digestion , phenol/chloroform extraction and re-precipitation . Finally , purified genomic DNA was digested into mononucleoside 5′-monophosphates with Nuclease P1 ( Sigma ) . Nucleosides were separated by an AQ-C18 column ( 5 µm , 4 . 6×250 mm , Welch Materials Inc . ) guarded by a precolumn ( Phenomenex , Security Guard ) , using a Beckman device ( SYSTEM GOLD 125 Solvent Module and SYSTEM GOLD 166 Detector ) . The eluate was obtained using a flow rate of 1 ml/min and 100% buffer A ( 10 mM KH2PO4/H3PO4 , pH 3 . 7 ) for 50 minutes , followed by a shift to 70% A/30% methanol for 10 minutes , and then to 100% buffer A for 10 minutes . Peaks were quantified by measuring their heights with a 32 Karat software V7 . 0 after identity confirmation by comparison of spikes with different combinations of dCMP and 5′-me-dCMP ( Hongene Biotechnology Ltd . Shanghai ) . At least three independent yeast clones were assayed for each construct . Southern blot was performed as described [62] . Yeast genomic DNAs were digested with restriction enzymes as indicated , separated by 1 . 2% agarose gel electrophoresis , and transferred to Hybond-N membrane ( Amersham ) . The blot was hybridized with a 32P-dCTP incorporated probe as indicated . The radioactive signal was detected by phosphorimager . Micrococcal nuclease ( MNase ) sensitivity assay was performed as described [27] . Briefly , Cells from 50-ml cultures were collected by centrifugation , treated with zymolyase , and digested with micrococcal nuclease ( MNase ) . genomic DNA samples were purified , digested with BamHI , resolved in 1 . 2% agarose gels . Then the cutting profiles were visualized after hybridization with an internal probe from Ty5-1 . Cells were grown in YPD medium overnight to a density of ∼1–2×107 cells/ml and were fixed for 30 min by incubation with 3 . 7% formaldehyde . Next , cells were washed with 0 . 1 M potassium phosphate ( pH 6 . 5 ) and P solution ( 1 . 2 M sorbitol , 1 M K2PO4 ) , and re-suspended in P solution . Cells were subsequently treated with 0 . 1 mg/ml Zymolyase ( 20T , MP Biomedicals ) for 10 min , washed with P solution , spotted on Poly-L-Lysine pre-treated slides . After rinsing in PBS-T buffer ( PBS containing 0 . 1% Triton X-100 and 1% BSA ) , slides were incubated overnight with anti-Myc , anti-Rap1 and anti-Nop1 antibody diluted in PBS containing 1% BSA . Slides were then washed with PBS-T and incubated with the appropriate secondary antibodies conjugated to Cy3 or fluorescein isothiocyanate ( FITC ) . The DNA fluorescence signal was detected by DAPI ( 1 µg/ml in Phosphate Buffered Saline ( PBS ) solution ) staining . Slides were mounted with PBS containing 1 mg/ml p-phenylenediamine , 2 . 5 µM NaOH , and 90% glycerol . Confocal microscopy was performed on a Leica TCS SP2 microscope with a 63× lamda blue objective ( oil ) . Image processing including similar filtration and threshold levels was standardized for all images . N terminal 6xHis tagged Esa1 protein was overexpressed and purified in E . coli according to the manufacturer's instructions ( GE Healthcare ) .
|
The genetic material in eukaryotes is packaged into chromatin . The chromatin structure is orchestrated such that euchromatic regions are relatively uncondensed and accessible to factors that bind DNA , whereas heterochromatic regions are densely packaged into higher-order conformations . The compact nature for heterochromatin may endanger normal DNA metabolism , such as DNA replication and recombination . We found that targeted histone acetylation provided a way for cells to maintain a relatively plastic heterochromatin structure that is necessary for DNA metabolisms within telomeric heterochromatin . Therefore , although heterochromatic domains are largely silenced , they are not as static as we previously assumed , and the dynamic aspect of heterochromatin is directly attributable to changes in its own chemical properties .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"molecular",
"biology/histone",
"modification",
"molecular",
"biology/chromatin",
"structure"
] |
2011
|
Histone H4 Lysine 12 Acetylation Regulates Telomeric Heterochromatin Plasticity in Saccharomyces cerevisiae
|
Animal and human trypanosomiasis are constraints to both animal and human health in Sub-Saharan Africa , but there is little recent evidence as to how these parasites circulate in wild hosts in natural ecosystems . The Luangwa Valley in Zambia supports high densities of tsetse flies ( Glossina species ) and is recognised as an historical sleeping sickness focus . The objective of this study was to characterise the nature of the reservoir community for trypanosomiasis in the absence of influence from domesticated hosts . A cross-sectional survey of trypanosome prevalence in wildlife hosts was conducted in the Luangwa Valley from 2005 to 2007 . Samples were collected from 418 animals and were examined for the presence of Trypanosoma brucei s . l . , T . b . rhodesiense , Trypanosoma congolense and Trypanosoma vivax using molecular diagnostic techniques . The overall prevalence of infection in all species was 13 . 9% ( 95% confidence interval [CI]: 10 . 71–17 . 57% ) . Infection was significantly more likely to be detected in waterbuck ( Kobus ellipsiprymnus ) ( Odds ratio [OR] = 10 . 5 , 95% CI: 2 . 36–46 . 71 ) , lion ( Panthera leo ) ( OR = 5 . 3 , 95% CI: 1 . 40–19 . 69 ) , greater kudu ( Tragelaphus strepsiceros ) ( OR = 4 . 7 , 95% CI: 1 . 41–15 . 41 ) and bushbuck ( Tragelaphus scriptus ) ( OR = 4 . 5 , 95% CI: 1 . 51–13 . 56 ) . Bushbucks are important hosts for T . brucei s . l . while the Bovidae appear the most important for T . congolense . The epidemiology of T . vivax was less clear , but parasites were detected most frequently in waterbuck . Human infective T . b . rhodesiense were identified for the first time in African buffalo ( Syncerus caffer ) and T . brucei s . l . in leopard ( Panthera pardus ) . Variation in infection rates was demonstrated at species level rather than at family or sub-family level . A number of significant risk factors interact to influence infection rates in wildlife including taxonomy , habitat and blood meal preference . Trypanosoma parasites circulate within a wide and diverse host community in this bio-diverse ecosystem . Consistent land use patterns over the last century have resulted in epidemiological stability , but this may be threatened by the recent influx of people and domesticated livestock into the mid-Luangwa Valley .
Trypanosomes are true multi-host parasites capable of infecting a wide range of wildlife species that constitute a reservoir of infection for both people and domestic animals . Natural infections of trypanosomes in wildlife were first identified during the Sleeping Sickness Commission in the Luangwa Valley , Zambia [1] , which had been set up following the identification of the first case of the ‘rhodesian’ form of human sleeping sickness in 1910 [2] . Since that time , many surveys from across Africa have identified extensive natural infection of wild animal hosts with a variety of trypanosome species [3]–[18] . Although much knowledge about infection rates in wildlife has been generated , there have been very few attempts to conduct epidemiological analyses of data from surveys of wildlife . This is , in part due to difficulties in the collection of sufficiently large sample sizes of a representative nature from wildlife species to enable a thorough statistical analysis [19]–[21] . Some studies have examined the association between wildlife infection rates and factors that might influence them [8] , [17] , but most have reported host species infection rates without any statistical analysis of the data . Consequently , knowledge of the factors associated with trypanosome infection in wildlife is limited . In the Luangwa Valley , high tsetse ( and trypanosome ) challenge coupled with high levels of predation and an arid environment , have meant that livestock keeping has been virtually non-existent in this area . The majority of the land is protected for the conservation of the environment and industries based around both consumptive and non-consumptive tourism form the main sources of revenue for local peoples . Pressure for land is lower than in many wildlife areas of Africa , although there has been an influx of people from Eastern Province into the Central Luangwa Valley in recent years . Three general surveys of trypanosome infections of wildlife have been conducted [1] , [7] , [13] . All three surveys have utilised parasitological methods of diagnosis , although the study by Keymer , 1969 combined parasitological identification with rodent inoculation and used the blood incubation infectivity test ( BIIT ) [22] to investigate the presence of human infective T . b . rhodesiense . This human infective , zoonotic subspecies has been identified in bushbuck ( Tragelaphus scriptus ) , duiker ( Sylvicapra grimmia ) , giraffe ( Giraffa camelopardalis thornicrofti ) , impala ( Aepyceros melampus ) , lion ( Panthera leo ) , warthog ( Phacocoerus africanus ) and waterbuck ( Kobus ellipsiprymnus ) [23] , impala and zebra ( Equus quagga boehmi ) [24] and warthog [7] , [25] in the Luangwa Valley to date . The investigation of the wildlife reservoir for the human infective subspecies has for a long time been complicated by difficulties in the definitive diagnosis of the parasite . Although the BIIT [22] represented a significant advance in diagnostic capabilities , recent advances in molecular diagnostic methods have resulted in the development of a new , highly specific and robust diagnostic tests for this parasite [26] . Advances in molecular methods of diagnosis have also resulted in the development of a multispecies polymerase chain reaction ( PCR ) that is capable of differentiating all the major pathogenic trypanosomes of domestic livestock in a single test [27] . This study aimed to apply these novel laboratory techniques to further characterise the wildlife reservoir for trypanosomes in the Luangwa valley . A statistical analysis of infection rates in wildlife was to be used to identify the principle components of the wildlife reservoir community for T . brucei s . l . , T . congolense and T . vivax , in the absence of any domesticated animal hosts . Additionally , in order to understand the transmission of trypanosomes better , the data was to be used to identify risk factors for infection .
A cross-sectional survey of the trypanosomiasis prevalence in wildlife hosts was conducted in the Luangwa Valley from 2005 to 2007 . The Luangwa Valley is situated in the Eastern and Northern provinces of Zambia and represents an extension of the Great Rift Valley in East Africa . Sample collection took place between May and November of each year as the main roads north and south within the Luangwa Valley are impassable during the rainy season . Due to the inherent difficulty in collecting samples from wildlife in remote areas samples were collected using non-randomised convenience sampling techniques . Samples were collected using two sampling approaches . Firstly , professional hunters were recruited to provide samples from animals harvested as part of the commercial ‘safari hunting’ system operating in Zambia . These animals are shot under licence in game management areas ( GMAs ) outside the national parks and hunting licenses are issued under a quota system regulated by the Zambian Wildlife Authority ( ZAWA ) . The study area covered eight GMAs with a widespread distribution across the north and central Luangwa Valley and one private hunting area further south in Luembe Chiefdom ( Figure 1 ) . Additional samples were also collected from animals destroyed in GMAs as part of population control measures or problem animal control . Secondly , samples were collected from animals immobilised or captured as part of routine conservation management activities within the national parks of the Luangwa Valley . The majority of the animals sampled were captured in North Luangwa National Park ( NLNP ) by a commercial game capture unit as part of a re-stocking programme for the Malawi / Zambia Transfrontier Conservation Area . Additional samples were collected from South Luangwa National Park and Lower Lupande GMA . The overall study area covered represents the largest area covered by any trypanosomiasis survey of wildlife in the Luangwa Valley to date . This study utilised blood samples collected from wild animals that had already been shot as part of commercial safari hunting activities under a strictly licensed quota system managed by the Zambian Wildlife Authority . These animals were not shot for the purpose of this study . Additional samples were also collected from animals captured and released unharmed as part of a translocation exercise for the Zambia / Malawi Transfrontier Conservation Area . All activities in protected areas were fully approved by the Zambian Wildlife Authority ( permit numbers 316295 and 323947 ) . All sampling protocols were approved by the Zambian Wildlife Authority and the Zambian Department of Veterinary and Livestock Development . The relevant export and import licences were obtained for samples from animals on CITES appendices 1 and 2 . Blood samples were collected onto FTA® Cards ( Whatman , Maidstone , Kent , UK ) and were left to air dry out of direct sunlight . Samples were stored in multi-barrier pouches ( Whatman ) with desiccant at ambient temperature prior to being processed in the laboratory . Samples collected by hunters were collected either directly from the bullet wound immediately after the animal was shot , or at the time of skinning from heart blood or muscle smeared onto the FTA® card . A small number of samples ( 26 ) were collected onto Isocode cards ( Schleicher & Schuell , Dassel , Germany ) , rather than FTA® cards , and were stored in a similar manner . The remaining samples were collected from a superficial ear vein into heparinised capillary tubes and applied to FTA® Cards . Where sufficient amounts of blood could not be obtained from an ear vein , blood samples were collected from larger peripheral veins ( jugular , saphenous , cephalic or abdominal ) into EDTA coagulated vacutainers and stored at 4° centigrade prior to application to FTA® Cards . Each sample was assigned a unique identification number . Supplementary data on the species , sex , age , date and location were recorded . Age was recorded using knowledge of breeding seasons and examination of the physical maturity of the animal to assign it to one of three categories: ( i ) born in the present breeding season or within the last six months ( juvenile ) ; ( ii ) born the previous breeding season , but not yet mature ( sub-adult ) ; ( iii ) physically mature ( adult ) . For the samples collected through the professional hunter survey , Global Positioning System ( GPS ) coordinates were requested for all samples , but a number of hunters were unable to provide this information . GPS coordinates were recorded for all other samples using a hand-held Garmin GPS device . All samples collected were screened using PCR methods . Eluted deoxyribonucleic acid ( DNA ) was used to seed each PCR and was prepared using the following sample preparation protocols . Samples collected onto Whatman FTA® Cards were firstly prepared for elution by punching five 3 mm diameter discs from each card using a Harris Micro-Punch ™ Tool . The use of multiple sample punches from each card increases the likelihood of detection of trypanosomes present at low levels of parasitaemia [28] . The discs were then washed twice in 1000 µl of Whatman Purification Reagent for 15 minutes followed by two washes in 1000 µl of 1× concentrated TE buffer for 15 minutes to remove any residual Whatman purification reagent . The discs were then transferred to 100 µl PCR tubes , with all five discs from each sample placed in one tube , and allowed to dry at room temperature . Once the discs had dried , a Chelex 100® elution protocol was used to elute the DNA [29] . 100 µl of 5% ( w/v ) Chelex solution ( sodium form , 100–200 mesh; Bio-Rad Laboratories , Hemel Hempstead , Hertfordshire , UK ) was added to each sample and mixed thoroughly by pipetting . The samples were then heated to 90°C for 30 minutes in a DNA Engine DYAD™ Peltier Thermal Cycler . Eluted DNA was stored at 4°C and used in a PCR within 12 hours of elution . Unused sample DNA was stored in aliquots at −20°C for longer periods . Samples collected onto Isocode cards , were prepared for elution by punching three 3 mm diameter discs from each sample card and placing them together in a sterile 1 . 5 ml microcentrifuge tube . 500 µl of sterile de-ionised water ( dH2O ) was added and the tubes pulse vortexed for five seconds a total of three times . The discs were gently squeezed against the side of the tube and placed in a sterile 0 . 5 ml microcentrifuge tube . The DNA was eluted using a simple water elution protocol . 100 µl of dH2O was added and the tubes were then heated to 95°C for thirty minutes in a DNA Engine DYAD™ Peltier Thermal Cycler . The tubes were gently tapped twenty times to mix the sample and the discs containing the matrix were removed . The eluate was stored at 4°C and used in a PCR within 12 hours of elution . Excess sample eluate was stored at −20°C in 10 µl aliquots . Samples were initially screened using a multispecies nested PCR that distinguishes all clinically important African trypanosome species and some sub-species [27] . The PCR targets the Internal Transcribed Spacers ( ITS ) of the small ribosomal subunit ( 200 copies per genome ) , producing different sized products for different trypanosome species . ITS-PCR was performed on each sample in a standard reaction volume of 25 µl using 1 µl of eluate as a template for each reaction , under the reaction conditions described by Cox et al [27] . All samples were also screened using a species-specific PCR for T . brucei s . l . [30] . The TBR-PCR is a species specific PCR for trypanosomes belonging to the Trypanozoon subgenus . The primers are designed to amplify a target region with a copy number of 10 , 000 per genome making it a highly sensitive test . The PCR was carried out on each sample using 5 µl of eluate in a standard reaction volume of 25 µl under the reaction conditions described by Moser et al [30] . Any samples detected as being positive for T . brucei s . l . using either of the above PCRs were then subjected to a multiplex PCR for the detection of the T . brucei rhodesiense subspecies [26] , [31] . The two T . brucei s . l . subspecies , T . brucei brucei and T . brucei rhodesiense are distinguished by the presence of the SRA gene in the latter's genome . The multiplex PCR is designed to amplify the SRA gene , thereby enabling the differentiation of the two subspecies . As the SRA gene is a single copy gene , primers amplifying the single copy GPI-PLC gene are also included within the PCR as a positive control to show that enough genomic material was present for the SRA gene to be detected if present . A failure to detect the GPI-PLC gene in TBR-PCR positive samples might suggest that the prevalence of T . b . rhodesiense was being underestimated . 5 µl of eluate from each positive sample was used in a standard reaction volume of 25 µl under the reaction conditions described by Picozzi et al [26] . In order to improve the sensitivity of the method , the reaction was run three times for each sample using 45 cycles and three times using 50 cycles . In all PCRs , a positive control ( genomic DNA ) and two negative controls ( blank FTA punch and water ) were run with each reaction . A DNA Engine DYAD™ Peltier Thermal Cycler was used to run the reactions and PCR products were separated by electrophoresis in a 1 . 5% ( w/v ) agarose gel containing 0 . 5 µg/ml ethidium bromide . Separated products were then visualised under ultraviolet light in a transilluminator . Logistic regression models with binomial errors were used for the investigation of trypanosomiasis prevalence . Data was initially entered and evaluated using the Microsoft ®Office Excel 2003 spreadsheet program . All analytical exploration of data was conducted using the statistical software package , R: A language and environment for statistical computing [32] . Additional functions included within the Epicalc 2 . 7 . 1 . 2 . package for R ( V . Chongsuvivatwong ) were also used in the analysis . The likelihood ratio test was used to assess the significance of individual factors in each model . For individual factor categories , the likelihood of infection in comparison to the reference category was presented as the odds ratio ( OR ) . The Walds statistic was used to assess the significance of the OR and is presented as the probability ( p ) value . Statistical significance was accepted at the 95% confidence level throughout the analysis . The models were firstly used to investigate the overall prevalence detected of all trypanosome species combined . The analysis was then repeated to investigate the prevalence of T . brucei s . l . , T . congolense and T . vivax separately . The effect of host species , age , sex , area , month and year on trypanosome prevalence detected was examined using each factor as an explanatory variable . The potential confounding effect of sample collection method was also investigated with each sample being assigned to a category according to whether it was sampled alive or dead . The data from the samples collected by hunters were also initially analysed as a separate dataset as were the data from the samples collected from national parks before the whole dataset was analysed together . Over-saturation of some FTA cards with blood was observed during laboratory analysis and there was a concern that excess haem might interfere with the PCR for these samples . To investigate this each sample was assigned to a non-oversaturated category if the colour of the eluate was clear and to an over-saturated category if the eluate was discoloured by residual blood pigments . To further investigate the variation in prevalence between species and to facilitate a multivariable analysis , three methods of grouping the species sampled were compared . Firstly , a grouping based on the taxonomic classification of species at the sub-family level ( or family level where no sub-family exists ) was used . This followed the standard text by Wilson and Reeder [33] . Secondly , a grouping based on both the predominant vegetation type that the species favoured ( open , closed or mixed ) and the territorial or spatial movement patterns of the species ( sedentary or non-sedentary ) was investigated , again using the above text as a reference . Habitat was classified in this way in order to reflect potential association with preferred tsetse habitat . The final grouping method investigated was designed to reflect the blood meal preferences of the three tsetse species found in the Luangwa Valley . Clausen et al's publication on blood meal preferences [34] was used as reference to assign each wild animal species sampled from into one of three levels ( low , medium and high ) depending on the proportion of blood meals it accounted for . Although this publication contained many samples from Zambia , blood meal preferences were presented only by tsetse species not by geographical region so no regional values could be used . Additionally , many species sampled from were not included in the publication and for these species more general publications on host preferences were used to subjectively assign categories [35] , [36] . The groupings and how they were calculated are summarised in Table 1 . The data was initially explored at the univariable level and then at the multivariable level . However , multivariable analysis was only possible for overall trypanosome prevalence as there was inadequate data for the individual trypanosome species .
In total 418 samples were collected from 24 species in the survey ( Table 2 ) . The majority of these were collected from GMAs through the professional hunter survey in which 331 samples were collected from 22 species . All of these samples were from adult animals and only four were from female animals . A total of 80 samples from five species were collected from the NLNP and these were more representative with 34 samples from male animals and 46 from females . Twenty-four sub-adults were sampled along with two juveniles , the remainder being adults . An additional seven samples were collected from the other management activities . The cumulative prevalence of all trypanosomes in the dataset was 13 . 9% ( 95% CI: 10 . 71–17 . 57% ) . Four mixed infections were detected giving an overall prevalence of mixed infections of 1 . 0% ( 95% CI: 0 . 26–2 . 43% ) . The percentage of total infections present as mixed infections was 6 . 9% . All involved T . brucei s . l . , with three occurring concurrently with T . congolense ( one each in a bushbuck , warthog and wildebeest ( Connochaetes taurinus cooksoni ) ) and one occurring concurrently with T . vivax ( in a waterbuck ) . The effect of wild animal species on the overall prevalence of trypanosome infections was highly significant ( p<0 . 001 ) and several species had a statistically significant increased risk of being infected with trypanosomes ( Table 3 ) . Waterbuck were the most likely species to be detected as being infected , with a significant OR of 10 . 5 ( 95% CI: 2 . 36–46 . 71 , p = 0 . 002 ) . Lion , greater kudu ( Tragelaphus strepsiceros ) and bushbuck were also significantly more likely to be detected as being infected , with respective ORs of 5 . 3 ( 95% CI: 1 . 40–19 . 69 , p = 0 . 014 ) , 4 . 7 ( 95% CI: 1 . 41–15 . 41 , p = 0 . 012 ) and 4 . 5 ( 95% CI: 1 . 51–13 . 56 , p = 0 . 007 ) . The prevalence detected in each species with at least one positive sample is shown in Figure 2 ( A ) . The effect of taxonomy group on the overall trypanosome prevalence was highly significant ( p = 0 . 002 ) . However , no individual taxonomy group had a significantly increased risk of being infected compared with the reference Suidae group . The group with the highest prevalence was the Pantherinae and the odds of this group being detected as infected with trypanosomes approached significance ( OR = 2 . 8 , 95% CI: 0 . 90–8 . 75 , p = 0 . 077 ) . The prevalence detected in each sub-family is shown in Figure 2 ( B ) . The effect of habitat group was also highly significant as a factor ( p<0 . 001 ) with the sedentary closed habitat group significantly more likely to be infected than the reference sedentary open habitat group ( OR = 6 . 6 , 95% CI: 2 . 18–20 . 15 , p<0 . 001 ) ( Figure 2 ( C ) ) . Both sedentary and non-sedentary mixed habitat groups had an increased likelihood of being infected , but this was not significant in either group . Using Tukey contrasts as a method of the multiple comparison of means , the sedentary closed habitat group had a significantly higher prevalence of trypanosome infections than both the sedentary mixed and sedentary open groups ( p = 0 . 004 and p = 0 . 009 , respectively ) . The effect of blood meal preference group was also significant as a factor ( p = 0 . 018 ) . The high blood meal preference group had an increased risk of being infected ( p = 0 . 013 ) with an OR of 2 . 2 ( 95% CI: 1 . 18–3 . 99 ) compared to the reference low blood meal preference group . Interestingly , the likelihood of the medium blood meal preference group being infected was lower than the reference group ( OR = 0 . 9 , 95% CI: 0 . 37–2 . 01 , p = 0 . 722 ) , but this difference was not statistically significant . The prevalence detected by blood meal preference group is shown in Figure 2 ( D ) . There were no significant effects of age on the prevalence of trypanosomes detected in any of the datasets . Sex was found to be a significant factor ( p = 0 . 028 ) , with male animals having higher odds of being infected than females ( OR = 3 . 2 , 95% CI: 0 . 96–10 . 58 , p = 0 . 058 ) . However , this effect was confounded by species as only one female sample was collected from a species with a high prevalence of trypanosomes . When adjusted for species the effect was no longer significant ( p = 0 . 544 ) and the adjusted OR was lower ( OR = 1 . 5 , 95% CI: 0 . 38–6 . 15 , p = 0 . 554 ) . There were no significant effects of area on the prevalence of trypanosome infection and no spatial patterns were apparent in the data ( Figure 3 ) . No significant effects of the year of sampling were detected . Although month of sampling had a significant effect ( p = 0 . 028 ) , most of the samples from NLNP were collected in September and the species sampled had a lower prevalence . When the effects of month were adjusted for confounding by area the effect was no longer statistically significant ( p = 0 . 248 ) . Although sample collection method appeared to have a significant effect ( p = 0 . 002 ) , this was no longer the case when adjusted for either species ( p = 0 . 267 ) or area ( p = 0 . 432 ) . The effect of over-saturating Whatman FTA or Isocode cards with blood was not statistically significant when the whole dataset was considered . However , when the samples collected by hunters were considered alone the effect approached statistical significance ( p = 0 . 059 ) and samples that were classified as being over-saturated had a reduced likelihood of being detected as infected ( OR = 0 . 6 , 95% CI: 0 . 31–1 . 03 , p = 0 . 063 ) . When combined with the rest of the data , the effect became statistically insignificant ( p = 0 . 209 ) , but the odds of being detected as infected was still lower for over-saturated samples ( OR = 0 . 7 , 95% CI: 0 . 39–1 . 24 , p = 0 . 215 ) . The results of the univariable analysis of risk factors for overall infection with trypanosomes are summarised in Table 4 . A multivariable analysis was conducted using the taxonomy grouping of species . This grouping method was selected for the final analysis as it had the lowest residual deviance . However , despite the grouping of species , the nature of the data resulted in large standard errors so a reduced dataset ( 326 observations ) with all species with a sample size less than five or no positive samples removed was used . The final multivariable model included taxonomy grouping and over-saturation of sample cards , with area included as a confounding variable . No factors were significant , but the effect of over-saturation approached significance ( p = 0 . 054 ) with over-saturated cards less likely to be detected as infected ( OR = 0 . 5 , 95% CI: 0 . 28–1 . 02 , p = 0 . 058 ) . Infection rates were highest in Pantherinae ( OR = 2 . 0 , 95% CI: 0 . 53–7 . 27 ) , Bovinae ( OR = 1 . 6 , 95% CI: 0 . 57–4 . 59 ) and Reduncinae ( OR = 1 . 2 , 95% CI: 0 . 41–3 . 71 ) taxonomy groups . The overall cumulative prevalence of T . brucei s . l . in all species was 5 . 7% ( 95% CI: 3 . 71–8 . 42% ) . The prevalence detected using the individual species PCR for T . brucei s . l . was 5 . 3% ( 95% CI: 3 . 33–7 . 86% ) compared with 0 . 5% ( 95% CI: 0 . 06–1 . 72% ) using the multispecies PCR . Two T . b . rhodesiense infections were detected using the SRA-PCR giving a prevalence of 0 . 5% ( 95% CI: 0 . 06–1 . 72% ) . The positive samples came from a male adult bushbuck from Chifunda hunting block in Musalangu GMA and a male adult buffalo ( Syncerus caffer ) from the Nyamaluma area of Lower Lupande GMA . The proportion of all T . brucei s . l . infections that were identified as T . b . rhodesiense was therefore 0 . 08 , or 8 . 3% . However , the GPI-PLC gene was not detected in the majority of the T . brucei s . l . positive samples . Host species was again significant as a factor ( p = 0 . 042 ) and the bushbuck presented a significantly greater odds of being detected as infected ( OR = 7 . 1 , 95% CI: 1 . 7–29 . 33 , p = 0 . 007 ) . No other host species had a significantly greater likelihood of being detected as infected when compared with the reference warthog ( Table 5 ) . A bar chart of the prevalence detected in all species with at least one positive sample is presented in Figure 4 ( A ) . Oversaturation of Whatman FTA cards also had a significant effect on T . brucei s . l . prevalence both when the complete dataset was analysed ( p = 0 . 024 ) and when the samples collected by hunters were considered separately ( p = 0 . 010 ) . Over-saturated FTA cards were significantly less likely to be detected as positive with an OR of 0 . 4 ( 95% CI: 0 . 13–0 . 94 , p = 0 . 038 ) using the complete dataset and 0 . 3 ( 95% CI: 0 . 11–0 . 81 , p = 0 . 018 ) using the hunter dataset . Year also had a significant effect on the prevalence ( p = 0 . 015 ) , with samples collected in 2007 presenting a reduced likelihood of being detected as positive for T . brucei s . l . ( OR = 0 . 2 , 95% CI: 0 . 04–0 . 57 , p = 0 . 005 ) . The overall prevalence of T . congolense in all species was 6 . 0% ( 95% CI: 3 . 91–8 . 70% ) . Host species had a significant effect on the prevalence ( p = 0 . 001 ) with greater kudu the species most likely to be detected as infected ( OR = 8 . 7 , 95% CI: 2 . 24–33 . 58 , p = 0 . 002 ) , followed by lion ( OR = 5 . 2 , 95% CI: 1 . 11–24 . 31 , p = 0 . 036 ) . No other species had a significantly increased risk of infection compared with the reference warthog species . A summary of the prevalence detected and OR for each species is shown in Table 5 and a bar chart of the prevalence detected for each species with at least one positive sample is shown in Figure 4 ( B ) . No other factors had a significant effect on the T . congolense prevalence using the combined dataset . There was , however , a significantly lower likelihood of detecting T . congolense in the month of September ( OR = 0 . 2 , 95% CI: 0 . 05–0 . 64 , p = 0 . 008 ) . The T . vivax prevalence of 3 . 1% ( 95% CI: 1 . 67–5 . 26% ) was lower than that for the two other trypanosome species that were investigated in this study . Host species had a significant effect on this prevalence ( p = 0 . 002 ) and waterbuck was highly significant as a host with an OR of 55 . 0 ( 95% CI: 5 . 33–567 . 59 , p = <0 . 001 ) ( Table 5 ) . Although buffalo also had an increased likelihood of being detected as infected , the OR was not significant . No other factors had significant effects on the T . vivax prevalence . Figure 4 ( C ) shows a bar chart of the prevalence of T . vivax in all wild animal species with at least one positive sample .
The accurate diagnosis of trypanosomes in field surveys of wildlife populations has historically presented many challenges , in particular for T . brucei species . The protocol employed in this survey offered the advantage of an efficient method of sample collection and storage , combined with highly specific molecular techniques for diagnosis . The use of hunter kills as a source of surveillance material enabled a wide range of species to be sampled and increased the sample size obtainable from the resources available . Although the data generated was a convenience sample and is likely to be biased in terms of the sex and age distribution of the population sampled , this is a common problem with surveys of wildlife populations [19]–[21] and is difficult to overcome . Where resources allow , molecular techniques of diagnosis offer the advantage over more traditional techniques of improved diagnostic specificity and sensitivity . This survey , along with a sister-project in Tanzania [37] , represented the first use of the multispecies ITS-PCR [27] on field samples collected from free-ranging wildlife . A recent publication that used very similar protocols reported the specificity for T . brucei s . l . in a cattle population in Kenya to be 0 . 997 for the ITS-PCR and 0 . 998 for the TBR-PCR [38] . The sensitivities were not as high , however , with estimates of 0 . 640 and 0 . 760 respectively for the two techniques . The lower sensitivities achieved were attributed to the use of filter paper cards for DNA preservation and illustrate the main disadvantage of this technique of sample storage . The figures reported in this paper , therefore , although highly specific , are likely to underestimate the true prevalence of infection in wildlife . Only the data for T . congolense Forest , Kilifi and Savannah sub-species , T . brucei s . l . and T . vivax were used in the data analysis due to difficulties encountered in the accurate differentiation of bands at the sizes expected for T . simiae and T . simiae Tsavo . This limited the ability to detect mixed infections and the level reported ( 1 . 0% ) might be lower than expected . However , it is possible that the high level of trypanosome challenge experienced by wild hosts in this ecosytem encourages the formation of a cross-immunity , as has been postulated for lions , and this may reduce the prevalence of mixed infections [39] . Host species was consistently identified as the most significant risk factor for infection with trypanosomes throughout the univariable analysis and no other factors had a significant effect after adjustment for confounding . The taxonomy grouping of species ( p = 0 . 002 ) and habitat grouping ( p<0 . 001 ) were also highly significant with blood meal preference grouping ( p = 0 . 02 ) less so . When the residual deviances for each model were compared , the lowest value was obtained when no grouping was used at all ( 267 . 04 ) compared with the models containing the taxonomy grouping ( 304 . 03 ) , habitat grouping ( 306 . 20 ) and blood meal preference grouping ( 328 . 57 ) . As the residual deviance represents the unexplained deviance in the model , it is clear that much of the variation in infection rates occurs at the species level . However , it is also clear that complex interactions between parasite , host and vector determine the infection status of wildlife hosts . At the individual trypanosome species level , host species was again the most significant factor explaining the variation in infection rates . Oversaturation of sample cards had a significant effect on T . brucei s . l . prevalence only and this suggests that the TBR-PCR may be more sensitive to inhibition by haem products than the multispecies ITS-PCR . The potential temporal effects detected in this study were most likely induced by the study design and resulted from sampling more species of low prevalence in the NLNP survey in 2007 . It was difficult to investigate any potential confounding induced by the different sampling methods used as samples collected using the two methods were not collected from the same areas and species . This illustrates the difficulties with collecting data from wildlife suitable for a robust statistical analysis . Although no association between the prevalence of trypanosome infection and age was evident in this study , the age distribution of the data limited the ability to investigate this as a risk factor . Very few studies have previously investigated the effect of age on trypanosome prevalence , but of those that have , one reported that the prevalence in buffalo peaked at two and a half years [8] and another found no statistically significant difference between the prevalence in young and old animals [17] . However , a more recent study of 13 lion prides in the Greater Serengeti Ecosystem in Tanzania where the actual age was known to within an accuracy of one month , reported that the prevalence of T . brucei s . l . infections showed a distinct peak and decrease with increasing age [39] . Most infections with T . brucei s . l . were cleared between three and five years of age and no human infective T . b . rhodesiense parasites were detected in lions over six years old . It was postulated that frequent challenge and an exposure dependent cross-immunity following infections with more genetically diverse species such as T . congolense , led to partial protection sufficient to prevent animals from harbouring human infective T . b . rhodesiense . As most samples in the study presented here were collected from older animals , it is possible that the prevalence in lions and potentially other species has been underestimated . The results of this survey demonstrate the ability of trypanosomes to survive in a very wide variety of wildlife hosts . New identifications of T . b . rhodesiense in African buffalo and T . brucei s . l . in leopard ( Panthera pardus ) suggest that the reservoir community is even more diverse than previously thought . However , as illustrated by the species prevalence graphs , the majority of infections were concentrated in a smaller number of species . The majority of T . brucei s . l . infections were detected in four species , namely bushbuck , leopard , lion and waterbuck . Of these , the bushbuck was the only species to have a significantly greater likelihood of infection than warthog ( OR: 7 . 1 , 95% CI: 1 . 7–29 . 33 ) . The bushbuck has previously been identified as an important reservoir host for T . brucei s . l . in the Luangwa Valley [1] , [40] as well as in the Lambwe Valley in Kenya [3] , [41] . Although overall densities of bushbuck are not high , they are locally abundant within the dense woodland and thicket vegetation common near the Luangwa River and its tributaries [42] . The high proportion of blood meals taken from this species by Glossina pallidipes tsetse [34] , despite the relatively low overall population of bushbuck , suggests that there is a close ecological association between the two species . A transmission cycle involving this sedentary host and G . pallidipes is therefore likely to play an important role in maintaining foci of T . brucei in the Luangwa Valley , as has been proposed for the Lambwe Valley in Kenya [3] . The identification of T . brucei s . l . in samples from leopard in this study appears to be the first published record of infection in this species . Compared to many other protected areas in Africa , leopards are relatively common in the Luangwa Valley . It would appear that both lion and leopard are capable of supporting a moderate prevalence of T . brucei s . l . infection and there may be a secondary transmission cycle involving the carnivores . Interestingly it has been postulated that carnivores can become infected from their prey through abrasions in the oral mucosa and this has been demonstrated in artificial experiments [4] . Despite spending much of the day lying in dense thicket , these species also do not account for a high proportion of tsetse blood meals [34] . Trypanosome infections in these species may therefore be an example of bioaccumulation rather than vector transmitted disease , but no conclusions can be drawn on the route transmission from this study . However , considering their relatively low density , their contribution to trypanosome transmission is unlikely to be large whichever transmission route is involved . The precise contribution of the waterbuck to the transmission of T . brucei s . l . is still unclear , although the high prevalence detected in this study and others [1] , [7] suggests that they are highly susceptible to infection . Although they may be locally abundant overall densities are not high and they are rarely fed on by tsetse [34] , [42] . They have been reported to produce allomones that repel tsetse and reduce the likelihood of feeding once landed [43] . However , they occupy a niche environment on the fringes of thicket and woodland and are clearly very susceptible to infection with all three trypanosome species . It has been postulated that their high susceptibility to infection has resulted from the fact that they are rarely challenged by infected tsetse bites [35] , but the same can be said for many other species in which low infection rates are detected . The diagnosis of T . b . rhodesiense in a sample from an African buffalo is , as far as the authors are aware , the first identification in this species . Buffalo are abundant in many savannah ecosystems and are capable of acting as a reservoir host for many pathogens of cattle , most probably because of their close phylogenetic relationship to the latter [44] . They have previously been demonstrated to be susceptible to sub-clinical infections with T . brucei s . l . [6] , [8] so this finding is not surprising , but has important implications for the control of the disease . Buffalo are not sedentary animals and herds frequently move over large distances with the potential to disseminate infection to other host species . The finding of this parasite in Nyamaluma , not far from Mambwe and Msoro Districts where there have recently been large influxes of cattle and people , also raises concerns about the possibility of infection becoming established in the cattle population of the Luangwa Valley . In other parts of Africa , particularly Uganda , cattle have demonstrated to be effective maintenance hosts for T . b . rhodesiense [45] , [46] . Areas with increasing populations of cattle adjacent to wildlife areas have also been identified as being at risk from epidemics of trypanosomiasis [47] . The prevalence of T . brucei s . l . in buffalo was relatively low , a finding that is in keeping with the moderate level of blood meals coming from this species despite their relative abundance [34] , [42] . The only other positive identification of T . b . rhodesiense in this study was in a bushbuck in Musalangu GMA . This subspecies has previously been isolated from a bushbuck in the Luangwa Valley [23] as well as in the Lambwe Valley in Kenya [41] and its important role within the community of hosts for T . brucei s . l . has already been outlined . The overall prevalence of T . b . rhodesiense detected in the study was relatively low at 0 . 5% ( 95% CI: 0 . 06–1 . 72% ) suggesting that approximately 8% of all T . brucei s . l . identifications were T . b . rhodesiense . However , given that the majority of TBR-PCR positive samples did not test positive for PLC gene , it is likely to be an underestimate of the true prevalence . Even allowing for this , it is clear that the human infective subspecies is maintained at low levels by the reservoir community alongside a rich diversity of other trypanosome species . This might seem surprising given that wildlife hosts have long been regarded as the natural host for this parasite . It is in stark contrast to the ecological picture in Uganda where the human infective parasites circulate efficiently between cattle and man against a background of reduced biodiversity [48] . This raises the possibility that maintenance of biodiversity within the Luangwa Valley ecosystem has influenced the limited emergence of this parasite , although the data in this study are insufficient to prove this and many factors have been implicated in determining the patterns of parasite species richness [49] , [50] . As spillover from wildlife has often been implicated as a risk factor for human infection [39] , [51] , the possibility that maintaining biodiversity might , conversely , limit the risk of infection in some situations warrants further investigation . Of interest from a conservation perspective was the identification during the study of T . brucei s . l . in two black rhinoceros ( Diceros bicornis ) that had recently been re-introduced into the Luangwa Valley from a tsetse free area of South Africa ( and were therefore not included in the data analysis ) . Histopathology of the brain from one rhinoceros which had died revealed severe meningo-encephalitis that was considered to be consistent with a diagnosis of clinical trypanosomiasis . Laboratory analysis of blood samples provided positive identification of T . brucei s . l . using the TBR-PCR and the GPI-PLC gene was positively identified using the SRA-PCR . Interestingly , the rhinoceros samples both had very strong positive GPI-PLC bands in comparison to other T . brucei s . l . positive samples where the GPI-PLC band was often negative , a finding which is suggestive of a higher level of parasitaemia in this species . The only clinical signs observed in the rhinoceros were depression and poor condition , although it was not examined by a veterinary surgeon . Although the final cause of death may be attributed to trypanosomiasis , it is not clear if it was a primary or secondary problem . Trypanosomiasis , including infection with T . brucei s . l . , has been implicated previously in the post-translocation deaths of rhino [52]–[54] . The community of reservoir hosts for T . congolense would appear to be wider than that for the other trypanosome species , with members of the Bovidae family most frequently represented . Again , two separate transmission routes would appear to occur , one involving many of the ungulate species that are regularly fed on by tsetse and a second one involving the carnivores and possible oral transmission . Of the ungulates , a significant prevalence was detected in this study in greater kudu ( OR = 8 . 7 , 95% CI: 2 . 24–33 . 58 ) , with moderate levels of infection in bushbuck and warthog . Both greater kudu and bushbuck are preferred hosts for G . pallidipes [34] and are sedentary hosts living largely in thicket or dense woodland , which is the prime habitat for this species of tsetse . This contrasts with the situation regarding warthog , where a close ecological association with G . m . morsitans has been described [34] , [55] . A significant prevalence of infection was detected in lion in this study ( OR = 5 . 2 , 95% CI: 1 . 11–24 . 31 ) , but , as with T . brucei s . l , it is doubtful that they form an important component of the community of reservoir hosts in terms of onwards disease transmission . It is less straightforward to draw conclusions about the epidemiology of T . vivax infections in wildlife as the overall prevalence detected was much lower . Of all species sampled , waterbuck was the only species with a significant likelihood of infection ( OR = 55 . 0 , 95% CI: 5 . 33–567 . 59 ) . Although this is clearly a significant odds ratio , the precise contribution of waterbuck to the transmission of infection is unclear and the reasons for this are as discussed for T . brucei s . l . A moderate prevalence with T . vivax was also detected in the more abundant buffalo , with occasional infections in other ungulates . Previous surveys have suggested that bushbuck and greater kudu are also capable of supporting T . vivax infections [7] , [13] and agree with the high levels of infection detected in waterbuck [1] , [7] , [13] . Therefore , although the epidemiological picture is less clear for this species of trypanosome , it is likely that a transmission cycle involving the bovinae sub-family is the most important component of the reservoir . The Luangwa Valley ecosystem is unusual in modern day Africa due to the limited level of contact between domesticated livestock and wildlife . The results of the survey presented here along with historical surveys conducted in the valley [1] , [7] , [13] suggest that the epidemiology of trypanosomiasis has remained largely unchanged over the last century . This is in keeping with consistent land use patterns with an almost complete absence of livestock and only a modest change in the human population over the same time period . Infection rates in many species in this survey were comparable with previous surveys in the Luangwa Valley ( Table 6 ) . However , in recent years an influx of people and livestock into the Msoro and Mambwe Districts of central Luangwa Valley has led to the development of a new wildlife / livestock / human interface . An investigation into the prevalence of trypanosomiasis in domestic livestock at the site of this new interface in Msoro District , revealed infection rates of 33 . 3% in cattle , 20 . 9% in pigs , 27 . 6% in sheep and 10 . 2% in goats [56] . Although the laboratory protocol differed slightly from that used in this study , the same multispecies ITS-PCR was used . This is a much higher prevalence than that found in the surrounding wildlife population and represents a significant departure from the historical situation , with ramifications both for trypanosomiasis transmission and that of other infectious diseases . New interfaces have been identified as an important factor in disease transmission [19] and areas surrounding these interfaces have been identified as being at risk of epidemics of bovine trypanosomiasis [47] . Prevalence data produced from trypanosome surveys in neighbouring countries are not directly comparable due to the different diagnostic techniques used , but in general the prevalence recorded has been lower than that in the Luangwa Valley [57] , [58] . Trypanosoma parasites circulate within a wide and diverse host community in this bio-diverse ecosystem . With the identification of the African buffalo and the leopard as new host species for T . b . rhodesiense and T . brucei s . l . respectively , it is clear that the reservoir community is wider than previously demonstrated . However , although the host range is very wide , the majority of infections are concentrated in a smaller number of species with a clear pattern of species forming the bulk of the reservoir community for each trypanosome species . Host species was the only consistent risk factor for infection identified in this study and , although many factors may interact to influence the trypanosome prevalence in wildlife , most of the variation in infection rates occurs at the species level . The epidemiology of trypanosomiasis in the Luangwa Valley has remained remarkably stable since the first survey in 1913 , in keeping with consistent land use patterns despite some changes in the human population over that period . The recent influx of cattle and people from the plateau regions of Eastern Province represents a significant diversion from these land use patterns and will almost certainly result in changes in the epidemiology of trypanosomiasis in the Luangwa Valley , with cattle becoming increasingly important members of the reservoir community .
|
Animal and human trypanosomiasis are constraints to both animal and human health in Sub-Saharan Africa , but there is little recent evidence as to how these parasites circulate in natural hosts in natural ecosystems . A cross-sectional survey of trypanosome prevalence in 418 wildlife hosts was conducted in the Luangwa Valley , Zambia , from 2005 to 2007 . The overall prevalence in all species was 13 . 9% . Infection was significantly more likely to be detected in waterbuck , lion , greater kudu and bushbuck , with a clear pattern apparent of the most important hosts for each trypanosome species . Human infective Trypanosoma brucei rhodesiense parasites were identified for the first time in African buffalo and T . brucei s . l . in leopard . Variation in infection is demonstrated at species level rather than at family or sub-family level . A number of significant risk factors are shown to interact to influence infection rates in wildlife including taxonomy , habitat and blood meal preference . Trypanosoma parasites circulate within a wide and diverse host community in this bio-diverse ecosystem . Consistent land use patterns over the last century have resulted in epidemiological stability , but this may be threatened by the recent influx of people and domesticated livestock into the mid-Luangwa Valley .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"biology",
"veterinary",
"science"
] |
2011
|
Characterisation of the Wildlife Reservoir Community for Human and Animal Trypanosomiasis in the Luangwa Valley, Zambia
|
Viral entry into the host cell is the first step towards successful infection . Viral entry starts with virion attachment , and binding to receptors . Receptor binding viruses either directly release their genome into the cell , or enter cells through endocytosis . For DNA viruses and a few RNA viruses , the endocytosed viruses will transport from cytoplasm into the nucleus followed by gene expression . Receptors on the cell membrane play a crucial role in viral infection . Although several attachment factors , or candidate receptors , for the infection of white spot syndrome virus ( WSSV ) were identified in shrimp , the authentic entry receptors for WSSV infection and the intracellular signaling triggering by interaction of WSSV with receptors remain unclear . In the present study , a receptor for WSSV infection in kuruma shrimp , Marsupenaeus japonicus , was identified . It is a member of the immunoglobulin superfamily ( IgSF ) with a transmembrane region , and is similar to the vertebrate polymeric immunoglobulin receptor ( pIgR ) ; therefore , it was designated as a pIgR-like protein ( MjpIgR for short ) . MjpIgR was detected in all tissues tested , and its expression was significantly induced by WSSV infection at the mRNA and protein levels . Knockdown of MjpIgR , and blocking MjpIgR with its antibody inhibited WSSV infection in shrimp and overexpression of MjpIgR facilitated the invasion of WSSV . Further analyses indicated that MjpIgR could independently render non-permissive cells susceptible to WSSV infection . The extracellular domain of MjpIgR interacts with envelope protein VP24 of WSSV and the intracellular domain interacts with calmodulin ( MjCaM ) . MjpIgR was oligomerized and internalized following WSSV infection and the internalization was associated with endocytosis of WSSV . The viral internalization facilitating ability of MjpIgR could be blocked using chlorpromazine , an inhibitor of clathrin dependent endocytosis . Knockdown of Mjclathrin and its adaptor protein AP-2 also inhibited WSSV internalization . All the results indicated that MjpIgR-mediated WSSV endocytosis was clathrin dependent . The results suggested that MjpIgR is a WSSV receptor , and that WSSV enters shrimp cells via the pIgR-CaM-Clathrin endocytosis pathway .
Viral infection process is a very complex interaction and consists of multiple steps [1] . It starts with virion attachment to the host cell membrane , followed by specific binding to receptors . Viral receptor engagement allows viruses either to release their genome into the cell directly at the plasma membrane , or to enter cells through endocytosis . Endocytosis is highly complex and dynamic , and involves recycling , trafficking , maturation and fusion of endocytic vesicles [2] . For DNA viruses and a few RNA viruses , the endocytosed viruses will traffic from cytoplasm into the nucleus for gene expression [1 , 3] . To enter the cytoplasm of host cells , viruses can adopt two main strategies , receptor-mediated endocytosis and endocytosis-independent receptor-mediated entry [4] . Viruses can use specific cell membrane receptors to enter and infect host cells , which determines the host specificity , tissue tropism and cell type a virus can infect [5 , 6] . Several classes of molecules are utilized as receptors by different viruses , such as sialic acid moieties , integrins , and some immunoglobulin-like superfamily ( IgSF ) proteins in vertebrates [7] . Some viruses use various types of receptors to attach to and enter into cells . For example , the receptors for hepatitis C virus ( HCV ) infection include heparin sulfate [8] , low-density lipoprotein receptor [9] , transferrin receptor 1 [10] , B type scavenger receptor [11] and occludin [12] in mammals . Cell-adhesion molecules can be divided into four protein families: Integrins , selectins , IgSF , and cadherins [13] . They are usually expressed on the cell surface and have diverse functions . Among them , the IgSF is a large protein superfamily of cell surface and soluble proteins that are involved in recognition , binding , adhesion , and immunity [14] . IgSF members have diverged in sequence and function; however , the definitive characteristic of the members is the presence of one or more immunoglobulin ( Ig ) -like domains [15] . The polymeric immunoglobulin receptor identified in vertebrates is a member of the IgSF . As a type I transmembrane glycoprotein , polymeric immunoglobulin receptor ( pIgR ) is widely expressed in epithelial cells [16] . The pIgR protein in different species shares four similar components: An intracellular region , a transmembrane region , a cleavage region , and an extracellular ligand-binding region ( secretory component , SC ) [17] . The Ig domains are located in the extracellular region; therefore , the N-terminal ligand-binding domain plays central roles in binding polymeric immunoglobulins ( pIg ) . PIgR acts as the receptor for pIg and transports pIgA/pIgM across intestinal epithelial cells ( IECs ) in vertebrates [18 , 19] . In addition , pIgR and SC-mediated protection prevent the invasion of pathogenic microorganism at mucosal surfaces [20 , 21] . Interestingly , some studies on pIgR have found that certain microorganisms , such as Streptococcus pneumoniae , hijack pIgR to their own benefit during the invasion of host cells [22 , 23 , 24] . Diverse groups of viruses bind to IgSF proteins at the cell surface to mediate cell entry [7] . For example , the cell surface CD4 glycoprotein carries four functional domains , and three of them resemble Ig variable regions , CD4 was confirmed as an important receptor of Human Immunodeficiency Virus ( HIV ) [25] . It is now known that entry of HIV-1 into lymphoid cells requires the cooperation of three host-cell proteins , the primary receptor CD4 , a chemokine co-receptor ( CCR5 or CXCR4 ) and an oxidoreductase protein disulfide isomerase ( PDI ) and the viral envelope glycoproteins gp120 and gp41 [26 , 27 , 28 , 29] . Viral gp120 attaches the virus to the cell by binding to host CD4 . It was found that CD4 also has a binding site for PDI and forms a PDI-CD4-gp120 complex [27] . Another example is where the Adeno-associated virus receptor ( AAVR ) serves as an important receptor for the invasion of Adeno-associated virus ( AAV ) . AAVR with five Ig-like domains , also known as polycystic kidney disease ( PKD ) domains , was captured by AAV during breaking of the defensive system of different cell lines [30] . White Spot Syndrome Virus ( WSSV ) is one of the most virulent pathogens in shrimp farming [31] . Studies on viral candidate receptors involved in WSSV infection can provide useful information for viral disease control . There were several reports about WSSV attachment proteins or candidate receptors in shrimp , such as Penaeus monodon Rab7 binding to WSSV envelope protein VP28 , which is beneficial for WSSV infection [32] , and a chitin-binding protein ( CBP ) in P . monodon interacts with 11 WSSV envelope proteins , which can reduce and delay mortality upon WSSV challenge in the neutralization assay [33 , 34] . Beta-integrin interacts with VP187 , which can mediate WSSV infection [35] . Glucose transporter 1 interacts with VP53A , which is related with entry of WSSV into host cells [36] . Laminin binding to VP31 mediates WSSV infection [37] and a soluble C-type lectin ( MjsvCL ) interacts with VP28 and calreticulin , which facilitates WSSV infection in shrimp [38] . Other studies found that some proteins interact with WSSV proteins to resist WSSV infection . F1-ATP synthase beta subunit of Litopenaeus vannamei binds to WSSV and attenuates WSSV infection [39] . Scavenger receptor C of Marsupenaeus japonicus interacts with VP19 of WSSV and β-arrestin mediates clathrin dependent endocytosis of WSSV , which can restrict virus proliferation [40] . These reports advanced our understanding of WSSV entry receptors . Viral receptors play important roles in the initial step of viral infection , and are ideal targets for antiviral intervention . Usually , interactions of virus with the receptors can elicit two types of signaling , viral particle conformational changes , and intracellular signals triggering specific cellular responses . In many cases , virus can usurp the signaling systems of host cells to create a favorable environment for their own amplification [41] . Among the reported WSSV candidate receptors that are beneficial for WSSV infection , only the β-integrin is an authentic transmembrane protein; therefore , further study of WSSV entry receptors is required . On the other hand , the signaling induced by WSSV interactions with receptors remains unknown . In the present study , we identified an IgSF cell adhesion molecule that was similar to poly immunoglobin receptor ( pIgR ) of vertebrates from Marsupenaeus japonicus , and designed it as Marsupenaeus japonicus pIgR like protein ( MjpIgR ) . MjpIgR is a type I transmembrane protein , and was significantly upregulated in shrimp challenged with WSSV . Knockdown of MjpIgR in shrimp decreased the numbers of WSSV . Meanwhile , overexpression of MjpIgR increased WSSV infection . The intracellular signaling triggering by interaction of WSSV with MjpIgR was investigated . MjpIgR interacted with MjCaM and the viral internalization was clathrin dependent . Our studies revealed that pIgR is a receptor for the invasion of WSSV into shrimp .
In our transcriptome sequence analysis , we found a pIgR-like molecule that was upregulated by 4 to 6 folds in shrimp challenged with WSSV . Therefore , we chose this molecule for further study . The full-length MjpIgR cDNA is 1686 bp and encodes a protein of 562 amino acid residues ( GenBank Accession no . MH051890 ) . MjpIgR contains a signal peptide; an extracellular domain , including an IG domain and two IG-like domains; a transmembrane region; and an intracellular region ( S1 and S2 Figs ) . MjpIgR is clustered with the vertebrate pIgR group ( S3 Fig ) . MjpIgR mRNA is expressed in hemocytes and in all other tested organs including heart , hepatopancreas , gills , stomach , and intestine analyzed by RT-PCR ( Fig 1A ) . The specificity of the MjpIgR ORF primers was confirmed by using other samples from Litopenaeus vannamei and Procambarus clakii , which shows no any band by PCR amplification ( S4 Fig ) . The extracellular SC of MjpIgR protein was recombinantly expressed in E . coli ( Fig 1B ) and anti-MjpIgR polyclonal antibodies were prepared ( Fig 1C ) . The MjpIgR protein was also widely distributed in hemocytes and other organs , as revealed via western blotting analysis ( Fig 1D ) . All the results indicated that MjpIgR is ubiquitously expressed in the shrimp . We performed a time course expression analysis of MjpIgR transcription and translation in hemocytes and intestine . The qPCR results showed that MjpIgR transcription was upregulated from 6 to 24 h in hemocytes and intestine of shrimp after WSSV challenge ( Fig 1E and 1F ) . The MjpIgR protein level was also upregulated similarly to mRNA level ( Fig 1G and 1H ) . These results suggested that MjpIgR is involved in WSSV infection and its increased expression prompted us to explore the detailed functions of MjpIgR in shrimp immunity . To explore the MjpIgR functions in WSSV infection , RNA interference , antibody blocking assays , and mRNA overexpression of MjpIgR were performed . WSSV proliferation in shrimp was analyzed via qPCR ( by testing vp28 expression level and WSSV copies ) and western blotting ( using VP24 or VP26 as indicators ) . The dsRNA and mRNA of MjpIgR were generated ( Fig 2A ) . Twenty-four hours after the injection of dsMjpIgR in shrimp , MjpIgR was observed to be knocked down at the mRNA and protein levels ( Fig 2B and 2C ) . The shrimp was then injected with WSSV . The WSSV levels in hemocytes and intestine of the dsMjpIgR-injection group were significantly reduced compared with those injected with dsGFP at 24 h post injection ( Fig 2D ) . Meanwhile , the number of copies of WSSV decreased significantly in the intestine of MjpIgR-knockdown shrimp ( Fig 2E ) . The WSSV protein level detected by anti-VP24 antibodies also decreased in hemocytes and intestine in the dsMjpIgR injection group compared with that in the control group ( Fig 2F ) . The survival rate of shrimp was also analyzed after RNAi of MjpIgR in shrimp followed WSSV injection . The results showed that the dsMjpIgR injection group had a much higher survival rate compared with that of the dsGFP group ( Fig 2G ) . In addition , the antibody blocking assay showed vp28 expression was decreased in the anti-MjpIgR injected shrimp , and suggested that the anti-MjpIgR antibodies blocked the binding site on the membrane in hemocytes and intestine ( Fig 2H ) . Taken together , these results suggested that MjpIgR promoted WSSV proliferation in shrimp . To further explore its function , overexpression of MjpIgR was performed by MjpIgR mRNA injection using Trx-His tag mRNA as a control . The MjpIgR protein was successfully expressed in hemocytes and intestine of the MjpIgR-overexpression group at 24 h post-mRNA injection ( Fig 2I ) . The shrimp were then challenged with WSSV . WSSV proliferation in the MjpIgR-overexpression group increased dramatically compared with that in the Trx-His tag mRNA groups ( Fig 2J ) . Similar results were obtained in the intestine by testing the number of WSSV copies ( Fig 2K ) . The protein levels of WSSV , as detected by the anti-VP26 antibody , were increased in hemocytes and intestine ( Fig 2L ) . In general , these data indicated that MjpIgR promoted the proliferation of WSSV in shrimp . To analyze the possible mechanism of MjpIgR in WSSV proliferation , MjpIgR oligomerization and internalization were detected . The oligomerization of recombinant MjpIgR ( rMjpIgR ) was first analyzed using native PAGE , and the result showed that rMjpIgR formed different oligomers in vitro ( Fig 3A ) . Further studies showed that the native MjpIgR formed a tetramer , determined by the molecular mass after WSSV challenge in vivo ( Fig 3B ) . We performed immunocytochemistry to detect the subcellular localization of MjpIgR using anti-MjpIgR antibodies . Under normal conditions , MjpIgR was mainly located on the cell membrane ( Fig 3C top panels ) . After WSSV challenge , MjpIgR gradually moved from surface to the cytoplasm as challenge time increased from 15 to 60 min ( Fig 3C ) . These results suggested that the internalization of MjpIgR might be associated with WSSV endocytosis . We further analyzed MjpIgR in the membrane and cytoplasm of hemocytes using western blot . The results showed that the MjpIgR level in membrane of hemocytes decreased slightly with increasing WSSV challenge time; however , its levels showed no significant differences between time points ( Fig 3D left panels ) . The MjpIgR level increased significantly in the cytoplasm of hemocytes after WSSV infection ( Fig 3D right panels ) . These results suggested that the MjpIgR was internalized from the membrane into the cytoplasm and this internalization might be related to WSSV endocytosis . To determine whether the internalization of MjpIgR is required for endocytosis of WSSV , an immunocytochemical assay was performed to detect the co-localization of MjpIgR with WSSV . The co-localization of MjpIgR with Dil-labeled WSSV was observed at 15 min post WSSV injection ( Fig 4A ) and the co-localization rate increased from 15 to 60 min , and the WSSV moved to perinuclear location at 60 min ( Fig 4B ) . The results suggested that MjpIgR might be a receptor for WSSV that is involved in endocytosis of WSSV in shrimp . To further confirm the results , flow cytometry was performed after RNA interference of MjpIgR . The results showed that the internalization rate of WSSV particles decreased in hemocytes after RNAi of MjpIgR ( Fig 4C and 4D ) . These results suggested that endocytosis of WSSV required the internalization of MjpIgR in shrimp hemocytes and that MjpIgR might be a receptor for WSSV . To address whether MjpIgR could independently facilitate WSSV entry , we assessed the level of WSSV entry in human HEK 293T cells upon MjpIgR overexpression . The plasmids pcDNA3 . 1 ( - ) -pIgR and pcDNA3 . 1 ( - ) -pIgR-ΔIG1 were constructed to express MjpIgR and pIgR-ΔIG1 ( containing a truncating mutation of pIgR ) in the non-permissive cells ( human HEK 293T cells ) ( Fig 5A ) . Then HEK 293T cells were transfected with empty vector , pcDNA3 . 1 ( - ) -pIgR or pcDNA3 . 1 ( - ) -pIgR-ΔIG1 , respectively . The cells were infected with WSSV for 1 hour or remained uninfected . After WSSV infection , the DNA of the cells was isolated and subjected to qPCR assay to detect the WSSV DNA . The expression of pIgR and pIgR-ΔIG1 were detected by western blot assays after transfection . The results showed that the pIgR and pIgR-ΔIG1 were successfully expressed on the cells ( Fig 5B ) . The viral DNA could be detected in the pcDNA3 . 1 ( - ) -pIgR transfected HEK 293T upon MjpIgR overexpression , but not in pcDNA3 . 1 ( - ) -pIgR-ΔIG1 transfected HEK 293T and empty vector transfected cells ( Fig 5C ) . The results suggest that MjpIgR serves as a receptor for WSSV entry and render the non-permissive cells ( HEK 293T ) susceptible to WSSV infection . To confirm whether MjpIgR is the entry receptor for WSSV , a binding assay was performed . We first recombinantly expressed the extracellular domains of MjpIgR ( MjpIgR-SC ) and then detected the interaction of MjpIgR-SC with the envelope proteins of WSSV using in vitro GST- and His-pulldown assays . VP19 , VP24 , VP26 , and VP28 of WSSV were used for the analysis . The results showed that MjpIgR-SC interacted with VP24 ( Fig 6A ) , but had no interaction with VP19 , VP26 , or VP28 ( Fig 6B , 6C and 6D ) . To identify which of the extracellular Ig domains of MjpIgR plays a central role in the interaction , the three Ig domains were recombinantly expressed and purified from E . coli , separately ( Fig 6E ) . The binding ability of MjpIgR-IG1 , MjpIgR-IG2 , and MjpIgR-IG3 to WSSV particles was analyzed using an ELISA binding assay . The results showed that all the three Ig domains bound to WSSV ( Fig 6F ) . We further analyzed the interaction of different Ig domains with VP24 using pulldown assays and results indicated that all three Ig domains could interact with VP24 ( Fig 6G , 6H and 6I ) . However , the truncating mutation of Ig domain could not interact with VP24 ( Fig 6J ) . Taken together , the above results suggested that MjpIgR could interact with WSSV through VP24 as a cellular receptor of WSSV . The intracellular domain of human pIgR could interact with calmodulin , and calmodulin could interact with the clathrin heavy chain [17 , 42 , 43] . A calmodulin cDNA was cloned from M . japonicus , and named as MjCalmodulin , ( MjCaM , GenBank Accession no . MH238441 ) . MjCaM mRNA was distributed in all tissues tested ( Fig 7A ) and was upregulated by WSSV infection in hemocytes and intestine ( Fig 7B and 7C ) . RNA interference was performed ( Fig 7D ) to explore the roles of MjCaM in shrimp infected by WSSV . The vp28 expression levels in hemocytes and intestine decreased in the dsMjCaM-injection group compared with that in the dsGFP-injection group ( Fig 7E ) . The shrimp in the dsMjCaM group had a relatively higher survival rate compared with that of the controls ( Fig 7F ) . The results indicated that MjCaM promotes WSSV infection . To analyze the possible interaction of MjpIgR with MjCaM , the intracellular domain of MjpIgR ( MjpIgR-In ) and MjCaM were expressed in E . coli ( Fig 7G and 7H ) . The interaction between MjpIgR-In and MjCaM was analyzed using pull-down assays ( Fig 7I ) . We found that MjCaM could bind to MjpIgR-In in vitro . A calmodulin antagonist , W-13 , was also used for MjCaM functional analysis in WSSV-infected shrimp . The results showed that in hemocytes and intestine , the vp28 levels decreased in W-13 injected shrimp in concentration dependent manner ( Fig 7J and 7K ) . The results suggested that MjCaM facilitated WSSV proliferation and might be involved in the regulation of MjpIgR internalization via their interaction . Viruses can hijack different cellular endocytic pathways for their internalization; among which , clathrin-mediated endocytosis is commonly used . Previously , we identified clathrin in the shrimp [40] . To determine whether the MjpIgR-Calmodulin-mediated endocytosis of WSSV was clathrin-dependent , the dose-dependent blocking effect of virus infection by chlorpromazine ( CPZ ) was first determined . The results showed that CPZ caused a concentration-dependent decrease in the vp28 expression level ( Fig 8A and 8B ) . After RNAi of Mjclathrin ( Fig 8C ) , the vp28 expression level also declined significantly ( Fig 8D ) . To confirm whether clathrin-mediated endocytosis was associated with MjpIgR , the mRNA of MjpIgR was overexpressed , and then CPZ was injected into the overexpression group after WSSV infection , and vp28 expression was detected . The results showed that the ability of MjpIgR to promote vp28 expression was blocked by CPZ injection ( Fig 8E ) . The number of WSSV copies in the intestine also decreased compared with that in the control group ( Fig 8F ) . The same results were obtained using western blotting analysis of WSSV VP26 levels ( Fig 8G ) . To further confirm that the endocytosis of WSSV via MjpIgR was clathrin-dependent , the co-localization between clathrin and WSSV particles was detected . In the dsMjpIgR group , co-localization of clathrin and WSSV was reduced ( Fig 8H and 8I ) . Taken together , the results suggested that WSSV enters shrimp cells via pIgR-CaM-clathrin-mediated endocytosis . Clathrin-based endocytic pathways involve a variety of adaptor proteins . The adaptor protein complex AP-2 has been considered one of the core components of the clathrin-based endocytic machinery [44 , 45 , 46] . We also identified complex AP-2 in shrimp , including AP-2α , β , μ and σ in the shrimp . To confirm above result about WSSV entering shrimp cells via clathrin-mediated endocytosis , the AP-2α was knockdown by RNAi ( Fig 9A ) , and WSSV replication and colocalization of MjpIgR with WSSV were detected . The results showed that the WSSV replication declined ( Fig 9B ) and the colocalization decreased ( Fig 9C and 9D ) upon down regulation of endocytic pathway , suggesting that pIgR-CaM-clathrin-mediated endocytosis associated with the classical adaptor protein complex AP-2 in shrimp .
In the present study , we identified a key receptor , MjpIgR , for WSSV entry and infection . The extracellular domain of MjpIgR could interact with WSSV envelope protein VP24 and intercellular domain interacted with MjCaM . MjCaM recruited Mjclathrin , and AP-2 adaptor complex also associated with the viral entry . Therefore , WSSV entered host cells via the pIgR-CaM-clathrin endocytotic pathway . The MjpIgR sequence obtained from M . japonicus possesses three immunoglobulin domains and is a member of the IgSF . As cell adhesion molecules , IgSF is a large protein superfamily of cell surface or soluble proteins that are involved in the recognition , binding , or adhesion processes of cells [47] . By BLAST analysis , the IgSF member from M . japonicus was observed to be similar with fasciclin molecules , especially fasciclin III . However , by domain architecture comparison ( S1 Fig ) , we found that Fasciclin I contains Fas 1 ( Fasciclin-like ) domains , Fasciclin II has Ig and FN3 ( Fibronectin type 3 ) domains , and Fasciclin III possesses Ig or Ig-like domains , in addition to a transmembrane motif . Compared with polymeric immunoglobulin receptor ( pIgR ) ( S2 Fig ) , the domain architecture of the IgSF member from M . japonicus is quite similar to vertebrate pIgRs . The phylogenetic analysis also showed the similarity of the IgSF member from M . japonicus with pIgR from vertebrates ( S3 Fig ) . Therefore , we designated the IgSF from kuruma shrimp as a pIgR-like protein ( MjpIgR ) . In the present study , as a receptor for WSSV , MjpIgR carrying WSSV particles enters hemocytes and induces a systemic infection in shrimp . Most of the IgSF members , including pIgR , are type I transmembrane proteins , which comprise an extracellular domain ( containing one or more Ig-like domains ) , a single transmembrane domain , and a cytoplasmic region [48] . These IgSF proteins can mediate adhesion through their N-terminal Ig-like domains , which usually bind other Ig-like domains of the same structure on the cell surface , or interact with other molecules , such as integrins and carbohydrates [49] . This suggested that the IgSF molecules could form homopolymers . In our study , we found that MjpIgR formed tetramer in vivo and interacts with VP24 of WSSV . Several classes of molecules are exploited as receptors by diverse groups of viruses , including sialic acid moieties [50] and integrins [51 , 52] . In particular , many IgSF proteins , such as pIgRs , have been identified as viral receptors [7 , 53] , such as the HIV receptor ( T-cell surface glycoprotein CD4 ) [25] , main rhinovirus receptors ( intracellular adhesion molecule-1 ) , and poliovirus receptor and adeno-associated virus receptors [30] . In our study , we found that MjpIgR interacted with VP24 of WSSV , and was used by WSSV as a receptor for its entry into cells . As one of the key processes of infection , DNA virus entry into host cells requires distinct cellular processes , including attachment to receptors; signaling; movement of the virus on the cell surface; endocytic uptake and trafficking; and uncoating of the genome , followed by replication , and , finally , particle assembly and release [54] . Usually , virus entry starts with binding to attachment factors , followed by association with receptors . The attachment factors merely bind the viruses and thus help to concentrate the viruses on the cell surface . The virus receptors can trigger changes in the virus , induce cellular signaling , promote endocytosis , and accompany the virus into the cell . However , the differentiation of attachment factors and receptors is often difficult in practice because both of them contribute to effective infection . Many viruses have evolved multi-step attachment processes , and a requirement for more than one receptor molecule is not uncommon . An extreme example is hepatitis C virus ( HCV ) , which requires more than ten molecules for cell entry [7] . For WSSV infection , Verbruggen et al . ( 2016 ) summarized the possible receptors or receptor complexes for WSSV , which include Chitin-binding protein , glucose transporter 1 , integrin , calreticulin , and C-type lectins ( such as MjscCL , MjLecA-C ) [55] . However , among the reported WSSV receptors , few molecules are genuine transmembrane proteins . We inferred that certain soluble molecules of the “receptors” , such as C-type lectin ( MjsvCL , MjLecA-C ) , Rab proteins , and Chitin-binding proteins , might be the attachment factors for WSSV , and that membrane proteins such as integrins [35] , and scavenger receptors [40] were the receptors for WSSV . In the present study , our results might hint that MjpIgR is not the only receptor involved in viral entry . As shown in Fig 2 , the difference of survival rate of MjpIgR-silenced shrimp and the control group although shows significant , a moderate improvement in survival of the two groups is observed ( Fig 2G ) . This might suggest that there are other receptors and pathways may function in WSSV adhesion/entry processes . To answer the questions , we knocked down the expression of β-Integrin ( a previously reported WSSV receptor ) in shrimp , WSSV replication and survival rate were analyzed ( S5 Fig ) . The results showed that after knockdown of β-Integrin ( S5A Fig ) , WSSV replication declined ( S5B Fig ) and survival rate of the shrimp was higher than that of control group ( S5C Fig ) . Comparing the results of MjpIgR ( Fig 2G ) and β-Integrin knockdown ( S5C Fig ) experiments , a similar moderate survival improvement was observed . These results suggested that like other viruses , WSSV has evolved multi-step attachment processes , and a requirement for more than one receptor in its infection . To date , however , no IgSF member has been reported to be a WSSV receptor . Therefore , this is the first report of a WSSV IgSF receptor . Receptors play a crucial role in determining the cell specificity and tissue tropism of viruses . WSSV exhibits a much broader cell tropism and can infect most cell types from organs of ectodermal and mesodermal origin , including those of the epidermis , gills , foregut , hindgut , lymphoid organ , muscle , heart , and gonads [56] . As a transmembrane receptor , MjpIgR was ubiquitously distributed in shrimp . The wide distribution of MjpIgR corresponded with WSSV’s broad cell tropism in shrimp . To ascertain whether MjpIgR is the WSSV receptor , we detected the WSSV entry in non-permissive cells ( HEK 293T ) with MjpIgR overexpression . The result showed that MjpIgR can independently render non-permissive cells ( HEK 293T ) susceptible to WSSV infection , and suggesting that MjpIgR is one of the receptors of WSSV infection ( Fig 5B and 5C ) . After binding to receptors on the cell surface , the enveloped virus can either penetrate the membrane directly by lipid fusion and membrane perforation , or enter the host cell by endocytosis [41] . How does the endocytosis take place ? The extracellular signal should be transferred to the cell cytoplasm by the receptor . The C-terminal intracellular domains of IgSF members often interact with cytoskeletal or adaptor proteins . This interaction can lead to the extracellular interaction signal being transmitted to the inside of the cells , which enables IgSF proteins to function in a wide range of biological processes [47] . In the present study , we identified that the intracellular domain of MjpIgR interacts with calmodulin ( MjCaM ) . Several studies have reported that calmodulin could interact with clathrin [42 , 57 , 58] . In the immunocytochemical analysis , the membrane MjpIgR moved to the cytoplasm . In addition , MjpIgR and clathrin colocalized with WSSV in the cells . This suggested that the endocytosis of WSSV was pIgR-calmodulin-clathrin dependent . The AP-2 adaptor complex is a multimeric protein that has been considered one of the core components of clathrin-mediated endocytosis . We also knocked down the AP-2α , a large subunit of the complex , and found that WSSV replication and co-localization of MjpIgR with VP28 were decreased ( Fig 9 ) . The results suggested that AP-2 also associated with clathrin-mediated endocytosis in shrimp . In conclusion , WSSV enters host cells by attachment to the primary receptor , MjpIgR , on the cell membrane , a process that might require other attachment factors or coreceptors . The binding WSSV with the receptor induces oligomerization of the receptor to tetramers and the signal is transferred to the cell cytoplasm , resulting in the intracellular domain of MjpIgR interacting with calmodulin . This further induces the interaction of calmodulin with clathrin , finally resulting in endocytosis of WSSV into the host cells ( Fig 10 ) . The trafficking , penetration , and genome uncoating of the incoming WSSV in the host cell require further study .
Healthy M . japonicus ( 9 g to 12 g each ) were purchased from a seafood market in Jinan City , Shandong province , China . The shrimp were acclimated for 48 h in an aerated aquarium with artificial seawater at about 24 °C . The salinity of the seawater was maintained between 24‰ ( w/v ) to 26‰ . Animals were randomly selected for the following experiments . The full-length sequence of MjpIgR was obtained through transcriptome sequencing using different tissues from infected shrimps . We used the NCBI database to determine the sequence homology ( http://blast . ncbi . nlm . nih . gov/Blast . cgi ) . The amino acid sequence , theoretical molecular weight , and isoelectric point of MjpIgR were analyzed using the online server ( http://web . expasy . org/translate/ ) . A domain prediction tool ( SMART: http://smart . embl-heidelberg . de/ ) was used to analysis the protein domain architecture . The WSSV inoculum was extracted based on the previously described method and the quantitative real-time PCR ( qPCR ) was used for viral quantification [59] . Each shrimp was injected with 50 μl of WSSV virions ( 1 × 105 ) from the viral infection . The same volume of sterile phosphate-buffered saline ( PBS ) ( 140 mM NaCl , 2 . 7 mM KCl , 10 mM Na2HPO4 , 1 . 8 mM KH2PO4 , pH 7 . 4 ) was injected into the control groups . Hemocytes were extracted from shrimp using a sterile syringe with anticoagulant buffer ( 450 mM NaCl , 10 mM KCl , 10 mM EDTA , 100 mM HEPES , pH 7 . 45 ) and then the hemolymph was discarded after centrifugation at 800 × g for 6 min at 4 °C , while the other tissues were dissected with scissors and forceps on ice for RNA or protein extraction . Total RNA was isolated from hemocytes and different organs ( heart , hepatopancreas , gills , stomach , and intestine ) of shrimp using the TRIpure Reagent ( Bioteke , Beijing , China ) . First-strand cDNAs were synthetized using a cDNA Synthesis Kit ( M-MLV version; Takara , Dalian , China ) . Genomic DNA was extracted using a genomic DNA Extraction Kit ( Toyobo , Osaka , Japan ) . Protein samples from different organs and hemocytes were homogenized separately in radio-immunoprecipitation assay ( RIPA ) buffer ( 50 mM Tris-HCl , 150 mM NaCl , 0 . 1% SDS , 0 . 5% Nonidet P-40 , 1 mM EDTA , 0 . 5 mM PMSF , pH 7 . 5 ) . The tissue homogenate was centrifuged at 12000 × g for 10 min at 4 °C to collect the supernatant for further analysis . The specific primers MjpIgR-EX-F and MjpIgR-EX-R ( Table 1 ) were used to amplify the extracellular fragment of MjpIgR . The PCR procedure was as follows: One cycle at 94 °C for 3 min; 35 cycles at 94 °C for 30 s , 54 °C for 30 s , and 72 °C for 90 s; and one cycle at 72 °C for 10 min . The PCR fragments were digested with restriction enzymes XhoI and EcoRI , and then ligated into the pGEX-4T-1 vector ( GE Healthcare , Piscataway , NJ , USA ) . The recombinant plasmid was transformed into Escherichia coli Rosseta ( DE3 ) cells . GST-tagged MjpIgR recombinant expression was induced with 0 . 5 mM isopropyl-β-D-thiogalactopyranoside ( IPTG ) at 37 °C for 4 h . The MjpIgR inclusion bodies were washed two times with Buffer A ( 50 mM Tris-HCl , 5 mM EDTA , pH 8 . 0 ) and then three times with Buffer B ( 50 mM Tris-HCl , 5 mM EDTA , 2 M urea , pH 8 . 0 ) , and the precipitate was collected by centrifugation at 12000 × g for 10 min at 4 °C . Denaturing solution ( 0 . 1 M Tris-HCl , 10 mM DL-Dithiothreitol , 8 M urea ) was added to dissolve the precipitate . The solution was shaken at 37 °C for 30 min and then the supernatant was collected after centrifugation at 12000 × g for 10 min at 4 °C . The MjpIgR was refolded in TBS buffer ( 150 mM NaCl , 3 mM EDTA , 50 mM Tris-HCl , pH 8 . 0 ) for 48 h at 4 °C . The protein was purified using an affinity chromatography with GST-resin ( GenScript , Nanjing , China ) according to the manufacturer’s instructions . Rabbit antiserum against MjpIgR was prepared following a previously reported method [60] . The three IG domains of MjpIgR and MjpIgR-ΔIG1 were also expressed in E . coli and purified for pulldown analysis . The tissue supernatants extracted from hemocytes and other organs ( heart , hepatopancreas , gills , stomach , intestine ) were resuspended in 200 μl of PBS , then 100 μl of SDS-PAGE Sample Loading Buffer ( 2% SDS , 0 . 1% bromophenol blue , 10% glycerin , 14 . 4 mM 2-Mercaptoethanol MCH , 50 mM Tris-HCl , pH 6 . 8 ) was added . The mixtures were centrifuged at 12000 × g for 1 min to collect the supernatants after treatment in a boiling water bath for 5 min . The proteins were separated by 12 . 5% SDS-PAGE and transferred onto a nitrocellulose membrane using Transfer Buffer ( 25 mM Tris , 20 mM Glycine , 0 . 037% SDS , 20% ethyl alcohol ) . After blocking with 3% nonfat milk diluted in TBST buffer ( 150 mM NaCl , 3 mM EDTA , 0 . 1% Tween-20 , 50 mM Tris-HCl , pH 8 . 0 ) for 1 h , the membrane was incubated with antiserum against MjpIgR ( 1:200 dilution in blocking milk solution ) for 4 h at room temperature . The membrane was washed three times with TBST and then incubated with horseradish peroxidase ( HRP ) -conjugated goat anti-rabbit antibodies at 1:10 , 000 dilution in blocking reagent ( ZSGB Bio , Beijing , China ) . The membrane was finally washed by TBST and TBS three times , respectively . Target bands were visualized via the colorimetric reaction by adding 10 ml reaction media ( 1 ml 4-chloro-1-naphthol and 6 μl H2O2 , diluted in TBS ) . Western blotting bands were digitalized and statistic analyzed by Image J . The tissue distribution of MjpIgR mRNA was determined by semi-quantitative reverse transcription-PCR ( RT-PCR ) using primers MjpIgR-RT-F and MjpIgR-RT-R ( Table 1 ) . The β-actin gene was used as the internal control with primers β-actin-RT-F and β-actin-RT-R . The PCR procedure consisted of an initial incubation at 94 °C for 3 min; followed by 26 or 30 cycles of 94 °C for 30 s , 54 °C for 30 s , and 72 °C for 30 s; followed by 72 °C for 10 min . The PCR products were analyzed using agarose gel electrophoresis ( 1 . 2% agarose ) . Correspondingly , the tissue distribution at protein level was analyzed using western blotting . Anti-β-actin antibodies prepared in our laboratory were used for internal protein normalization . Quantitative real-time PCR ( qPCR ) was performed to determine the expression profiles of MjpIgR mRNA after WSSV challenge using the above primers . The CFX96 Real-Time System ( Bio-Rad , USA ) was used to carry out the following PCR procedures: 95 °C for 10 min; 40 cycles at 95 °C for 15 s , 60 °C for 50 s , and reading at 72 °C for 2 s; and then a melting period from 65 °C to 95 °C . The data obtained were analyzed using the cycle threshold ( 2−ΔΔCT ) method , as previously described [61] . The results were expressed as the mean ± SD from three independent repeats and significant differences in Student’s t-test were accepted at p < 0 . 05 . Expression profiles of MjpIgR were analyzed by western blotting at different infection times ( 0 , 12 , 24 , 36 , and 48 h ) corresponding to the mRNA level . Gene-specific primers dsMjpIgR-F and dsMjpIgR-R , linked to the T7 promoter ( Table 1 ) , were used to amplify a partial MjpIgR cDNA fragment . The PCR products acted as the templates for double-stranded RNA ( dsRNA ) synthesis using T7 RNA polymerase ( Fermentas , Burlington , Canada ) , following the manufacturer’s instructions . The dsGFP ( Green fluorescent protein ) coding region , serving as a control , was synthesized using primers dsGFP-F and dsGFP-R ( Table 1 ) . For the RNA interference assay , 50 μg of dsRNA was injected into shrimp and then another 50 μg of dsRNA was injected 12 h after the first injection . The efficiency of RNA interference was assessed at 24 h using qPCR . Similar method was used for knockdown of Mjclathrin , Mjcalmodulin , Mjβ-integrin and MjAP-2α . The pre-serum of rabbit and anti-MjpIgR serum were purified as described previously [62] . Each shrimp was injected with 30 μg of purified antibodies for 2 h and then WSSV was injected into shrimp for 24 h . The vp28 expression level was detected in hemocytes and intestine using qPCR . The survival rate was analyzed after RNAi of MjpIgR in shrimp challenged by WSSV . Shrimp were divided into two groups: The dsGFP group and the dsMjpIgR group . After RNAi for 24 h , WSSV particles ( 1 × 105 ) were injected into two groups of shrimp separately . The two groups were monitored every half-day by counting the numbers of dead shrimp . To detect the function of MjpIgR , an overexpression assay was performed . The MjpIgR open reading frame ( ORF ) was amplified using primers MjpIgR-ORF-F and MjpIgR-ORF-R ( Table 1 ) . The PCR fragments were then ligated into vector pET-32a ( + ) , which contains a T7 promoter . Thereafter , the recombinant plasmid was used for mRNA synthesis and capping , as previous described [40] . The mRNA from empty pET-32a ( + ) vector was used as a control . Each group was injected with 100 μg mRNA for 24 h and the overexpression efficiency was detected using MjpIgR antibodies . Later , WSSV particles were injected into the shrimp for additional 24 h . The RNA , DNA , and protein were extracted from different tissues to evaluate the quantity and copies of WSSV . To qualify as a bona fide receptor of MjpIgR for WSSV entry , the WSSV DNA was detected in MjpIgR overexpressed non-permissive cell type ( human HEK 293T cells ) . To construct the plasmid pcDNA3 . 1 ( - ) -pIgR for expression of pIgR , the ORF of pIgR was amplified using the primers pIgR-pcDNA3 . 1 ( - ) -F and pIgR-pcDNA3 . 1 ( - ) -R ( Table 1 ) and cloned into the XhoI and EcoRI restriction sites of the plasmid pcDNA3 . 1 ( - ) vector . The truncated mutation of IG1 domain ( named pIgR-ΔIG1 ) was also amplified with pIgR-pcDNA3 . 1 ( - ) -F and pIgR-ΔIG1-pcDNA3 . 1 ( - ) -R ( Table 1 ) and cloned into pcDNA3 . 1 ( - ) -pIgR-ΔIG1 plasmid . HEK 293T cells were seeded in 24-well-plate one day before transfection . The pcDNA3 . 1 ( - ) -pIgR expression plasmid , pcDNA3 . 1 ( - ) -pIgR-ΔIG1 expression plasmid or empty vector was transfected into the HEK 293T cells using Lipofectamine 2000 ( invitrogen ) transfection reagent . Twenty-four hours later , WSSV was added into the cells and incubated at 37°C for 1 h . Then the cells were extensively washed with PBS twice to remove uninfected virus particles . Subsequently , the DNA of the cells was isolated using Dneasy Blood & Tissue Kit ( QIAGEN ) , and subject to qPCR assay to detect the WSSV DNA . The primers used in qPCR assay is WSSV VP28-RT-F and WSSV VP28-RT-R; genomic DNA F and genomic DNA R . To detect the distribution and translocation of MjpIgR in hemocytes of shrimp challenged by WSSV , immunocytochemical assays were performed following previous report [63] . Hemocytes were collected in 4% paraformaldehyde and anticoagulation mixtures ( 1:1 ) at different time points after WSSV challenge . The hemocytes were then washed three times with PBS and centrifuged at 800 × g for 6 min at 4 °C to remove the plasma . After re-suspending in PBS , the hemocytes were dropped onto poly-lysine coated glass slides and left to stand for 1 h . The slides were washed six times , and blocked with 3% bovine serum albumin ( dissolved in PBS ) for 30 min at 37 °C . Anti-MjpIgR antibody was then added ( 1:100 diluted in 3% bovine serum albumin ) and the cells were incubated over night at 4 °C . The hemocytes were washed with PBS six times , incubated with goat anti-rabbit antibody conjugated with ALEXA 488 ( 1:1000 diluted in PBS ) for 2 h at 37 °C , washed with PBS again , and then stained with 4-6-diamidino-2-phenylindole ( DAPI ) for 10 min at room temperature . After washing six times , the slides were examined under a fluorescent microscope ( Olympus BX51 , Japan ) . For the immunocytochemical assay of Mjclathrin , rabbit anti-clathrin heavy chain ( Bioss , Beijing , China ) was used ( 1:1000 diluted in 3% bovine serum albumin ) as the primary antibody . The other steps were the same as those described above . To detect the interaction of WSSV particles with MjpIgR , the purified WSSV particles were labeled with Dil ( Beyotime , Shanghai , China ) by incubation with Dil reagent ( 25 μg/ml ) for 2 h at 37 °C and then centrifuged at 12000 × g for 20 min at 4 °C to remove the supernatant . The sediment was washed with PBS twice and resuspended in PBS . The Dil-labeled WSSV was injected into shrimps and hemocytes were collected at different times ( 0 , 15 , 30 , and 60 min ) . The cells were subjected to immunocytochemical assays using anti-MjpIgR antibodies to detect the colocalization of WSSV with MjpIgR . Pull-down assays were performed to further explore the interaction between MjpIgR and WSSV envelope proteins . The four main envelope proteins of WSSV ( VP19 , VP24 , VP26 , and VP28 ) were recombinantly expressed in E . coli using recombinant vector pET32A-VPs . Purified GST-tagged MjpIgR ( 200 μg ) was incubated with the four His-tagged envelope proteins ( 1:1 ) , separately , for 5 h at 4 °C . After incubation with GST-bound resin ( 50 μl ) for 45 min at 4 °C , the resin was washed with PBS five times . Elution buffer ( 10 mM reduced glutathione , 50 mM Tris-HCl , pH 8 . 0 ) was added to wash out the bound proteins . SDS-PAGE was conducted to analyze the proteins . His-pulldown was also performed . Purified His-tagged VPs was incubated with GST-tagged MjpIgR , respectively . After incubation with His-bound resin for 45 min at 4 °C , the resin was washed with PBS five times . Elution buffer ( 0 . 5 M NaCl , 1 M imidazole , 20 mM Tris-HCl , pH 8 . 0 ) was used to wash out the bound protein . To further confirm the interaction of MjpIgR with VP24 , the expression of truncating mutation of IG1 of MjpIgR was performed . The sequence of MjpIgR-ΔIG1 was amplified with primers MjpIgR-ΔIG1-EX-F and MjpIgR-ΔIG1-EX-R ( Table 1 ) and cloned into pGEX-4T-1 vector for recombinant expression . The purified GST-tagged MjpIgR-ΔIG1 was used for pulldown analysis . Flat-bottomed 96-well microliter plates were coated with purified WSSV particles ( 50 μl ) overnight at 4 °C , washed with TBST five times , and then blocked with 3% bovine serum albumin ( dissolved in TBST ) for 1 h at 4 °C . Different proteins were added to the plates at different concentrations . After incubation for 4 h at room temperature and washing five times , an anti-GST Tag Mouse monoclonal antibody ( mAb; Abbkine , CA , USA ) was added to the plates and incubated overnight at 4 °C . Horse anti-mouse antibody ( Zsbio , Beijing , China ) ( 1:2000 diluted in 3% bovine serum albumin ) was added and incubated for 2 h at room temperature . After washing five times , 100 μl of the chromogenic reaction liquid ( 1 mg/ml p-nitro-phenyl phosphate , 10 mM diethanolamine , 0 . 5 mM MgCl2 ) was added to each well for 20 min at room temperature . The absorbance of each well was read using a Universal Microplate Reader ELX800 ( Bio-Tek , USA ) at 405 nm . N- ( 4-Aminobutyl ) -5-chloro-2-naphthalenesulfonamide hydrochloride W13 ( W-13 , Sigma-Aldrich , USA ) was used as a calmodulin antagonist . Different concentrations of W-13 ( 10 , 15 , 30 , 45 , and 60 μM ) were injected into shrimp , and WSSV particles were injected 2 h later . The WSSV expression levels were detected in the hemocytes and intestine at 24 h after WSSV infection . To detect the endocytosis of WSSV , an inhibitor of clathrin-dependent endocytosis , chlorpromazine ( CPZ , Sangon Biotech , Shanghai , China ) was injected into shrimp at different concentrations ( 10 , 15 , 30 , 45 , and 60 μM ) and WSSV was injected 2 h later . The amount of WSSV in the hemocytes and intestine was detected using qPCR . To analyze whether the MjpIgR-induced WSSV endocytosis is clathrin-dependent , CPZ was injected into MjpIgR-overexpressing shrimp infected with WSSV . WSSV replication was detected using qPCR and western blotting with an envelope protein of WSSV as the indicator . WSSV particles were labeled with Dil ( red ) for 2 h and then collected by centrifugation at 12000 × g for 20 min . The Dil-labeled WSSV particles were washed with PBS twice , and then suspended in PBS for shrimp injection . Hemocytes were collected at 1 h for overexpression or RNA interference , and detected using flow cytometry ( ImageStreamX MarkII , USA ) . The MjpIgR ( extracellular domain ) recombinant was used for native PAGE to detect the oligomerization in vitro , as described in previous articles [40] . A crosslinking assay was performed to detect oligomerization in vivo . Intestines from shrimp were ground into a homogenate in PBS , and Subric acid bis sodium salt ( 3-sulfo-N-hydroxysuccinimide ester , BS3; Sigma-Aldrich , USA ) was added to a final concentration of 5 mM . After incubation for 2 h at room temperature , SDS-PAGE sample loading buffer was added for reaction termination . The reagent mixture was treated in a boiling water bath for 5 min followed by SDS-PAGE and western blotting .
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White Spot Syndrome Virus ( WSSV ) is one of the most virulent pathogens in shrimp farming . Several viral candidate receptors , or attachment factors were reported in previous studies , however , most of them are not authentic transmembrane proteins . In particular , the protein receptor ( s ) required the intracellular signaling triggering by interaction of WSSV with receptors remain unclear . In the present study , a polymeric immunoglobulin receptor ( pIgR ) like protein , a bona fide transmembrane receptor , was identified in kuruma shrimp , Marsupenaeus japonicus ( MjpIgR for short ) . Knockdown of MjpIgR by RNA interference , and blocking it by its antibody prevented WSSV infection in shrimp and overexpression of MjpIgR facilitated the invasion of WSSV . Further study found that MjpIgR could independently render non-permissive cells susceptible to WSSV infection . The extracellular cellular domain of MjpIgR interacts with envelope protein VP24 of WSSV and the intracellular domain interacts with calmodulin ( MjCaM ) . MjpIgR was oligomerized and internalized following WSSV infection and the internalization was associated with endocytosis of WSSV . The viral internalization facilitating ability of MjpIgR could be blocked using chlorpromazine , an inhibitor of clathrin dependent endocytosis , indicating that MjpIgR-mediated WSSV endocytosis was clathrin dependent . The results suggested that MjpIgR is a WSSV receptor , and that WSSV enters shrimp cells via the pIgR-CaM-Clathrin endocytosis pathway . This study provides a new target for WSSV control in shrimp aquaculture .
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2019
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The polymeric immunoglobulin receptor-like protein from Marsupenaeus japonicus is a receptor for white spot syndrome virus infection
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Plant leaves are arranged around the stem in a beautiful geometry that is called phyllotaxis . In the majority of plants , phyllotaxis exhibits a distichous , Fibonacci spiral , decussate , or tricussate pattern . To explain the regularity and limited variety of phyllotactic patterns , many theoretical models have been proposed , mostly based on the notion that a repulsive interaction between leaf primordia determines the position of primordium initiation . Among them , particularly notable are the two models of Douady and Couder ( alternate-specific form , DC1; more generalized form , DC2 ) , the key assumptions of which are that each leaf primordium emits a constant power that inhibits new primordium formation and that this inhibitory effect decreases with distance . It was previously demonstrated by computer simulations that any major type of phyllotaxis can occur as a self-organizing stable pattern in the framework of DC models . However , several phyllotactic types remain unaddressed . An interesting example is orixate phyllotaxis , which has a tetrastichous alternate pattern with periodic repetition of a sequence of different divergence angles: 180° , 90° , −180° , and −90° . Although the term orixate phyllotaxis was derived from Orixa japonica , this type is observed in several distant taxa , suggesting that it may reflect some aspects of a common mechanism of phyllotactic patterning . Here we examined DC models regarding the ability to produce orixate phyllotaxis and found that model expansion via the introduction of primordial age-dependent changes of the inhibitory power is absolutely necessary for the establishment of orixate phyllotaxis . The orixate patterns generated by the expanded version of DC2 ( EDC2 ) were shown to share morphological details with real orixate phyllotaxis . Furthermore , the simulation results obtained using EDC2 fitted better the natural distribution of phyllotactic patterns than did those obtained using the previous models . Our findings imply that changing the inhibitory power is generally an important component of the phyllotactic patterning mechanism .
Plants bear leaves around the stem in a regular arrangement; this is termed phyllotaxis . Across diverse plant species , phyllotaxis has common characteristics , which are often described mathematically and are reflected in a limited variety of phyllotactic patterns , including the distichous , decussate , tricussate , and Fibonacci spiral ( spiral with a divergence angle close to the golden angle of 137 . 5° ) patterns [1] . The origin of the regularity of , and the few particular patterns that are allowed in , phyllotaxis have long been fascinating questions for botanists . In the early days , morphological studies attributed phyllotactic patterning to Hofmeister’s axiom , which claims that , on the periphery of the shoot apical meristem ( SAM ) , a new leaf primordium is formed in the largest gap between existing primordia and as far away as possible from them [2] . Following this axiom , many theoretical models have been proposed to explain the generation of phyllotactic patterns [3–21] . Such theoretical models are based on a common concept: the existence of an inhibitory field created by a repulsive , either physical or chemical , interaction between leaf primordia , which conforms to Hofmeister’s axiom . Among them , the two mathematical models proposed by Douady and Couder [15–18] are particularly notable ( they will be referred to as DC1 and DC2 hereafter ) . The key assumptions shared by DC models are that each individual leaf primordium emits a constant power that inhibits the production of a new primordium near it and that the inhibitory effect of this power decreases as the distance from the emission point increases . In DC1 , it is additionally assumed that leaf primordia are formed one by one at a constant time interval , i . e . , plastochron; thus , DC1 deals only with alternate phyllotaxis [15 , 16] . In contrast , DC2 does not deny the simultaneous formation of leaf primordia or temporal changes of the plastochron and can deal with both alternate and whorled phyllotaxis [17] . Computer simulations using DC models demonstrated that they can generate various major standard phyllotactic patterns as stable patterns that depend on parameter settings [15–17] . In the early 2000s , experimental studies showed that auxin determines the initiation of shoot lateral organs and that its polar transport serves as a driving force of phyllotactic patterning [22–24] . Briefly , the auxin efflux carrier PIN1 , which is localized asymmetrically in epidermal cells of the shoot apex , polarly transports auxin to create auxin convergence , thus directing the position of lateral organ initiation . Subsequently , assuming the existence of a positive feedback regulatory loop between the auxin concentration gradient and PIN1 localization , a novel mathematical model was developed to explain the spontaneous formation of the auxin convergence . It was further shown by computer simulation analysis that these models can produce several typical patterns of standard phyllotaxis [25 , 26] . In the auxin-transport-based models , auxin polar transport toward the auxin convergence removes auxin from its surroundings , which prevents the formation of a new , vicinal auxin convergence . This effect is considered to correspond to the repulsive interaction between primordia described in the previous models . The parameters of the auxin-transport-based model were mapped on the parameters of DC2 [27 , 28] , which shows that DC2 can be treated as an abstract model of the auxin-transport-based models . DC models and the auxin-transport-based models , DC2 in particular , have been studied extensively regarding the ability to produce the various phyllotactic patterns that are observed in nature [15–17 , 25–26]; however , several types were never addressed in the studies that used these models . An interesting example is orixate phyllotaxis , which is named after Orixa japonica ( Rutaceae , Sapindales ) [29] . Orixate phyllotaxis is a tetrastichous alternate phyllotaxis that is characterized by the periodic repetition of a sequence of different divergence angles: 180° , 90° , −180° ( 180° ) , and −90° ( 270° ) . Although plant species that show orixate phyllotaxis are uncommon , they are found in several distant taxa ( Fig 1 ) . Many species of Kniphofia ( Asphodelaceae , Asparagales ) display a tetrastichous arrangement of leaves [30] , and K . uvaria , K . pumila , and K . tysonii exhibit orixate phyllotaxis [31 , 32] . Lagestroemia indica ( Lythraceae , Myrtales ) and Berchemiella berchemiaefolia ( Rhamnaceae , Rosales ) are also known as species with orixate phyllotaxis [29] . The rare and sporadic distribution of orixate phyllotaxis among plants suggests that this peculiar phyllotaxis occurred independently a few times during plant evolution . Therefore , it is likely that orixate phyllotaxis is generated by a common regulatory mechanism of leaf-primordium formation under some particular condition rather than by an orixate-unique mechanism . If this is true , mathematical models that account fully for the spatial regulation of leaf-primordium formation should be able to produce not only major phyllotactic patterns , but also orixate phyllotaxis . In this study , we re-examined the original DC models exhaustively under various parameter conditions , to test whether they can produce orixate phyllotaxis . We then expanded DC models by introducing primordial age-dependent changes in the inhibitory power . Our results indicate that a late and slow increase in the inhibitory power is critical for the establishment of orixate phyllotaxis and imply that changing the inhibitory power is generally an important component of the mechanism of phyllotactic patterning .
Terminal winter buds of O . japonica that had been collected in July from nine plants growing at the Koishikawa Botanical Gardens , Graduate School of Science , The University of Tokyo were used for morphological analyses . The winter buds were fixed with 5% v/v formalin , 5% v/v acetic acid , 50% v/v ethanol ( FAA ) , dehydrated in an ethanol series , and finally infiltrated in 100% ethanol . For light microscopic observation , the dehydrated samples were embedded in Technovit 7100 , cut into 5-μm-thick sections using a rotary microtome , and stained with 0 . 5% w/v toluidine blue . The center of gravity was determined for each leaf primordium on the section with ImageJ ( https://imagej . nih . gov ) and was used as its position when measuring morphometric data . For scanning electron microscopy ( SEM ) , the dehydrated samples were infiltrated once with a 1:1 v/v mixture of ethanol and isoamyl acetate and twice with isoamyl acetate . Subsequently , the samples were critical point dried , sputter coated with gold–palladium , and observed using SEM ( Hitachi S-3400N ) . The essential points of the DC1 model are as follows [15 , 16] . At the time when the nth primordium Ln is arising , for a position ( R0 cos θ , R0 sin θ ) on the circle M , the inhibitory field strength I ( θ ) is calculated by summing the inhibitory effects from all preceding primordia , L1 to Ln−1 , as follows: I ( θ ) ≡∑m=1n−1k ( dm ( θ ) ) −η=k∑m=1n−1 ( R02+rm2−2R0rmcos ( θ−θm ) ) −η2 , ( 1 ) where dm is the distance between the position ( R0 cos θ , R0 sin θ ) and the mth primordium ( rm cos θm , rm sin θm ) and k is a proportional coefficient ( Fig 2A ) . In this equation , the inhibitory field strength is assumed to be inversely proportional to the ηth power of the distance from the point emitting the inhibitory power . Considering assumptions 5 and 7 , the distance from the center of the shoot apex to the mth primordium ( rm ) is expressed with the initial radial velocity V0 as: rm=R0eV0R0 ( n−m ) T . ( 2 ) The total inhibitory field strength I is expressed as: I ( θ ) =kR0η∑m=1n−1{1+e2 ( n−m ) G−2e ( n−m ) Gcos ( θ−θm ) }−η2 , ( 3 ) where G is defined as G≡V0T/R0 = ln ( rm/rm+1 ) . Morphometrically , rm/rm+1 is identical to the “plastochron ratio” introduced by Richards [34] . The point ( R0 cos θ , R0 sin θ ) where I ( θ ) is smallest is chosen for the position of a new primordium . Note that η and G are the only relevant parameters that influence the behavior of I ( θ ) in DC1 . The essential points of the DC2 model are as follows [17] . Positions on the conical surface are expressed in spherical coordinates ( r , ψ2 , θ ) ( Fig 2B ) . The inhibitory field strength I ( θ ) at the position ( R0 , ψ2 , θ ) on M is calculated by summing the inhibitory effects from all preceding primordia , L1 to Ln−1 , as follows: I ( θ ) ≡∑m=1n−1E ( dm ( θ ) d0 ) , ( 4 ) where dm is the distance between the mth primordium and the position ( R0 , ψ2 , θ ) , d0 is the maximum distance within which an existing primordium excludes a new primordium , and E is the inhibitory effect from the preceding primordium , which is defined as a monotonically decreasing , downward-convex function: E ( x ) ≡Es−1+ ( tanhαx ) −1−1+ ( tanhα ) −1 , ( 5 ) where , if I ( θ ) <Es , a new primordium is placed at the position ( R0 , ψ2 , θ ) . Throughout this study , Es = 1 . Because of assumption 6 , the distance from the center of the shoot apex to the mth primordium on the conical surface ( rm ) is expressed with the time after its emergence Tm and the initial radial velocity V0 as: rm=R0eV0R0Tm . ( 6 ) By using tm≡TmV0/R0 , a standardized age of the mth primordium defined as the product of Tm and the relative SAM growth rate V0/R0 , rm is more simply expressed as: rm=R0etm . ( 7 ) The DC2 model is characterized by three parameters: α , N≡sinψ2 , and Γ≡d0R0N . These parameters represent the steepness of the decline of the inhibitory effect around the threshold , the flatness of the shoot apex , and the ratio of the inhibition range to the SAM size , respectively . In DC2 , as a distance between points ( r ( 1 ) , ψ2 , θ ( 1 ) ) and ( r ( 2 ) , ψ2 , θ ( 2 ) ) on the conical surface , instead of the true Euclidian distance , its slightly modified version ( as defined in the following equation ) was used to avoid the discontinuity problem [17]: d≡ ( r ( 1 ) −r ( 2 ) ) 2N+2Nr ( 1 ) r ( 2 ) {1−cos ( θ ( 1 ) −θ ( 2 ) ) } . ( 8 ) Model simulations were implemented in C++ with Visual C++ in Microsoft Visual Studio 2015 as an integrated development environment . Contour mapping was performed using OpenCV ver . 3 . 3 . 1 ( https://opencv . org/ ) . Computer simulations using DC2 and DC2-derived models were initiated by placing a single primordium or two primordia at a central angle of 120° on the SAM periphery . In the former initial condition , the second primordium arises at a certain time or immediately after the first primordium , in dependence on parameter settings , at the opposite position , and in some cases , more primordia are immediately inserted at middle positions . Thus computer simulations with this condition substantially cover situations starting with 1×2x primordia ( x = 0 , 1 , 2⋯ ) evenly distributed on the SAM periphery . Similarly , simulations with the latter condition substantially cover situations starting with 3×2x primordia ( x = 0 , 1 , 2⋯ ) . We also tested simulations with another initial condition , in which two primordia were placed at opposite positions with a central angle of 180° , but they returned completely same results as simulations initiated by placing a single primordium did and are therefore omitted . Computer simulations were performed with an angle resolution of 0 . 1° . DC2 and DC2-derived models were simulated with a time step of Δtm = 0 . 001 . In all model simulations , calculation was iterated until the total number of primordia reached 100 . For alternate patterns generated by simulation , the last nine primordia were used to judge the stability and regularity of divergence angles . For the other patterns , the last two nodes were used to judge the stability of the number of primordia per node . Then the patterns were categorized and displayed as shown in Fig 3 .
First , we performed an anatomical analysis of the apical winter buds of O . japonica , to characterize morphologically its phyllotaxis . In the transverse sections of the winter buds , there was a very obvious tetrastichous pattern of leaf primordia , which were arranged in opposite pairs on either of two orthogonal lines ( Fig 4A ) . This pattern looked similar to decussate phyllotaxis; however , unlike decussate phyllotaxis , it was not symmetric . Opposite pairs of primordia varied in size and radial distance and , in each pair , a smaller primordium was positioned closer to the center of the shoot apex . Such asymmetry was also clearly recognized in the longitudinal sections and by observations performed using SEM ( Fig 4B and 4C ) . Importantly , SEM observations detected incipient primordia that were not paired ( Fig 4C ) . Therefore , the asymmetric arrangement of leaves was attributed to the alternate initiation of leaf primordia instead of the secondary displacement of originally decussate leaf primordia . The divergence angle between successive primordia changed in the sequence of approximately 180° , 90° , −180° ( 180° ) , and −90° ( 270° ) , and this cycle was repeated a few times in the winter bud ( Fig 4D ) . These results confirmed that the phyllotaxis of O . japonica is genuinely an “orixate phyllotaxis” . Richards’ plastochron ratio was found to oscillate in relation to the divergence angle . Plastochron ratios measured from the adjacent pairs of primordia with a divergence angle of approximately ±90° were significantly larger than those measured from the opposite pairs with a divergence angle of approximately ±180° ( Fig 4E ) . A similar relationship between divergence angles and plastochron ratios had been , albeit fragmentarily , described for the orixate phyllotaxis of K . uvaria [32]; thus , it is likely to be a common feature of orixate phyllotaxis . DC1 is an inhibitory field model specialized for alternate phyllotactic patterning . DC1 assumes one-by-one formation of leaf primordia at a constant time interval , which strongly limits the model flexibility [16] . Nevertheless , as this constraint makes the patterning process simple and possible to be dealt with theoretically , it is worth investigating DC1 as a primary model for generation of any types of alternate phyllotaxis . To test whether DC1 can produce orixate phyllotaxis , we re-examined this established model via detailed computer simulation analysis using exhaustive combinations of the determinant parameters , η and G . As reported previously [15 , 16] , distichous and relatively major spiral phyllotactic patterns , i . e . , alternate patterns with a regular divergence angle near 180° , a Fibonacci angle ( 137 . 5° ) , or a Lucas angle ( 99 . 5° ) , were generated as stable patterns over broad ranges of η and G in these simulations ( Fig 5 ) . Of note , when η and G were set to 1–3 and about 0 . 2 , respectively , tetrastichous patterns were formed that resembled orixate phyllotaxis , as they showed a four-cycle periodic change of the divergence angle in the order of p , q , −p , and −q ( −180°≤p≤180° , |p|>|q| ) ( Fig 5A ) . In these patterns , however , the larger absolute value of the divergence angle was considerably deviated from 180° , whereas this should be very close to 180° in orixate phyllotaxis ( Fig 5B ) . These patterns showed nonorthogonal tetrastichy , which is distinct in appearance from the orthogonal tetrastichy of orixate phyllotaxis ( Fig 5C ) . Therefore , we concluded that the tetrastichous patterns found in simulations with DC1 are not orixate and that DC1 does not generate the orixate phyllotactic pattern at any parameter setting . The absence of the occurrence of normal orixate phyllotaxis , the divergence angles of which are exactly ±180° and ±90° , in the context of DC1 can be explained analytically ( S1 Text ) . Next , we examined whether modification of DC1 could enable it to produce orixate phyllotaxis . In an attempt to modify DC1 , we focused on the inhibitory power of each leaf primordium against new primordium formation—which is assumed to be constant in DC models but may possibly change during leaf development—and expanded DC1 by introducing age-dependent , sigmoidal changes in the inhibitory power . In this expanded version of DC1 ( EDC1 ) , the inhibitory field strength I ( θ ) was redefined as the summation of the products of the age-dependent change in the inhibitory power and the distance-dependent decline of its effect: I ( θ ) ≡∑m=1n−1{k ( dm ( θ ) ) −ηF ( n−m ) } . ( 9 ) F is defined as: F ( Δt ) ≡11+e−a ( Δt−b ) , ( 10 ) where parameters a and b are constants that represent the rate and timing of the age-dependent changes in the inhibitory power , respectively . Under this equation , in an age-dependent manner , the inhibitory power increases at a>0 and decreases at a<0 . In the present study , η was fixed at 2 for EDC1 . Prior to computer simulation analysis with EDC1 , we searched for parameters of EDC1 that can fit the requirements of normal orixate phyllotaxis . When the normal pattern of orixate phyllotaxis is stably maintained , a rectangular coordinate system with the origin at the center of the shoot apex can be set such that all primordia lie on the coordinate axes , and every fourth primordium is located on the same axis in the same direction , i . e . , the position of any primordium ( mth primordium ) can be expressed as ( rm cos θm−4i , rm sin θm−4i ) for integers i . Under this condition , we considered whether a new primordium ( nth primordium ) is produced at the position ( R0 cos θn−4i , R0 sin θn−4i ) , to keep the normal orixate phyllotactic pattern . In EDC1 , as in DC1 , new primordium formation at ( R0 cos θn−4i , R0 sin θn−4i ) implies that the inhibitory field strength I ( θ ) on the circle M has a minimum at θn−4i . For this reason , we first attempted to solve the following equation: dI ( θ ) dθ|θ−θn−4i=0=0 . ( 11 ) This equation was numerically solved under two geometrical situations of primordia: the divergence angle between the newly arising primordium and the last primordium is ±90° ( situation 1 ) or ±180° ( situation 2 ) ( S1A Fig ) . The solutions obtained identified parameter sets that satisfied the above equation under both these two situations ( Fig 6A , S1B Fig ) . The calculation of I ( θ ) using the identified parameter sets showed that I ( θ ) has a local and global minimum around θn−4i with large values of G , such as 0 . 5 or 1 , while it has a local maximum instead of a minimum around θn−4i with small G values , such as 0 . 1 ( S1C Fig ) . This result indicates the possibility that EDC1 can form orixate phyllotaxis as a stable pattern under a particular parameter setting with large G values . We conducted computer simulations using EDC1 over broad ranges of parameters and found that EDC1 could generate tetrastichous alternate patterns in addition to distichous and spiral patterns ( Fig 6B ) . The tetrastichous patterns included orthogonal tetrastichous ones with a four-cycle divergence angle change of approximately 180° , 90° , −180° , and −90° , which can be regarded as orixate phyllotaxis ( Fig 6C , S2 Fig ) . Under the conditions of assuming an age-dependent increase in the inhibitory power ( a>0 ) , these orixate patterns were formed within a rather narrow parameter range of G = 0 . 5~1 , a = 1~2 , and b = 4~9 around the parameter settings that were determined by numerical solution , to fit the requirements for the stable maintenance of normal orixate phyllotaxis ( Fig 6B and 6C ) . When assuming an age-dependent decrease in the inhibitory power ( a<0 ) , orixate phyllotaxis appeared at a point of G = 0 . 1 , a≈−10 , and b≈3 . 5 ( Fig 6B and 6C ) . These values of a and b represent a very sharp drop in the inhibitory power at the primordial age corresponding to approximately three plastochron units . Around this parameter condition , there were no numerical solutions for normal orixate phyllotaxis; however , patterns that were substantially orixate , although they were not completely normal , could be established . The orixate patterns that were generated under the conditions in which the inhibitory power increased and decreased were visually characterized by sparse primordia around the small meristem and dense primordia around the large meristem , respectively ( Fig 6C ) . In the results of computer simulations with EDC1 , besides the orixate patterns , we also found peculiar patterns with an x-cycle change in the divergence angle consisting of 180° followed by an ( x−1 ) -times repeat of 0° ( S3 Fig ) . Such patterns were generated when all the parameters a , b , and G were set to relatively large values and are displayed as periodic distribution of black regions in the upper right area of the middle and right panels of Fig 6B . In these patterns , as b is increased , the number of repetition times of 0° is increased , resulting in the shift from x-cycle to ( x+1 ) -cycle . This shift is mediated by the occurrence of spiral patterns with a small divergence angle , and the transitions from x-cycle to spiral and from spiral to ( x+1 ) -cycle takes place suddenly in response to a slight change of b ( S3 Fig ) . DC2 , as DC1 , is an inhibitory field model but is more generalized than DC1 [17] . Unlike DC1 , DC2 does not assume one-by-one formation of primordia at a constant time interval and thus does not exclude whorled phyllotactic patterning . Indeed , DC2 was shown to produce all major patterns of either alternate or whorled phyllotaxis depending on parameter conditions [17] . To test whether DC2 can generate orixate phyllotactic patterns , we carried out extensive computer simulation analyses using this model . Our computer simulations confirmed that major phyllotactic patterns , such as distichous , Fibonacci spiral , Lucas spiral , decussate , and tricussate patterns , are formed as stable patterns in wide ranges of parameters , and also showed formation of tetrastichous alternate patterns with a four-cycle change of the divergence angle at N = 1 and Γ≈1 . 8 when initiated by placing a single primordium at the SAM periphery ( Fig 7A ) . The possible inclusion of orixate phyllotaxis in these tetrastichous four-cycle patterns was carefully examined based on the ratio of plastochron times and the ratio of absolute values of divergence angles , which should be much larger than 0 and close to 0 . 5 , respectively , in orixate phyllotaxis . Although all the tetrastichous four-cycle patterns detected here had a divergence angle ratio near 0 . 5 , their ratios of plastochron times were too small to be regarded as orixate phyllotaxis , and the overall characters indicated that they are rather similar to decussate phyllotaxis ( Fig 7B and 7C ) . These results led to the conclusion that the DC2 system does not generate orixate phyllotaxis under any parameter conditions . Similar to the approach used for DC1 , we expanded DC2 by introducing primordial age-dependent changes in the inhibitory power . In this expanded version of DC2 ( EDC2 ) , the inhibitory field strength I ( θ ) was redefined as the summation of the products of the age-dependent change in the inhibitory power and the distance-dependent decrease of its effect: I ( θ ) ≡∑m=1n−1{E ( dm ( θ ) d0 ) F ( tm ) } , ( 12 ) where F is a function expressing a temporal change in the inhibitory power , defined as: F ( t ) ≡11+e−A ( t−B ) . ( 13 ) Computer simulations using EDC2 were first conducted under a wide range of combinations of A and B at three different settings of Γ ( Γ = 1 , 2 , or 3 ) and fixed conditions for α and N ( α = 1 , N = 1/3 ) ( S4 Fig ) . In this analysis , tetrastichous four-cycle patterns were formed within the parameter window where A was 3–7 and B was 0 . 4–1 , which represents a late and slow increase in the inhibitory power during primordium development ( Fig 8A ) . Further analysis performed by changing Γ , α , and N showed that small values of α , which indicate that the distance-dependent decrease in the inhibitory effect is gradual , and large values of Γ , which indicate that the maximum inhibition range of a primordium is large , are also important for the formation of tetrastichous four-cycle patterns ( Fig 9 , S5 Fig ) . All of these four-cycle patterns were found to be almost orthogonal and to have a sufficiently large ratio of successive plastochron times , thus fitting the criterion of orixate phyllotaxis ( Fig 8B , S7 Fig ) . Furthermore , the plots of these patterns lied within the cloud of the data points of real orixate phyllotaxis , and therefore we concluded that they are orixate . A typical example of such orixate patterns was obtained by simulation using the parameters , A = 4 . 8 , B = 0 . 72 , Γ = 2 . 8 , N = 1/3 , and α = 1 , and is presented as a contour map of the inhibitory field strength in Fig 10A , which clearly depicts orixate phyllotactic patterning . Under this parameter condition , the inhibitory field strength on the SAM periphery was calculated to have a minimum close to the threshold at 0° at the time of new primordium formation when the preceding primordia were placed at 0° , 180° , and ±90° ( S8 Fig ) . This landscape of the inhibitory field stabilizes the orixate arrangement of primordia . In summary , our analysis demonstrated that orixate phyllotaxis comes into existence in the EDC2 system when the inhibitory power of each primordium increases at a late stage and slowly to a large maximum and when its effect decreases gradually with distance . In the orixate phyllotactic patterns generated by EDC2 , the plastochron time oscillated between two values together with a cyclic change in the divergence angle: the longer plastochron was observed for the adjacent pairs of primordia with a divergence angle of ±90° and the shorter plastochron was recorded for the opposite pairs with a divergence angle of ±180° ( Fig 10B , S1 Movie ) . This relationship between the plastochron and the divergence angle agreed with the real linkage observed for the plastochron ratios and divergence angles in the winter buds of O . japonica ( Fig 4E ) . Based on a comprehensive survey of the results of the computer simulations performed using EDC2 , we examined the distribution of various phyllotactic patterns and the possible relationships between them in the parameter space of EDC2 ( Figs 8A and 9 , S4 , S5 , S9 and S10 Figs ) . Major phyllotactic patterns , such as the distichous , Fibonacci spiral , and decussate patterns , occupied large areas in the parameter space , and the Lucas spiral pattern occupied some areas . Depending on the initial condition , the tricussate pattern also took a considerable fraction of the space . In the parameter space , the distichous pattern adjoined the Fibonacci spiral pattern , while the Fibonacci spiral adjoined the distichous , Lucas spiral , decussate , and tricussate patterns . The regions where the orixate pattern was generated were located next to the regions of the decussate , Fibonacci spiral , Lucas spiral , and/or two-cycle alternate patterns . This positional relationship suggests that orixate phyllotaxis is more closely related to the decussate and spiral patterns than it is to the distichous pattern . The two-cycle patterns formed in a narrow parameter space next to the region of orixate phyllotaxis and had a divergence angle ratio of approximately 0 . 55 and a plastochron time ratio of approximately 0 . 2 ( Fig 8B , S6A Fig ) ; thus , they are similar to semi-decussate phyllotaxis , which is an alternate arrangement characterized by the oscillation of the divergence angle between 180° and 90° ( S6B Fig ) . These semi-decussate-like patterns were not observed in the computer simulations performed using DC2 ( Fig 7B and 7C ) ; rather , they were produced only after its expansion into EDC2 . The overall distributions of major phyllotactic patterns in the parameter space were compared between DC2 and EDC2 using color plots drawn from the results of simulations conducted for EDC2 with various settings of the inhibition range parameter Γ and the inhibitory power change parameter A ( Fig 9 ) . In these simulations , large A values accelerated the age-dependent increase in the inhibitory power of each primordium; if A is sufficiently large , the inhibitory power is almost constant during primordium development and the EDC2 system is almost the same as DC2 . Therefore , the colors along the top side of each panel of Fig 9 , where A was set to 20 , which is a high value , show the phyllotactic pattern distribution against Γ in DC2 , while the colors over the two-dimensional panel show the phyllotactic pattern distribution against Γ and A in EDC2 . The order of distribution of the distichous , Fibonacci spiral , and decussate patterns was unaffected by decreasing A and , thus , did not differ between DC2 and EDC2 . As reported in the previous study of DC2 [17] , on the top side of Fig 9 , the stable pattern changed from distichous to Fibonacci spiral , and then turned into decussate as Γ decreased . In the parameter space of EDC2 , this order of distribution of major phyllotactic patterns was not affected much by decreasing A to moderate values; however , when A was further decreased , the orixate pattern appeared in the region of the Fibonacci spiral ( Fig 9 , S10 Fig ) . As A decreased , the range of Γ that produced a Fibonacci spiral became wider and the transition zone between the distichous and Fibonacci spiral patterns , where the divergence angle gradually changed from 180° to 137 . 5° , became narrower ( Fig 9 ) . This result indicated that Fibonacci spiral phyllotaxis is more dominant when assuming a delay in the primordial age-dependent increase in the inhibitory power .
Orixate phyllotaxis is a special kind of alternate phyllotaxis with orthogonal tetrastichy resulting from a four-cycle change in the divergence angle in the order of approximately 180° , 90° , −180° ( 180° ) , and −90° ( 270° ) ; this phyllotaxis occurs in a few plant species across distant taxa [29–32] . In the present study , we investigated a possible theoretical framework behind this minor but interesting phyllotaxis on the basis of the inhibitory field models proposed by Douady and Couder [16 , 17] , which were shown to give a simple and robust explanation for the self-organization process of major phyllotactic patterns by assuming that each existing leaf primordium emits a constant level of inhibitory power against the formation of a new primordium and that its effect decreases with distance from the primordium . Re-examination of the original versions of Douady and Couder’s models ( DC1 and DC2 ) via exhaustive computer simulations revealed that they do not generate the orixate pattern at any parameter condition . The inability of DC models to produce orixate phyllotaxis prompted us to expand them to account for a more comprehensive generation of phyllotactic patterns . In an attempt to modify DC models , we introduced a temporal change in the inhibitory power during primordium development , instead of using a constant inhibitory power . Such changes of the inhibitory power were partly considered in several previous studies . Douady and Couder assessed the effects of “the growth of the element’s size” , which is equivalent to the primordial age-dependent increase in the inhibitory power and found that it stabilizes whorled phyllotactic patterns [17] . Smith et al . assumed in their mathematical model that the inhibitory power of each primordium decays exponentially with age and stated that this decay promoted phyllotactic pattern formation de novo , as well as pattern transition , and allowed the maintenance of patterns for wider ranges of parameters [9] . A DC1-based model equipped with a primordial age-dependent change in the inhibitory power was also used to investigate floral organ arrangement [35 , 36] . In these studies , however , temporal changes in the inhibitory power were examined under limited ranges of parameters focusing on particular aspects of phyllotactic patterning , and the possibility of the generation of minor patterns , such as orixate phyllotaxis , was not addressed . We expanded DC1 into EDC1 and DC2 into EDC2 by simply incorporating the assumption that the inhibitory power of a primordium is not necessarily constant but may increase or decrease sigmoidally with its age . Extensive computer simulations performed using EDC1 and EDC2 over wide ranges of parameters demonstrated that both of the expanded models can produce orixate phyllotaxis under some parameter conditions . In EDC1 , orixate patterns occurred when the inhibitory power was set to increase gradually at large values of the parameter G , which represent a small SAM relative to the growth velocity and/or plastochron , and when the inhibitory power decreased suddenly after a certain time lag of about 3T at small G values , which represent a large SAM . In these two conditions , orixate phyllotactic patterns obviously arise for distinct reasons ( Fig 11 ) . Here , let us consider the effect of four pre-existing primordia , which are arranged in the normal orixate pattern on the orthogonal tetrastichy lines , on a new primordium arising at 0° . The key requirements for the formation of a new primordium at 0° to maintain the orixate pattern are: that the inhibitory effects of the primordia at ±90° ( previous and second or third previous primordia ) are balanced at the site of new primordium formation , and that the inhibitory effect from the fourth previous primordium at 0° is negligible . In the case of a large G value with a gradual increase in the inhibitory power , the primordia at ±90° are quite different in the distance to the new primordium site , but their effect can be equalized because of the compensation of the distance-dependent decrease in the inhibitory effect by the age-dependent increase in the inhibitory power , and the fourth previous primordium has little impact because it is located far away . In contrast , in the case of a small G value with a sudden decrease in the inhibitory power , the primordia at ±90° exhibit almost the same distance and , therefore , almost the same strength of influence on the site of formation of the new primordium , and the fourth previous primordium no longer has an impact because of the immediately preceding sharp drop in its inhibitory power . In EDC2 , the constraint imposed in EDC1 that leaf primordia are formed one by one at a regular time interval is removed , which allows the simultaneous formation of two or more primordia . Probably because the removal of this constraint destabilizes orixate patterning with a sudden decrease in the inhibitory power , EDC2 , unlike EDC1 , generated orixate phyllotaxis as a stable pattern only when the inhibitory power was assumed to increase at a late stage and slowly . The orixate patterns produced using EDC2 under this condition had relatively small and large plastochron ratios for the opposite and adjacent pairs of primordia , respectively . A similar feature was observed in the phyllotactic pattern of the winter buds of O . japonica and was previously reported for the orixate phyllotactic patterns of Kniphofia [32] . These findings suggest that orixate pattern generation in computer simulations performed using EDC2 reflects actual phyllotaxis development and that the occurrence of orixate phyllotaxis in distant plant species can be generally explained by the slow and late increase in the inhibitory power . In real plants , the first leaf primordium arises under some influence of pre-existing structures such as cotyledons , which should be considered as the initial condition in model simulation analysis . However , as simulations with EDC2 under two different initial conditions produced orixate patterns at similar parameter settings , orixate phyllotaxis seems not to require specific initial conditions . There are two views regarding the relationship between orixate phyllotaxis and major phyllotactic patterns . One view was derived from ontogenic observations and regards orixate phyllotaxis as an intermediate form between the distichous and decussate patterns [29] , while the other view was derived from a theoretical consideration of symmetry-breaking processes and regards orixate phyllotaxis as an intermediate form between the spiral and decussate patterns [37] . In the parameter space of EDC2 , orixate patterns were located in the vicinities of the regions of the decussate , Fibonacci spiral , and Lucas spiral patterns , which indicates a close relationship between orixate phyllotaxis and the decussate and spiral patterns , but not the distichous phyllotaxis; thus , this observation favors the latter view . Among the neighbors of orixate phyllotaxis , oscillating patterns were also found , including a semi-decussate-like one , which could not be generated in DC2 . Semi-decussate or semi-decussate-like phyllotaxis is quite rare in nature and has been described in only a few plants , such as Dioscorea sansibarensis , Najas guadalupensis , and Kniphofia “Tubergeniana” [30–32] . The tomato plant ( Solanum lycopersicum ) Shin-Toyotama No . 2 , a Japanese cultivar , and e-2 , a mutant of Sister-of-PIN1 , which is a paralogue of the auxin-efflux carrier gene PIN1 , were also reported to exhibit a semi-decussate pattern [38 , 39] . Among these plants , K . “Tubergeniana” is of particular interest , because its relatives of the same genus have orixate phyllotaxis ( K . uvaria , K . pumila , and K . tysonii ) or spiral phyllotaxis ( K . northiae ) [30 , 32] . This phyllotactic variety in Kniphofia fits well the simulation result that the spiral and semi-decussate-like patterns were located close to the orixate pattern in the EDC2 parameter space and can be converted into the orixate pattern by small changes in the parameters . The Fibonacci spiral with a divergence angle close to the golden angle ( 137 . 5° ) is one of the most common patterns of phyllotaxis observed in plants and is predominant among the spiral phyllotactic patterns . Although this pattern can be generated by previous inhibitory field models , such as DC models , its dominance has not been fully explained by these models [40] . For example , in DC2 , the divergence angle of alternate phyllotaxis is shifted gradually from 180° ( distichous ) to 137 . 5° ( Fibonacci spiral ) as the parameter Γ is reduced from 2 . 6 to 1 . 9 at α = 8 and N = 1/3 , and the range of Γ that generates the Fibonacci spiral is not wider than that observed for the other spirals [17] . Our computer simulations performed using EDC2 showed that , compared with DC2 , the expanded model assigns a smaller area to spiral patterns with a non-golden angle in the parameter space . This tendency in EDC2 suggests that the dominant occurrence of the golden spiral in nature may be better explained by introducing primordial age-dependent changes in the inhibitory power into the inhibitory field model . In summary , we here propose EDC2 as a most appropriate abstract model of phyllotaxis that can generate a wide range of phyllotactic patterns , including not only major types but also minor types of phyllotaxis , with reasonable proportions comparable to the frequencies of their natural occurrence . At the molecular level , phyllotactic patterning is now believed to be based on the regulation of the PIN1-driven polar transport of auxin [28] . According to the widely accepted auxin-transport-based model , the membrane localization of PIN1 in the epidermis of the shoot apical region is regulated in response to auxin concentrations in neighboring cells , to build up the auxin gradient , which forms a positive feedback loop that results in the spontaneous establishment of auxin convergence , leading to primordium initiation . In this framework , the age-dependent increase in the inhibitory power included in EDC models is supposed to reflect that the range at which the auxin convergence absorbs auxin expands with time after its emergence . A possibly relevant assumption was included in Smith et al . ’s auxin model [25] , in which , in addition to the positive feedback dynamics between the auxin gradient and PIN1 localization , it is assumed that PIN1 proteins of all epidermal cells of each primordium are polarized to the tip of the primordium and that this polarization is maintained throughout the growth of the primordium . We now hypothesize that , following the establishment of auxin convergence by the basic feedback dynamics , the range of auxin polar transport toward the convergence point expands by a mechanism different from the basic dynamics as a primordium develops at the auxin convergence , which may correspond to the primordial age-dependent increase of the inhibitory power in EDC2 , and that the timing and rate of this expansion can be greatly different among plant species , which should affect phyllotactic patterning as one of critical determinants . Future investigation of the regulatory properties of auxin polar transport during primordium development would provide clues regarding the molecular mechanisms underlying the presumptive age-dependent increase in the primordial inhibitory power and contribute to understanding the variation and limitation of phyllotactic patterns .
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Phyllotaxis , the beautiful geometry of plant-leaf arrangement around the stem , has long attracted the attention of researchers of biological-pattern formation . Many mathematical models , as typified by those of Douady and Couder ( alternate-specific form , DC1; more generalized form , DC2 ) , have been proposed for phyllotactic patterning , mostly based on the notion that a repulsive interaction between leaf primordia spatially regulates primordium initiation . In the framework of DC models , which assume that each primordium emits a constant power that inhibits new primordium formation and that this inhibitory effect decreases with distance , the major types ( but not all types ) of phyllotaxis can occur as stable patterns . Orixate phyllotaxis , which has a tetrastichous alternate pattern with a four-cycle sequence of the divergence angle , is an interesting example of an unaddressed phyllotaxis type . Here , we examined DC models regarding the ability to produce orixate phyllotaxis and found that model expansion by introducing primordial age-dependent changes of the inhibitory power is absolutely necessary for the establishment of orixate phyllotaxis . The simulation results obtained using the expanded version of DC2 ( EDC2 ) fitted well the natural distribution of phyllotactic patterns . Our findings imply that changing the inhibitory power is generally an important component of the phyllotactic patterning mechanism .
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2019
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Mathematical model studies of the comprehensive generation of major and minor phyllotactic patterns in plants with a predominant focus on orixate phyllotaxis
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Axon injury can lead to several cell survival responses including increased stability and axon regeneration . Using an accessible Drosophila model system , we investigated the regulation of injury responses and their relationship . Axon injury stabilizes the rest of the cell , including the entire dendrite arbor . After axon injury we found mitochondrial fission in dendrites was upregulated , and that reducing fission increased stabilization or neuroprotection ( NP ) . Thus axon injury seems to both turn on NP , but also dampen it by activating mitochondrial fission . We also identified caspases as negative regulators of axon injury-mediated NP , so mitochondrial fission could control NP through caspase activation . In addition to negative regulators of NP , we found that nicotinamide mononucleotide adenylyltransferase ( Nmnat ) is absolutely required for this type of NP . Increased microtubule dynamics , which has previously been associated with NP , required Nmnat . Indeed Nmnat overexpression was sufficient to induce NP and increase microtubule dynamics in the absence of axon injury . DLK , JNK and fos were also required for NP . Because NP occurs before axon regeneration , and NP seems to be actively downregulated , we tested whether excessive NP might inhibit regeneration . Indeed both Nmnat overexpression and caspase reduction reduced regeneration . In addition , overexpression of fos or JNK extended the timecourse of NP and dampened regeneration in a Nmnat-dependent manner . These data suggest that NP and regeneration are conflicting responses to axon injury , and that therapeutic strategies that boost NP may reduce regeneration .
The ability of neurons to survive injury , misfolded proteins , hypoxic stress and other deleterious conditions allows the nervous system to function for a lifetime without large-scale production of new neurons . Neuronal survival strategies buy the cells time to maintain or regain function . For example , neurons may remain non-functional for weeks , months or years after axonal trauma . Their survival allows axon regeneration to take place , and eventually , if an appropriate target is reached , the cells may again function . Preconditioning is a transient survival strategy triggered by a stressful , but sublethal , event . For example , when blood flow to a region of the brain is transiently reduced , the effects of a subsequent ischemic stroke are not as severe [1 , 2] . Tissue-level preconditioning seems to have an immediate phase , and then a longer-term transcription-dependent phase [2 , 3] and is proposed to be a very general stress response mechanism . Preconditioning has also been described at a single cell level . In Dorsal Root Ganglion ( DRG ) neurons , severing the peripheral axon enables the central axon for regeneration [4] . The initial peripheral lesion triggers transcriptional changes in the cell body that are proposed to facilitate subsequent regeneration of the central axon [5 , 6] . In Drosophila models of conditioning lesion in sensory and motor neurons , axon severing turns on a stabilization pathway that is measured by resistance to degeneration after a subsequent injury [7 , 8] . This single cell neuroprotection ( NP ) requires dual leucine zipper kinase ( DLK ) [7] and c-Jun N-terminal Kinase ( JNK ) [8] . DLK is a MAP kinase kinase kinase , and JNK is the downstream MAP kinase , which play central roles in the regulatory cascade that initiates axon regeneration in nematodes , flies and mammals [9–12] . DLK/JNK are therefore implicated in regulation of both axon regeneration and preconditioning or NP in response to axon injury . Using the Drosophila sensory neuron model for preconditioning , we investigate the effectors mediating NP downstream of DLK/JNK , and the relationship between NP and axon regeneration . One hallmark of NP is a dramatic increase in microtubule dynamics [8] , a response that has also been seen in mammalian neurons [13] . Mitochondria have been suggested to play a central role in brain preconditioning [14] , and are important for axonal stability in C . elegans [15] and in many systems the Wallerian degeneration slow ( Wlds ) protein seems to act through mitochondria to stabilize axons [16–19] . We therefore started by investigating the role of mitochondria in NP . Surprisingly , we found that , rather than promoting NP , mitochondria have an inhibitory role in this process , and caspases share this negative regulatory role . Moreover , although regeneration and NP are downstream of the same kinase cascade , NP antagonizes regeneration . These results are unexpected , but fit together into a multi-step model of axon injury responses downstream of DLK/JNK .
In Drosophila sensory neurons , severing an axon with a pulsed UV laser stabilizes the cell such that if a dendrite is later removed its degeneration is delayed [8] . Dendrites normally degenerate completely within 18h ( Fig 1A ) . However , when axons are damaged 8h prior to dendrite injury , the severed dendrites are stabilized and take more than 18h to fragment [8] . Stabilization is maximal at 8-24h and tapers off at 48h after axon injury [8] ( Fig 1A ) . The timing of this stabilization correlates with a dramatic increase in the number of growing microtubules , and this increase in microtubule dynamics is required for stabilization [8] . To assess the role of mitochondria in axotomy-induced stabilization or NP , we depleted mitochondria from dendrites using RNAi-mediated knockdown of the mitochondrial Rho-GTPase Miro , which is required for mitochondrial transport in neurons [20 , 21] . We have previously shown that Miro RNAi reduces the number of mitochondria in dendrites of ddaE neurons [22] . Because the NP assay uses the speed of dendrite degeneration to probe stability , we tested whether reduction of mitochondria would affect dendrite degeneration itself , without prior axon injury . We previously demonstrated that small regions of dendrites with no mitochondria degenerate with normal timing [22] . Here , we severed the whole dendrite with normal numbers of mitochondria ( wild-type , WT ) or reduced mitochondria ( Miro RNAi ) and assayed degeneration at different times after severing . The time course of degeneration in neurons expressing a control ( Rtnl2 ) RNAi or Miro RNAi was similar ( Fig 1E ) with a few cells starting to degenerate at 7h after dendrite injury , and most cells degenerating by 11h after severing . In the standard NP assay we sever an axon , wait 8h , then sever a dendrite . Dendrite degeneration in this assay is scored 18h after dendrite severing , so the time course we used to assay dendrite degeneration alone ( 4h , 7h , 11h ) was much finer and should have picked up any small differences in speed of degeneration without prior axon injury . As Miro reduction did not change the timing of dendrite degeneration , we performed the NP assay in control and Miro RNAi neurons . This assay was performed as diagrammed in Fig 1A’ . In control neurons , axon injury 8h before dendrite severing results in about 50% of dendrites remaining at 18h ( Fig 1B and 1D and [8] ) . In Miro RNAi neurons , NP was increased and 100% of dendrites remained at the 18h timepoint ( Fig 1C and 1D ) . RNAi targeting milton , which recruits Miro to mitochondria [20 , 21] , also increased NP ( Fig 1D ) . These results suggest that normal mitochondrial trafficking or dynamics limits injury-induced NP . To understand how mitochondria might regulate axotomy-induced NP , we compared mitochondrial shape in dendrites before and 8h post axon injury ( hpa ) . Mitochondria were labeled with mito-GFP and membranes with mCD8-RFP . The average length of mitochondria decreased significantly after injury ( Fig 2A and 2B ) . Specifically , more short ( magenta arrows Fig 2A ) and fewer long mitochondria ( orange arrows , Fig 2A ) were present at 8hpa ( Fig 2C ) . The total number of mitochondria in dendrites also increased at 8hpa ( Fig 2D ) . To determine whether mitochondrial fission was responsible for the changes in mitochondria length after axon injury , we used RNAi to target Drp1 , a dynamin-related GTPase that mediates mitochondrial fission [23] . Drp1 RNAi hairpins were expressed in ddaE neurons under control of 221-Gal4 , together with Dicer2 , mito-GFP and mCD8-RFP . One of the Drp1 RNAis ( referred to as Drp1 RNAi #2 ) dramatically elongated mitochondria in dendrites and led to clustering of mitochondria in the cell body ( S1A Fig ) . Injury-induced changes in mitochondria were not visible in this background ( S1A Fig ) . However , mitochondrial shape was so different in these cells that we looked for an alternate Drp1 RNAi line that would not have such a strong effect in uninjured neurons . In neurons expressing a different Drp1 RNAi , mitochondrial length was fairly normal in uninjured cells ( S1B Fig ) . The only significant difference was a decrease in the number of mitochondria under 0 . 5 μm , which is consistent with partial reduction of Drp1 protein levels . Although the effects in uninjured cells were subtle , this Drp1 RNAi also completely eliminated the axotomy-induced changes in length and number ( Fig 2A’–2D’ ) . Because this RNAi line eliminated injury-induced mitochondrial fission without dramatically altering baseline mitochondrial length we used it for the subsequent experiments . Mitochondrial motility was also upregulated in dendrites after axon injury , but this change was not related to Drp1-mediated fission ( S1C Fig ) . To determine whether the increase in mitochondrial fission induced by axon injury was related to downregulation of NP by mitochondria , we assayed NP in Drp1 RNAi neurons . As in Miro and milton RNAi neurons , Drp1 RNAi increased the level of protection , while overexpression of Drp1 had the opposite effect ( Fig 2E and 2F ) . Drp1 RNAi did not influence the normal time course of dendrite degeneration ( Fig 2G ) , thus Drp1 only affects degeneration after axotomy in the NP assay , but has no effect on the baseline rate of dendrite degeneration in the absence of axon injury . Drp1-mediated mitochondrial fission occurs during apoptosis in Drosophila and other organisms [24 , 25] , and mitochondria and caspases have been linked in a neurodegenerative response triggered by glial signaling [26] . In mammals and in C . elegans fission is upstream of caspase activation [27 , 28] . Because of this connection between mitochondrial fission and caspase activation , we hypothesized that caspases might also inhibit axotomy-induced NP . To test this hypothesis we expressed large RNA hairpins to target the initiator caspase Dronc and assayed both dendrite degeneration and NP . Dendrite degeneration is normally complete by 18h after severing , and blocking caspases does not alter this [22] . To test whether caspase reduction might subtly alter the timing of dendrite degeneration , we assayed earlier timepoints , and again found that the dendrite degeneration proceeded was not influenced by caspase reduction ( Fig 3A ) . However , Dronc RNAi did result in a significantly higher level of axotomy-induced NP compared to control cells ( Fig 3B and 3C ) consistent with a role for Dronc in negative regulation of NP . We also tested whether Dcp-1 and Drice , two effector caspases , inhibit axotomy-induced NP . Indeed , both RNAi and a strong loss-of-function mutant of Dcp-1 , Dcp-13 [29] , increased NP ( Fig 3C ) . Drice RNAi and heterozygous Drice17 [30] neurons also had higher NP than control , but the results were not significantly different . It was not possible to test homozygous Drice17 mutants as these animals die , so we introduced one copy of Drice17 , into heterozygous Dcp-13 mutant animals , and this significantly enhanced protection ( Fig 3C ) , indicating both effector caspases are likely to be involved in negative regulation of axon injury-induced protection . Although in C . elegans and mammals , mitochondrial fission and Drp1 act upstream of caspases [27 , 28] , in Drosophila the effector caspase Dcp-1 can regulate mitochondrial shape and function [31] . To determine whether caspases were required for injury-induced mitochondrial fission , we assayed fission in Dronc RNAi neurons . Mitochondrial length still decreased in response to axon injury in Dronc RNAi neurons ( Fig 3D ) , suggesting caspases do not act upstream of mitochondrial fission , but may be downstream as in other organisms . However , we cannot rule out that caspases and Drp1 act independently to dampen NP . Thus far we have identified mitochondrial fission and caspases as negative regulators of NP . We also wished to identify positive regulators . As Nmnat , a conserved NAD+ biosynthetic enzyme , can protect neurons from degeneration induced by long poly-Q proteins [32] and tau [33] , we tested whether it might also be involved in injury-induced NP . Indeed , Nmnat RNAi completely eliminated NP induced by axon injury ( Fig 4B ) . To test the specificity of this effect , we also assayed dendrite degeneration without prior axon injury . We did not find any changes in the timing of degeneration in the absence of prior axon injury ( Fig 4A ) despite previous association of Nmnat with dendrite stability . The previous studies were done in class IV neurons , which have much larger and more complex dendrite arbors than the neurons used here , and exhibit gradual loss of complexity over time in Nmnat heterozygotes [34] . We did not observe any differences in arbor structure in ddaE neurons , and the previous degeneration was observed over days rather than hours . To confirm a role for Nmnat in injury-induced degeneration , we assayed NP in both Nmnat heterozygous mutant animals ( Fig 4B ) . The mutant is a previously characterized null allele of Nmnat [35] . NP was eliminated in this genetic background ( Fig 4B ) , suggesting that normal levels of Nmnat are required for NP . As the phenotype in the Nmnat RNAi and heterozygous mutant animals was similar , we used an antibody to Nmnat to stain Nmnat RNAi neurons . We observed about 50% reduction in Nmnat signal in these neurons compared to control ( S2A Fig ) , consistent with partial reduction of Nmnat protein in the RNAi experiment . To determine whether the elimination of NP in animals with a partial reduction of Nmnat were due to a general inability to respond to injury , we tested whether Nmnat RNAi neurons could regenerate axons . When ddaE neurons are axotomized close to the cell body , axon regeneration proceeds by converting a dendrite into a growing axon [36] . Nmnat RNAi neurons were fully capable growing a new axon from a dendrite after a proximal axotomy , indicating the cell can mount at least one demanding injury response ( S2B Fig ) . Together , these results suggest that a partial knockdown of Nmnat in ddaE neurons does not alter the ability of the cell to sense and respond to axon injury . Therefore the loss of NP in this background is most likely due to a specific role of Nmnat in injury-induced protection . We next tested how Nmnat reduction impacts the increased stabilization that occurs when Miro , Drp1 and Dronc are reduced . We found that Nmnat RNAi completely eliminated the increase in NP caused by Miro , Drp1 and Dronc RNAi ( Fig 4C ) . This result suggests that negative regulation of NP by caspases acts upstream of Nmnat . To try to position Nmnat relative to other regulators of NP , we examined microtubule dynamics . We previously found that microtubule dynamics , specifically the number of growing plus ends , is dramatically upregulated in dendrites after axon injury in sensory neurons [29] , and that this increase in dynamics acts to stabilize dendrites against degeneration [8] . To test whether microtubules and Nmnat protect dendrites in the same pathway or parallel pathways , we labeled the growing ends of microtubules using EB1-GFP in ddaE neurons . We then compared microtubule dynamics in control and Nmnat RNAi neurons . Nmnat reduction specifically abolished the increase in microtubule dynamics at 8hpa without influencing the base-line microtubule dynamics ( Fig 4D ) . Thus the upregulation of microtubule dynamics after axon injury requires Nmnat . It seems unlikely , however , that microtubule dynamics is the sole effector of Nmnat as dampening microtubule dynamics does not block injury-induced protection as strongly or consistently as reducing Nmnat ( Fig 4B and 4C and [8] ) . In summary , our results lead to a model in which Nmnat is a central effector of NP acting upstream of microtubule dynamics and downstream of negative regulation by Drp1 and Dronc ( Fig 4E ) . As Nmnat seemed so closely linked to NP and was required for both stabilization and increased microtubule dynamics induced by axon injury , we tested whether it was sufficient to induce these responses . We therefore expressed GFP-tagged Nmnat-B-delta-N . The delta-N refers only to a difference from the cDNA used to the annotated cDNA in flybase ( see next section in results ) . In the background of Nmnat-B overexpression we severed a dendrite without prior axon injury and scored its presence 18h later . In control neurons almost no dendrites remained at this time , while in the Nmnat-expressing neurons almost all were intact ( Fig 5A ) . Because Nmnat was sufficient to protect dendrites in the absence of axon injury , we also tested whether it was sufficient to increase microtubule dynamics in uninjured neurons . As a control we expressed a soluble fluorescent protein , Kaede . Expression of either GFP-Nmnat-B-delta-N and Wlds ( mouse Nmnat1 with an additional stretch of amino acids at the N-terminus ) increased the number of growing microtubule ends in dendrites of uninjured neurons ( Fig 5B ) . Thus Nmnat is not only required for injury-induced NP , but is sufficient both for NP and the associated increase in microtubule dynamics . While increased stability is likely to help neurons to survive after axon damage , the reason for limiting stability through caspase activity is not intuitive . However , the timing of events triggered by axon injury suggested a hypothesis . Axotomy-induced NP is maximal 8-24h after axon injury in ddaE neurons ( Fig 1A and 1A’ and [8] ) , while axon regeneration typically begins 24-48h after injury in these cells [36] . We therefore hypothesized that turning down early NP might promote subsequent regeneration . To test whether uncontrolled NP might inhibit regeneration , we compared regeneration in control and Dronc RNAi neurons . In control neurons the average amount of new axon growth 96h after injury was over 200 microns , but in Dronc RNAi neurons the average growth was less than half of that ( Fig 6A ) . Dronc activity therefore promotes regeneration , perhaps by limiting axotomy-induced NP . If reduced regeneration in Dronc RNAi neurons is due to overactive Nmnat , we predict that Nmnat overexpression should lead to a similar defect in axon regeneration . To test this idea , we generated transgenic flies that encode GFP-tagged Drosophila Nmnat . Two splice forms of Nmnat exist in Drosophila . Nmnat-A contains a nuclear localization signal ( NLS ) while Nmnat-B does not . The Nmnat-B described in flybase has 31 amino acids at its N-terminus that are not encoded in any existing cDNAs; our GFP-Nmnat-B does not have these 31 amino acids , so we refer to it as Nmnat-B-deltaN . Consistent with our hypothesis , over-expression of either Nmnat isoform suppressed axon regeneration ( Fig 6A ) . In addition , over-expression of Wlds [37] , which includes mouse Nmnat1 and 70 additional amino acids [38] , had the same effect ( Fig 6A ) . HA-tagged Nmnat [35] had a similar , although not statistically significant , effect ( S3A Fig ) . These results are consistent with previous studies showing that Wlds overexpression can lead to reduced axon regeneration in a variety of cell types and contexts [39–42] . To determine whether Nmnat might act to dampen regeneration downstream of Dronc , we paired Dronc RNAi with Nmnat RNAi to see if reducing Nmnat would rescue the Dronc RNAi phenotype . To control for potential Gal4 dilution effect when expressing many UAS-driven transgenes together , we paired Dronc RNAi with a control RNAi . Indeed , the addition of the control transgene reduced the effect of Dronc RNAi on regeneration ( Fig 6 ) . However , in Dronc plus Nmnat double RNAi neurons , regeneration was significantly enhanced compared to the matched control ( Fig 6B ) . This result is consistent with Nmnat acting as a negative regulator of regeneration downstream of Dronc . Levels of regeneration in this experiment were higher than in other genetic backgrounds . It is possible that Dronc also targets positive regulators of regeneration that can increase outgrowth when Nmnat-mediated in inhibition of regeneration is reduced . Although we found Nmnat was central to NP , we did not see large changes in amount or distribution of endogenous Nmnat in ddaE neurons after injury using immunofluorescence ( S3D Fig ) . This may be because small or transient changes in levels or activity of Nmnat are sufficient to stabilize dendrites , and because endogenous Nmnat was difficult to detect . GFP-Nmnat-B-deltaN was evenly distributed in the nucleus and cytoplasm and did not change its localization in response to injury ( S3B Fig ) . In uninjured ddaE neurons , GFP-Nmnat-A was detected primarily in nuclei ( S3C Fig ) . At 8h post axon injury , the ratio of nuclear to cytoplasmic Nmnat-A signal was significantly decreased ( S3C Fig ) . In contrast , the ratio did not change in response to axotomy in Dronc RNAi neurons ( S3C Fig ) . Although we do not know the significance of the decrease in nuclear Nmnat-A relative to cytoplasmic , the fact that it is dependent on Dronc is consistent with Dronc regulating Nmnat after axon injury . There are two ways excessive Nmnat could dampen regeneration: either by generating a persistent stump that blocks regeneration or more directly from within the regenerating cell . A persistent axon stump could block or repel new axon growth , as has been demonstrated in zebrafish [42] . In the regeneration assay used here , physical block by the stump cannot be important as the new axon grows from a dendrite on the opposite side of the cell . To test whether a persistent stump might influence regeneration in some other way , we expressed the Wlds protein in ddaC neurons , which are next to ddaE neurons . This approach enabled generation of a persistent stump near a cell body that did not itself express extra Wlds or Nmnat . When we severed axons of both the Wlds-expressing cell ( ddaC ) and wild-type ddaE , the ddaC axon persisted as expected , and regrowth of the axon from a dendrite occurred normally in the ddaE neuron ( S3E Fig ) . Failure of a neighboring persistent stump to reduce regeneration is consistent with excessive Wlds or Nmnat acting cell-autonomously to dampen regeneration . Thus far we have shown that Nmnat is required for NP , that caspases limit NP , and that overactivation of NP dampens regeneration . However , there must also be positive signals that turn NP on in response to axon injury . Indeed , we previously showed that JNK is required for NP mediated by increased microtubule dynamics [8] . JNK can act downstream of DLK in initiation of axon regeneration in an injury-induced cascade that results in fos-mediated transcription in Drosophila [10] . We therefore tested whether DLK and fos played a role in NP . A trans-heterozygous combination of wnd alleles ( wnd is the name for Drosophila DLK ) , and a fos dominant negative ( fosDN ) transgene have been shown to block injury signaling [10] and regeneration [43] , so we used these tools to test for a role in NP . In both genetic backgrounds induction of NP by axon injury was completely blocked ( Fig 7A ) . In addition , fosDN blocked the increased microtubule dynamics in dendrites after axon injury ( Fig 7B ) . We conclude that the DLK/JNK/fos pathway is required for NP and its associated upregulation of microtubule dynamics . The NP that protects dendrites in sensory neurons may therefore be similar to the DLK and fos-mediated axon stabilization induced by crushing motor axons [7] . We also tested whether the fos pathway might be upstream of mitochondrial fission induced by axon injury . Unlike control neurons ( Fig 2A and 2B ) , no decrease in mitochondrial length was observed in fosDN neurons ( Fig 7C ) . Thus fos activity is required for injury-induced mitochondrial fission . This suggests fos is required both to turn on NP and to induce mitochondrial fission that limits NP . If fos is a critical regulator of NP , then its overexpression might alter the time course of dendrite stabilization . Indeed , in control neurons NP is low 48h after axon severing , but in fos overexpressing neurons it remained high ( Fig 7D ) . This is consistent with previous studies showing that fos can stabilize axons in other situations [7 , 44] . Overexpressing fos also blocked axon regeneration ( Fig 7E ) . To determine whether the inhibition of regeneration by fos was due to excessive NP , we co-expressed Nmnat RNAi with fos . As in other experiments with multiple transgenes we paired fos with a control RNAi so that transgene number was matched . Nmnat RNAi completely rescued regeneration in the fos overexpression ( Fig 7E ) . We conducted similar experiments with overexpressed bsk , the JNK homolog in Drosophila . Like fos , bsk overexpression extended protection ( Fig 8A ) , and blocked regeneration in a Nmnat-dependent manner ( Fig 8B ) . Together these results demonstrate that overexpression of fos or JNK extends the normal timing of NP and , in a Nmnat-dependent manner , reduces regeneration . Thus Nmnat is a both a positive regulator of NP and a negative regulator of regeneration that can act downstream of JNK and fos signaling .
Our results lead to a model ( Fig 8C ) in which axon injury triggers opposing responses downstream of the initial DLK/JNK/fos signaling cascade . One early output of this conserved injury response pathway is NP , a global stabilization of the parts of the neuron still connected to the cell body . The central mediator of NP is Nmnat . One Nmnat effector is the dramatic increase in microtubule dynamics observed after axon injury . As axon damage is likely to be accompanied by disturbances in the surrounding tissue , making the cell more resistant to degeneration by turning on NP may help the neuron survive the initial trauma . Fos injury signaling also triggers Drp1-mediated mitochondrial fission in the first few hours after axon injury , and this leads to dampening of NP by caspases . We envision positive and negative regulation of NP balancing one another in different ways through time after injury . Eventually the negative pathway must outweigh the positive or regeneration is dampened by persistent NP ( Fig 8D ) . It is possible that the timing of this balance shift is controlled by additional signals that report whether the environment is conducive for regeneration . This model suggests that rather than DLK/JNK/fos directly regulating regeneration , this signaling pathway kicks off a multi-step response to axon injury that includes regeneration as a relatively late event . Indeed , although this pathway is known as the conserved axon regeneration pathway , we find that it first turns on a response that inhibits regeneration . Although this idea is surprising , this model does make sense in the overall picture of neuronal injury responses and stabilization . For example , in mammals [45 , 46] and flies [10] the AP-1 transcription factor fos is activated soon after axon injury , but its role in regeneration is not as clear as that of some other transcription factors like jun . Our data suggests that this early activation could be because fos orchestrates the injury response that precedes regeneration . Our results also touch on the role of caspases in axon regeneration . A study in C . elegans demonstrated that caspases are positive regulators of axon regeneration [47] , which is surprising considering their involvement in self-destruct programs like apoptosis and dendrite pruning . We confirm that in Drosophila caspases are pro-regenerative . In addition , our data suggests that this effect is not through a direct role in regeneration , but because caspases down-regulate NP , which inhibits regeneration . A negative role for mitochondria in NP is also intriguing . Mitochondria seem to promote axonal stability [15] , and there are studies in several systems that suggest the neuroprotective effects of Nmnat or Wlds require mitochondria [17–19] . However , mitochondria can play prodegenerative roles in other contexts [26 , 48] . More specifically mitochondrial fission can promote degeneration [49] . Here we demonstrate that mitochondria , Drp1 and caspases all counteract NP , suggesting that caspase activation may regulate NP downstream of mitochondrial fission . This does not mean that mitochondria are not also positive regulators of this type of NP . Indeed the data in this study combined with others suggests that mitochondria are critical nodes for control of neuronal stability and both positive and negative regulation likely converge on them . Like mitochondria , the role of Nmnat in injury responses has been difficult to classify simply as either positive or negative . Its ability to prevent injury-induced Wallerian degeneration , as well as to act as an endogenous neuroprotective factor [50] has led to the idea that it has a purely positive influence on neuronal health . However , the myriad ways in which it can be regulated [50] suggest that it is useful only in exactly the right dose . Indeed we show that when its regulation is disrupted , Nmnat inhibits a different type of neuronal resilience: axon regeneration . Thus upregulation of Nmnat as a potential therapeutic strategy to counteract neurodegeneration could have negative outcomes due to dampened regeneration . While our experiments support the idea that endogenous Nmnat is a central regulator of neuronal stability , the way it exerts this effect remains unclear . Nmnat is an enzyme that uses ATP and NMN ( nicotinamide mononucleotide ) to make NAD+ . Protective effects of endogenous or overexpressed Nmnat have been proposed to be due to maintenance of high NAD levels [51–53] , keeping levels of the precursor NMN low [54] , acting as a chaperone [32 , 35] , and through maintaining mitochondrial integrity or function [17–19] . We now show that Nmnat also acts upstream of increased microtubule dynamics after axon injury . This Increased microtubule dynamics in response to axon injury is also seen in mammalian neurons , and so this part of the NP response is likely to be conserved [13] . Although we have previously shown increased microtubule dynamics plays a role in NP [8] , and now show that Nmnat overexpression is sufficient to increase microtubule dynamics , it is possible that Nmnat has other effectors that can mediate NP . In conclusion , we propose a model in which DLK signaling initiates key injury responses before axon regeneration begins . These responses include upregulation of Nmnat-mediated NP , microtubule dynamics and mitochondrial fission . Mitochondrial fission likely counteracts NP through caspase activation , although it is possible that mitochondria and caspases regulate NP independently . Although this early response is downstream of the core axon regeneration kinase cascade , it actually inhibits regeneration if unchecked . This multi-step model of injury responses downstream of DLK helps explain the function of caspases in promoting regeneration . We anticipate that understanding the transition between early injury responses and regeneration itself will suggest strategies for promoting axon regeneration without overactivating NP , which would , in turn , dampen regeneration . A more complete understanding of the relationship between NP and regeneration is essential to designing any therapeutic approach to either stabilize neurons or to enhance regeneration .
The following RNAi fly strains were used in this study: Rtnl2 ( 33320 ) [8] , gammaTub37C ( 25271 ) [8] , Miro ( 106683 ) [22] , milton ( 41508 ) [55] , Dronc ( 23035 ) [22] , Dcp-1 ( 107560 ) Drice ( 28065 ) [56] and Drp1 ( 44156 , referred to as #2 ) from the Vienna Drosophila RNAi Center , and Drp1 ( 27682 ) , Nmnat ( 29402 ) [18] from the Bloomington Drosophila Stock Center ( BDSC ) . All RNAi transgenes were coexpressed with UAS-Dcr2 to increase knockdown efficiency [57] . Other lines include 221-Gal4 , ppk-Gal4 , UAS-mito-GFP ( BDSC 8443 ) , UAS-EB1-GFP , UAS-mCD8-RFP , UAS-Drp1 [58] , UAS-Wlds [37] , Nmnatdelta4790-8 [35] , Drice17 [30] , Dcp-13 [29] , UAS-fosWT ( BDSC 7213 ) , UAS-bsk-A . Y ( BDSC 6407 ) , wnd1 , wnd3 , and UAS-fosDN [59] . Fly embryos were collected at 20C overnight and aged at 25C for 2 or 3 days before imaging . Two days of aging was used for all axon regeneration assays because these extend 96h , and 3 days of aging were used for all other experiments . Larvae were mounted between a slide coated with a dry agarose patch and a coverslip , which was held in place with sticky tape . A MicroPoint pulsed UV laser ( Andor Technology ) was used to injure dendrites and axons of ddaE neurons expressing EB1-GFP or mCD8-RFP under the control of 221-Gal4 . Confocal images were acquired using a Zeiss LSM510 with a 63X oil objective ( NA1 . 4 ) right after injury . Larvae were then kept in individual food caps at 20C for the indicated time periods and were then reimaged using an Olympus FV1000 confocal microscope equipped with a 60X oil objective ( NA1 . 42 ) . Maximum intensity projections were generated using ImageJ software , and were aligned and processed using Adobe Photoshop software . To measure microtubule dynamics in dendrites after injury , we imaged neurons for at least 100 frames ( 1 frame/2s ) using an Olympus FV1000 microscope with a 60X objective at zoom 3 , and counted the total number of EB1-GFP comets in a 10 μm dendrite segment close to the cell body from 3 in-focus frames . Only comets moving in 3 consecutive frames were included for quantification . The reslice tool of ImageJ was used to generate kymographs with 1 pixel spacing . In uninjured neurons , EB1-GFP-expressing neurons were imaged with a Zeiss AxioImager M2 equipped with LED illumination and an AxioCam 506 camera . A 63x 1 . 4 NA objective was used to acquire images every second . After image capture , analysis was performed in ImageJ . In each movie , the length of the comb dendrite of the ddaE neuron that was in focus was measured . EB1-GFP comets that passed through this region during the 300 seconds of the movie were counted and this number was divided by the length to get comets per length . The time ( 300s , with 1 frame per second ) was the same in all movies and so was not included in the normalization . Live imaging of mitochondria was performed by expressing UAS-mCD8-RFP and UAS-mito-GFP under the control of 221-Gal4 . Images were taken on a Zeiss LSM510 at 1 frame/s using a 63x objective and 2x zoom . Injury-induced mitochondrial behavior changes were analyzed in dendrites in a 66 . 8 μm2 region close to the cell body . The template matching plugin in ImageJ was used to minimize the effect of larval body movements . Mitochondrial length was measured along the longest dimension of mito-GFP shapes using the measure tool in ImageJ . The average length of mitochondria was calculated from at least 8 neurons , each of which contained 13–52 mitochondria in the imaging region . To further analyze changes in length , mitochondria were grouped according to the length of mito-GFP , and the percentage of each group was calculated before and after injury . ddaE neurons expressing EB1-GFP were axotomized close to the cell body and reimaged after 96h . One dendrite usually extends and converts into a new axon by 96h in response to a proximal axon injury [36] . using the NeuronJ plugin in the ImageJ software , we measured the length of the specified dendrite at 0h ( R0h ) and 96h ( R96h ) , and a nearby non-regenerating dendrite ( NR0h and NR96h ) so that normal dendrite expansion as the larva grows could be taken into account . The formula R96h-R0h*NR96h/NR0h was used to calculate growth of the new axon tip . The comb dendrite of ddaE neurons was severed close to the cell body . Degeneration speed was measured by scoring morphology of the severed dendrite at 4 , 7 , and 11 . Dendrites with no discontinuities were scored as intact , and any breaks resulted in them being scored as not intact . ddaE neurons expressing EB1-GFP were axotomized close to the cell body 8h or 48h before the dorsal comb-like dendrite was severed . Dendrite status was determined 18h post dendrite injury . In all the neurons we examined , dendrites either completely degenerate and no remnants are left or remained intact . Example images of both types of result are shown in Fig 1B . Therefore , NP is measured using the percentage of neurons with intact dendrites at 18hpd . GraphPad Prism 6 software was used to generate graphs and perform statistical analysis . A Fisher’s exact test was used to determine significance of neuroprotection assays and length distribution of mitochondria . Other types of data were tested for normal distribution using D’Agostino-Pearson normality test . If the data passed the normal distribution test , a t test or in Fig 6A , a one way ANOVA followed by Dunnett’s multiple comparison test , was used to determine statistical significance . Otherwise , a Mann-Whitney test was used . Details of the specific t test performed and sample size for each experiment are described in Fig legends . Data were plotted as mean ± standard deviation ( SD ) . * p<0 . 05 , ** p<0 . 01 , *** p<0 . 001 . The coding sequence of Drosophila Nmnat isoform A was amplified from a cDNA clone using forward primer 5’-CCGGAATTCATGATTGTGAAAATCAGCTGGCCCAAG-3’ and reverse primer 5’-ATATGCGGCCGCCTAAAGTTGCACTTGGGAAATC-3’ . The coding sequence of Drosophila Nmnat isoform B was amplified from the UAS-Nmnat . HA construct [35] using forward primer: 5’-CCGGAATTCATGTCAGCATTCATCGAGGAAAC-3’ and reverse primer: ATATGCGGCCGCTCAAGAGTCGCATTCGGTCGGAG . Both forward primers contain an EcoRI site and reverse primers contain a NotI site . The amplified sequences were cloned into a pUAST-GFP vector and the resulting constructs were injected into fly embryos to generate several transgenic flies . UAS-GFP-Nmnat-A4 and UAS-GFP-Nmnat-B-deltaN8 lines were used in this study .
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Unlike many other cell types , most neurons last a lifetime . When injured , these cells often activate survival and repair strategies rather than dying . One such response is regeneration of the axon after it is injured . Axon regeneration is a conserved process activated by the same signaling cascade in worms , flies and mammals . Surprisingly we find that this signaling cascade first initiates a different response . This first response stabilizes the cell , and its downregulation by mitochondrial fission and caspases allows for maximum regeneration at later times . We propose that neurons respond to axon injury in a multi-step process with an early lock-down phase in which the cell is stabilized , followed by a more plastic state in which regeneration is maximized .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
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2016
|
Mitochondria and Caspases Tune Nmnat-Mediated Stabilization to Promote Axon Regeneration
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SCYX-7158 , an oxaborole , is currently in Phase I clinical trials for the treatment of human African trypanosomiasis . Here we investigate possible modes of action against Trypanosoma brucei using orthogonal chemo-proteomic and genomic approaches . SILAC-based proteomic studies using an oxaborole analogue immobilised onto a resin was used either in competition with a soluble oxaborole or an immobilised inactive control to identify thirteen proteins common to both strategies . Cell-cycle analysis of cells incubated with sub-lethal concentrations of an oxaborole identified a subtle but significant accumulation of G2 and >G2 cells . Given the possibility of compromised DNA fidelity , we investigated long-term exposure of T . brucei to oxaboroles by generating resistant cell lines in vitro . Resistance proved more difficult to generate than for drugs currently used in the field , and in one of our three cell lines was unstable . Whole-genome sequencing of the resistant cell lines revealed single nucleotide polymorphisms in 66 genes and several large-scale genomic aberrations . The absence of a simple consistent mechanism among resistant cell lines and the diverse list of binding partners from the proteomic studies suggest a degree of polypharmacology that should reduce the risk of resistance to this compound class emerging in the field . The combined genetic and chemical biology approaches have provided lists of candidates to be investigated for more detailed information on the mode of action of this promising new drug class .
Human African trypanosomiasis ( HAT ) is caused by two subspecies of the unicellular parasite Trypanosoma brucei , an infection which is transmitted by the bite of a tsetse fly . HAT progresses through a haemo-lymphatic stage into a meningo-encephalitic stage [1] and has a fatality rate close to 100% if left untreated [2] . The disease is also a key factor in maintaining the poverty cycle , and patients are often discriminated against or abandoned [3] . The reporting of new cases of HAT has fallen to below 7 , 000 in 2011 [4] . However , the disease has previously resurged from even lower levels in the 1980s and 1990s [5] . Current estimates place 70 million people at risk with more than 5 million living in areas of high or very high risk for contracting HAT [4] . T . brucei gambiense is responsible for around 98% of reported cases [5] , and has been targeted by the World Health Organization for elimination by 2020 . However , elimination of T . brucei rhodesiense , which has epidemic potential , is not feasible due to its animal reservoir [5] . The tsetse fly vector also presents significant risks to disease control in that climate change may allow the vector to invade new geographical regions [6] , and sexual recombination , which occurs within the vector , could allow rapid transfer of drug resistance and virulence factors [7] . Hence , whilst improvements in control have been achieved , there are several risk factors that could lead to resurgence of the disease [8 , 9] . Existing drugs are highly unsatisfactory due to toxicity , mode of administration and efficacy [8] . The ease of developing resistance to both components of the nifurtimox / eflornithine combination therapy ( NECT , the newest treatment to enter the clinic ) [10] is also a major concern [11 , 12] . Moreover , due to its status as a neglected disease of declining incidence , the current drug discovery pipeline for HAT is far from robust [13] . Thus , development of new drugs remains a critical task . Recent advances have included the entry of fexinidazole into phase II/III trials against HAT ( ongoing ) [14] and the identification of oxaboroles as a class of compounds active against T . brucei by a collaboration between the Drugs for Neglected Disease initiative , Anacor Pharmaceuticals and SCYNEXIS [15] . One member of this class , SCYX-7158 , shown to be effective in the meningo-encephalitic stage of HAT [16] , entered phase I clinical trials in March 2012 and studies , including safety profiling , are ongoing ( DNDI diseases and projects portfolio accessed 14/08/15 www . dndi . org/diseases-projects/portfolio/oxaborole-scyx-7158] ) . Oxaborole compounds have been demonstrated to act via inhibition of leucyl RNA synthetase as anti-pneumococcal agents [17] and anti-fungal agents [18] . They can also form adducts with cis-diols in sugars and have been shown to inhibit other enzymes such as phosphodiesterases , β-lactamases and kinases ( see review [19] ) . However , the mode of action against African trypanosomes has not been determined . This information would inform the selection of appropriate partner compounds to protect against resistance , and could also open up novel areas of drug discovery . Our objective was to apply genomic sequencing and chemo-proteomic approaches [20] to facilitate mode of action studies on the oxaborole series , an approach which has been successful with other antitrypanosomal compounds [21 , 22] . Here , we report the use of two orthogonal methods ( proteomic studies using affinity chromatography and stable isotope labelling by amino acids in cell culture [SILAC]; and whole genome sequencing of sensitive and drug-resistant cell lines ) to produce lists of candidate targets for oxaborole compounds that will be pursued in future work . Our experiments suggest a high level of polypharmacology that could protect the oxaborole class from resistance emerging in the field .
All chemicals were obtained from Sigma-Aldrich ( Gillingham , UK ) unless otherwise indicated . Amino acids for SILAC labelling ( 4 , 4 , 5 , 5-D4 L-Lysine and U-13C6 L-Arginine ) were obtained from CK Gas Products ( Hampshire , UK ) . PBS was formulated in-house . Foetal bovine serum for HMI9T was obtained from PAA Laboratories ( Yeovil , UK ) , dialysed foetal bovine serum for SILAC-labelling was obtained from Life Technologies ( Paisley , UK ) . IMDM for SILAC–labelling ( lacking Arginine and Lysine ) was obtained from Thermo Scientific ( Basingstoke , UK ) . The cOmplete EDTA-free protease inhibitor cocktail was obtained from Roche Diagnostics ( West Sussex , UK ) . Bloodstream-form T . brucei ‘single marker’ cells [23] were cultured at 37°C with 5% CO2 in HMI9T medium [24] . Cells were counted using a CasyCounter model TT ( Roche Innovatis , Reutlingen ) and maintained at densities below 5×106 ml−1 , sub-culturing as necessary . EC50 determinations were carried out using a resazurin-based assay , and means weighted to the standard error calculated as previously described [25 , 26] . SILAC-labelling was carried out using an adapted HMI11 [27]; T . brucei log-phase cells in HMI9T medium were washed in PBS and seeded at 1×104 ml−1 into HMI11-SILAC + R6K4 as described previously [28] . Following 3 days growth to ~1×106 ml−1 , cells were harvested by centrifugation , washed in ice-cold PBS , and resuspended in PBS at 1 . 25×109 ml−1 . Four parts cell suspension was mixed with one part 5× lysis buffer ( 25% glycerol; 30 mM MgCl2; 4% IGEPAL CA-630 ( octylphenoxy poly ( ethyleneoxy ) ethanol ) ; 5 mM DTT ) , cOmplete protease inhibitor cocktail added to 1× concentration and the sample freeze-thawed three times . DNase-I was added to 1 μg ml−1 , the mixture incubated on ice for 5 min , vortexed for 10 s and clarified by centrifugation at 20 , 000 g for 1 h at 4°C . The supernatant was divided into 500 μl aliquots , adjusted to 5 mg ml−1 with 1 × lysis buffer , snap frozen , and stored at -80°C prior to subsequent processing . SCYX-6759 and oxaborole-1 were prepared using previously published methodology [29] . Full experimental details for the synthesis of the oxaboroles utilised in this study are given in the Supporting Information ( S1 Text ) . Beads derivatised with an oxaborole , or control compound were prepared as follows . The storage solvent was removed from commercial NHS functionalised magnetic beads ( Thermo Scientific ) and the beads washed and resuspended in anhydrous DMSO ( 150 μl [mg resin]−1 ) . The amine-containing compound ( 7 nmol [mg resin]−1 ) and DIPEA ( 14 nmol [mg resin]−1 ) were then added and the resin gently agitated for 24 h at room temperature . After which the reaction solvent was removed , and the beads washed and resuspended in anhydrous DMSO ( 150 μl [mg resin]−1 ) . Ethanolamine ( 70 nmol [mg resin]−1 ) and DIPEA ( 70 nmol [mg resin]−1 ) were then added and the resin gently agitated for 24 h at room temperature . The reaction solvent was then removed and the resin washed with DMAc , prior to storage in the same solvent . Note , the incubation step with an amine-containing compound is omitted when preparing ‘blank’ ethanolamine-capped beads . Lysates were pre-cleared by incubation with ethanolamine-capped paramagnetic beads ( 0 . 2 mg ) for 30 min at 4°C , after which the supernatant was transferred to a new sample tube along with a 50 μl wash . For competition experiments , oxaborole-1 in DMSO ( 1 μM final concentration ) or a DMSO control was added ( 0 . 5% DMSO final ) and incubated with mixing for 30 min at 4°C . Subsequently , 0 . 2 mg of oxaborole-resin ( Fig 1 ) was added to each sample and incubated for a further 60 min at 4°C . The beads were isolated using a magnet , washed twice with lysis buffer and united into a single sample tube . The beads were further washed three times with PBS , and bead-bound proteins were eluted with NuPAGE LDS buffer ( Invitrogen ) containing 50 mM DTT for 5 min at 95°C . For comparison of the oxaborole resin and control resin , pulldowns were performed in a similar manner in the absence of soluble compound . Eluted samples were subjected to electrophoresis on a NuPAGE bis-Tris 10% acrylamide gel until the dye front had entered about 1 cm into the gel . The proteins were stained with InstantBlue ( Expedeon ) , and the entire stained area excised and subjected to in-gel digestion for 18 h at 37°C with 12 . 5 μg ml−1 trypsin gold ( Promega ) in 10 mM NH4HCO3 , 10% MeCN . Tryptic peptides were recovered in 45% MeCN , 1% formic acid and lyophilized prior to analysis . Liquid chromatography tandem mass spectrometry was performed by the Fingerprints Proteomic Facility at the University of Dundee , as described previously [28] . Data was processed using MaxQuant [30] version 1 . 3 . 0 . 5 which incorporates the Andromeda search engine [31] . Proteins were identified by searching a protein sequence database containing T . brucei brucei 927 annotated proteins ( Version 4 . 0 , downloaded from TriTrypDB [32] , http://www . tritrypdb . org/ ) supplemented with the VSG221 sequence and frequently observed contaminants ( porcine trypsin , bovine serum albumin and mammalian keratins ) that contains a total of 10 , 081 protein sequences . Search parameters specified an MS tolerance of 6 ppm , an MS/MS tolerance at 0 . 5 Da and full trypsin specificity , allowing for up to two missed cleavages . Carbamidomethylation of cysteine was set as a fixed modification and oxidation of methionine residues , N-terminal protein acetylation and N-pyroglutamate were allowed as variable modifications . Peptides were required to be at least 7 amino acids in length and a MaxQuant score >5 , with false discovery rates ( FDRs ) of 0 . 01 calculated at the levels of peptides , proteins and modification sites based on the number of hits against the reversed sequence database . SILAC ratios were calculated using only peptides that could be uniquely mapped to a given protein group , and required a minimum of two SILAC pairs . To account for any errors in the counting of the number of cell numbers mixed , the distribution of SILAC ratios was normalised within MaxQuant at the peptide level so that the median of log2 ratios is zero [30] . Data were visualized using Perseus 1 . 3 . 0 . 4 ( www . perseus-framework . org ) and further information on the identified proteins was obtained from TriTrypDB [32] ( http://www . tritrypdb . org ) . T . brucei cultures ( 50 ml ) were seeded at 5×105 ml−1 in the presence of 225 nM Oxaborole-1 ( 5× EC50 ) . Samples ( 4 ml ) were taken at 0 , 14 , 20 and 28 h , collected by centrifugation at 850 g for 10 min and processed essentially as previously described [33] . Briefly , cells were washed in 1 ml PBS containing 1% FBS , the supernatant was removed and cells resuspended in the residual volume . Cells were fixed with 1 ml ice cold 70% ethanol , adjusted to 5×105 ml−1 and washed twice with PBS containing 1% FBS . Cells were resuspended in 400 μl staining solution ( PBS containing 1% FBS , 50 μg ml−1 propidium iodide , 50 μg ml−1 RNase A ) , stained for 20 min at room temperature before being analysed by flow cytometry as previously described [33] . Oxaborole-resistant T . brucei were generated in three independent flasks by sub-culturing in the presence of increasing concentrations of Oxaborole-1 . Beginning at the sub-lethal concentration of 20 nM , the process was continued until the cells were growing in 500 nM ( ~10 × original EC50 ) . Throughout the process , increasing concentrations of Oxaborole-1 were attempted once the cells were displaying cell growth and motility similar to a control grown in the absence of the drug . After 180 days , cells were cloned by limiting dilution in the presence of 500 nM Oxaborole-1 to yield independent clones from each of the three flasks . The three resistant clones were diluted 1000-fold into media without Oxaborole-1 and sub-cultured as necessary over a two month period . EC50 determinations were carried out to indicate whether resistance had been maintained in the absence of exposure to Oxaborole-1 . Genomic DNA was prepared from five T . brucei lines , i . e . the parental clone Lister 427 ( SM ) , three oxaborole-resistant clones ( clone 1 , 2 , and 3 ) , and a drug-resistance revertant clone ( clone 1R ) . For each sample , 0 . 6–2 μg of genomic DNA was used to produce standard Illumina libraries of 400–600 base pairs ( bp ) [34] . Sequencing was carried out on an Illumina HiSeq 2000 sequencer according to the manufacturer’s standard sequencing protocol and yielded 22 . 8–29 . 6 million reads of 100 bp length per library . These data sets represented a nominal sequencing coverage of the T . brucei genome ( 35Mb ) of approximately 65 . 2- to 84 . 6-fold . The Illumina data were aligned against the T . brucei brucei TREU927 reference genome [35] assembly using SMALT v0 . 7 . 4 ( http://www . sanger . ac . uk/resources/software/smalt/ ) . For variant calling , the alignment was run employing an exhaustive search ( -x ) and with parameters wordlen = 13 ( -k ) , skipstep = 1 ( -s ) , minscor = 0 . 8 ( -m ) , and insertmax = 1000 ( -i ) . To assess relative read coverage and copy number variations ( CNVs ) , the alignment runs were repeated using the above parameters with repetitive mapping ( -r ) enabled which results in read pairs with multiple equally good alignment positions being aligned to one of these locations at random . Variants were called using SAMtools v0 . 1 . 19 mpileup ( -Q 15 for baseQ/BAQ filtering ) and BCFtools [36] . To exclude the hypervariable subtelomeric regions , only variants found in the following chromosomal core regions were included in the downstream analyses: Tb927_01_v4:202 , 695–988 , 120; Tb927_02_v4:259 , 723–1 , 161 , 408; Tb927_03_v4:146 , 614–1 , 602 , 829; Tb927_04_v4:80 , 380–1 , 467 , 268; Tb927_05_v4:72 , 088–1 , 366 , 595; Tb927_06_v4:111 , 409–1 , 414 , 033; Tb927_07_v4:26 , 571–2 , 177 , 541; Tb927_08_v4:135 , 192–2 , 476 , 033; Tb927_09_v4:325 , 850–2 , 394 , 987; Tb927_10_v5:55 , 698–3 , 993 , 940; Tb927_11_01_v4:36 , 585–4 , 482 , 610 . SNP calls were further filtered for all of the following: for a minimum of 8 "high-quality" base calls ( "DP4" ) ; for a minimum phred-scale QUAL score of 20; for a maximum phred-scale likelihood of the best genotype call of 5 ( "PL1" ) ; for a minimum phred-scale likelihood of the second best genotype call of 10 ( "PL2" ) ; for a minimum strand bias P-value of 0 . 01 ( first of "PV4" ) ; for a maximum ratio of conflicting base calls for homozygous genotypes of 5%; and , for positions with a minimum and maximum read depth of three times the median read depth observed for that chromosome: for a minimum mapping quality of 20; and for a minimum distance of 10 nucleotides from the nearest INDEL call . Files in Variant Call Format ( VCF ) listing all 206 , 417 genomic positions at which any one of the five sequenced parasite lines had a variant call are available in the supplementary material ( S1–S5 Datasets ) . The illustrations showing the location of genes along chromosomal regions in Figure CNVs was generated using Web-Artemis ( http://www . genedb . org/web-artemis/ ) . The raw sequence data are available under the following accession numbers at the European Nucleotide Archive ( http://www . ebi . ac . uk/ena ) : parent: ERS136142; resistant clone 2: ERS136134; resistant clone 3: ERS136135; resistant clone 1: ERS136145; revertant clone of clone 1: ERS136137 . The raw and processed mass spectrometry data have been deposited with the ProteomeXchange Consortium [37] ( http://www . proteomexchange . org/ ) via the PRIDE partner repository under the identifier PXD002848 .
The synthesis of SCYX-6759 and Oxaborole-1 was readily achieved using published procedures [29] . Initial attempts to immobilise the oxaborole scaffold involved the direct attachment of biotin ( for use in conjunction with a streptavidin resin ) to the aniline functionality to give Oxaborole-2 ( Fig 1 and S1 Fig ) . However , subsequent biological assay demonstrated that Oxaborole-2 was only weakly active against T . brucei ( EC50 15 μM ) compared to SCYX-6759 ( EC50 0 . 16 μM ) , Oxaborole-1 ( EC50 0 . 064 μM ) or SCYX-7158 ( EC50 0 . 79 μM , data from [16 , 38] ) . Therefore , a small number of analogues retaining the benzamide functionality of SCYX-6759 and SCYX-7158 were prepared ( one of which is shown in S2 Fig ) . Oxaborole-3 , which contains a polyethyleneglycol linker in the meta position of the benzamide was found to retain activity in the bloodstream form T . brucei assay ( EC50 0 . 086 μM ) . The carbamate protecting group of Oxaborole-3 was subsequently removed and the resultant primary amine reacted to prepare an amide of biotin ( Oxaborole-Biotin , S2 Fig ) , which in this case was found to be bioactive ( EC50 0 . 40 μM ) . An analogue of SCYX-6759 , where the oxaborole bicycle was replaced with a phthalide bicycle ( Control-1 ) was prepared and found to be inactive against T . brucei . Therefore , a control biotin conjugate ( Control-Biotin ) was prepared in an analogous fashion to Oxaborole-Biotin ( S3 Fig ) . Pilot chemical proteomics studies suggested that the use of a biotin-conjugate/streptavidin bead system was sub-optimal . As a result , the linker containing oxaborole and control analogues were instead attached to paramagnetic beads via an amide linkage to give an Oxaborole-Resin and a Control-Resin respectively ( Fig 1 and S2 and S3 Figs ) . In order to directly profile the proteins that bound to the Oxaborole-Resin , chemical proteomic profiling was undertaken using two orthogonal strategies that utilised SILAC quantitation to eliminate non-specific binding proteins . In these experiments parasites are grown in identical media where one contains “light” and the other “heavy” amino acid isotopes ( in this case arginine and lysine ) . After several rounds of cell division , the two populations are identical except for the differential labelling of the proteome with either light or heavy isotopes . After undergoing differential processing , the two samples are combined and the ratio of heavy to light peptide from each individual protein determined by mass spectrometry . Proteins that are specifically enriched by the differential treatment will have a heavy to light ratio not equal to 1 , whereas proteins affected equally with have a ratio = 1 ( or binary logarithm of 1 = zero ) . In the first strategy , the profile of proteins from T . brucei cell lysates that bind the beads in the presence ( heavy label ) or absence ( light label ) of soluble inhibitor was quantified ( Fig 2 ) . Non-specific binders will be unaffected by the presence of soluble compound , thus will produce an equal heavy to light ratio ( log2 H/L = 0 ) . In contrast , the specific binders will bind Oxaborole-1 in the pre-incubation step , making them unavailable to bind to the immobilised oxaborole , resulting in a low heavy to light ratio ( log2 H/L < 0 ) ( Fig 2A , upper pair ) . In the second strategy , an inactive Control-Resin was prepared ( Fig 1 and S3 Fig ) . The profile of proteins from T . brucei cell lysates that bind the Control-Resin ( heavy label ) or Oxaborole-Resin ( light label ) can then be quantified ( Fig 2 ) . Proteins that bind non-specifically , or whose binding is not related to activity , produce an equal heavy to light ratio ( log2 H/L = 0 ) , whereas proteins whose binding correlates with activity will have a low heavy to light ratio ( log2 H/L < 0 ) ( Fig 2A , lower pair ) . The results of the two orthogonal strategies are shown in Fig 2 and Table 1 , with the full data presented in the supplementary material ( S1 Table ) . The binding of a subset of proteins was prevented by the presence of the soluble compound ( Fig 2B ) , with 42 proteins displaying greater than a four-fold reduction in binding ( log2 H/L < -2 ) . In the orthogonal strategy , a subset of 24 proteins displayed greater than a four-fold reduction in binding ( log2 H/L < -2 ) to the Control-Resin compared to the Oxaborole-Resin ( Fig 2C ) . Comparing the profile of the proteins quantified in both experiments revealed a strong correlation ( Pearson 0 . 841 ) between the proteins that are displaced by Oxaborole-1 and those that bind only the Oxaborole-Resin and not the Control-Resin ( Fig 2D ) . The 14 proteins that display greater than four-fold selectivity in each experiment , presented in Table 1 , can be considered to be specific targets of Oxaborole-1 . The number of specific targets identified and the lack of discernible commonality strongly suggest that oxaboroles display considerable polypharmacology , and provide too great a number to investigate systematically as individual targets . FACS analysis of T . brucei cells incubated with 225 nM Oxaborole-1 ( 5× EC50 ) indicated a statistically significant increase in the proportion of G2 and >G2 cells compared to the untreated control ( Fig 3 ) . These increases probably result from re-replication of DNA in the absence of cytokinesis . DNA re-replication has been seen in a variety of mutant T . brucei cell lines; however , it is possible that the cytokinesis defect is an indirect effect [40] . Indeed perturbation of several processes results in inhibition of cytokinesis including flagellar attachment , GPI biosynthesis , Golgi duplication and kinetoplast duplication [41 , 42] . Given such an impact on DNA fidelity , we wanted to investigate the genomic effects of resistance to the oxaborole . In order to investigate the ease with which resistance to the oxaborole could occur , we generated three independent clones of T . brucei able to sustain growth in 500-nM Oxaborole-1 ( Fig 4A ) . This process took 180 days for all three cell lines to achieve the target of growth in 500 nM Oxaborole-1 . A single clone was chosen for each resistant line , and sensitivity to the oxaborole measured by EC50 ( Fig 4B ) . The resulting EC50 shifts were between 5–8 fold compared to the sensitivity of the parental cell line to Oxaborole-1 . A similar process using nifurtimox generated T . brucei able to grow in >20× EC50 after 140 days [11] , although after cloning , the shift in sensitivity to nifurtimox was 8-fold . T . brucei resistant to eflornithine , pentamidine and the methionine tRNA synthetase inhibitor 1433 , have all been generated to grow at 32× their EC50 concentrations within 120 days or less [43] . Whilst a major motive for investigating mode of action was to aid the protection of the oxaborole class from resistance in the field , these results indicate greater resilience to resistance to the oxaborole class than drugs currently used in the field as well as compounds in development . Following two weeks incubation in the absence of Oxaborole-1 , an EC50 determination showed loss of resistance from cell line 1 ( EC50 value of 123 ± 12 nM compared to 530 ± 21 nM ) . This cell line was cloned by limiting dilution and EC50 determinations were carried out on five clones . All of the clones showed loss of resistance , the clone with greatest loss of resistance ( termed clone 1R ) had an EC50 value of 83 ± 2 nM ( weighted mean of two determinations ) . Culture of resistant cell lines 2 and 3 failed to show any loss of resistance over eight weeks in the absence of Oxaborole-1 . The apparent greater instability of resistance in resistant cell line 1 was consistent with problems encountered when attempting to revive frozen cells . Stabilated cells revived from resistant cell lines 2 and 3 grew at the same rate as the parental cell line . However , cells from resistant cell line 1 showed little motility , although there were no abnormalities in gross morphology by light microscopy . After 7–14 days growth was regained , however after a single passage and three days of growth to select for healthy cells , resistance had been lost . This suggests at least two routes of resistance , an unstable mechanism and one or more stable mechanisms . Greater stability could be conferred by a gene segment being totally lost rather than silenced , or genomic amplification carrying a significant fitness cost compared to one with no such cost . To identify genetic determinants that may be involved in drug resistance to the oxaborole class , we sequenced the genomes of the susceptible parental strain Lister 427 , the three drug-resistant clones and the revertant cell line . We found striking copy number variations ( CNVs ) between the parasite clones , ranging from apparent whole chromosome duplications to CNVs affecting regions of approximately 5 kb to 15 kb in length ( Fig 5 ) . For example , chromosome 1 occurs in three instead of the usual two copies in the genome of clones 1 and 1R ( Fig 5A ) , while chromosome 4 displays an elevated copy number in clone 2 ( Fig 5B ) . In addition , a short region of chromosome 4 of approximately 5 . 5 kb is further duplicated in this cell line ( Fig 5C ) , thereby providing further complete copies of the two genes CPSF3 ( a putative cleavage and polyadenylation specificity factor subunit , Tb927 . 4 . 1340 ) and glx2-2 ( a glyoxalase , Tb927 . 4 . 1350 ) ( Table 2 ) . In contrast , two short regions on chromosome 6 and chromosome 10 in drug-resistant clone 3 have lost one of their two alleles ( Fig 5D and 5E ) . Interestingly , the deleted regions are flanked in both cases by shorter regions with nearly 100% sequence identity: on chromosome 6 the central , deleted region of 5 . 1 kb is flanked by two near-identical regions of approximately 4 . 8 kb each , whereas on chromosome 10 it is a central region of 12 . 9 kb that is flanked by two near-identical regions of approximately 3 . 1 kb each ( Fig 5D and 5E ) . This suggests homologous crossover as the mechanism of DNA deletion in these cases . These deletions directly affect over a dozen genes ( Table 2 ) and render the affected regions hemizygous , an observation that is confirmed by the loss of a second allele in the genotype of some of these genes ( S2 Table ) . The comparison of genotype assignments between the drug-resistant lines and those of the parental strain uncovered in total 78 single nucleotide polymorphisms ( SNPs ) in 66 genes . Of these , 41 in 38 genes are predicted to result in non-synonymous amino acid changes that could potentially contribute to the observed drug resistance phenotypes ( S2 Table ) . Only one SNP was common to all four clones ( receptor-type adenylate cyclase GRESAG 4 , putative ) , but no SNP was common to all 3 resistant clones and absent from the revertant clone , as might be expected if a single point mutation in a single gene was responsible for resistance . Likewise , the small ubiquitin-related modifier ( SUMO ) contained different SNPs in all 4 clones . SUMOylation regulates a wide variety of cellular processes , including transcription , mitotic chromosome segregation , DNA replication and repair and ribosomal biogenesis [44 , 45] and offers an attractive explanation for the chromosomal abnormalities described above . Knock down of SUMO in procyclic forms of T . brucei results in arrest in G2/M phase of the cell cycle as observed here [46] . In the case of clone 3 , the SNP results in replacement of the initiator methionine residue with an isoleucine . Inspection of the flanking region of SUMO revealed no upstream in-frame methionine and the next downstream methionine is at residue 50 in this 114 amino acid protein . Based on the solution structure of T . brucei SUMO [47] , the truncated protein is likely to be non-functional . In the other two clones , the SNPs are located at the C-terminus of the 114 residue peptide close to ( Ala101Gly , clone 2 ) or adjacent to ( Thr106Ile , clone 1 ) the site of cleavage by ULP1/SENP which reveals a C-terminal di-glycine motif required for activation of SUMO by the E1 activating complex [45] . Ala101 maps to a region predicted to interact with the SUMO conjugating enzyme E2 ( Ubc9 ) [47] , but it is difficult to predict whether or not such a conservative substitution with a glycine would significantly alter the interaction of the enzyme with its substrate . Cells expressing Thr106Arg or Thr106Lys SUMO mutants have been used in a proteomic study to successfully identify SUMO targets in T . brucei [48] so it appears possible that an isoleucine would also be tolerated at this position . Furthermore , this SNP is retained in the revertant clone , suggesting this mutation is not involved in resistance . Nevertheless , it is possible that different resistance mechanisms may have arisen in each of these lines . None of the genes potentially involved in SUMOylation [49] were found in common with either our proteomic or our genomic studies . However , of the 44 proteins identified as SUMOylated in a previous study [48] , one gene Tb927 . 4 . 1330 ( DNA topoisomerase 1B , large subunit ) was identified as duplicated in resistant clone 2 ( Table 2 ) . This 90 kDa protein has 4 SUMOylation sites [48] and forms a functional heterodimer with a 36 kDa catalytic subunit and is essential for growth of the parasite [50] . However , it is noteworthy that topoisomerase-IIα , a SUMOylated protein in other organisms , is essential in bloodstream form T . brucei for centromere-specific topoisomerase cleavage activity [48] , but was not present in any of our candidate lists Another candidate in the genome sequencing data , T . brucei homoserine kinase , has recently been studied in our laboratory in relation to de novo synthesis of threonine [51] . The recombinant enzyme was completely insensitive to inhibition by Oxaborole-1 ( up to 50 μM ) and therefore is not the target for this compound . To investigate if the deletion of a copy homoserine kinase ( CNV in resistant line 3 , Table 2 ) was implicated in resistance , the sensitivity to Oxaborole-1 in wild-type ( WT ) , single knockout ( SKOPAC ) and double knockout ( DKO ) bloodstream forms was determined . The resulting EC50 values were all within experimental error of each other ( 41 . 1 ± 1 . 6 , 36 . 1 ± 1 . 3 and 37 . 5 ± 1 . 5 nM for WT , SKOPAC and DKO , respectively ) . Taken together , we can conclude that HSK is neither the target nor a resistance determinant for oxaborole compounds . This list of candidate mode of action genes , affected either by CNVs or the presence of SNPs , is too long to be systematically investigated . Since no genes are common to both the proteomic studies and the resistant studies , genes involved in resistance mechanisms would appear to be distinct from candidate proteins implicated in the mode of action . In addition , the genomic variations we have observed could be the result of either oxaborole exposure or an unidentified resistance mechanism resulting in a general loss of DNA fidelity . In conclusion , genetic analysis of laboratory-generated resistant lines has been an effective technique when the field can be narrowed to particular genes of interest as in the case of resistance of T . brucei to tRNA synthetase inhibitors resulting from overexpression of the target [43] . However , taking an unbiased whole genome sequencing approach alongside the analysis of oxaborole-binding proteins in the current study , has revealed too many candidates to embark on a systematic appraisal . Our SILAC-based analysis suggests considerable polypharmacology consistent with the unusually long time taken to develop resistance , apparent multiple routes to resistance and lack of stability in at least one of those routes . It should be borne in mind that resistance and/or mode of action may involve several candidates acting in concert . The surprising number of large-scale genomic aberrations in our resistant cell lines ( Fig 5 ) , and the accumulation of cells in G2/>G2 ( Fig 3 ) suggest DNA fidelity as an area of specific interest . The presence of SNPs in the gene for SUMO ( S2 Table ) is particularly striking as its repertoire of targets includes proteins involved in chromatin structure and DNA repair [52] . Future work will involve selecting candidates to test by protein modulation and sensitivity to oxaboroles in the whole cell .
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The mode of action of a new class of boron-containing chemicals ( the oxaboroles ) , currently under development for the treatment of human African trypanosomiasis , is unknown . Here we identify a number of potential candidate proteins that could be involved either in the mode of action of these compounds or in the mechanism of resistance . This information could prove critical in protecting the compounds against resistance emerging in the field as well as opening up new avenues for drug discovery .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"and",
"Discussion"
] |
[] |
2015
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Genomic and Proteomic Studies on the Mode of Action of Oxaboroles against the African Trypanosome
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Biological networks entail important topological features and patterns critical to understanding interactions within complicated biological systems . Despite a great progress in understanding their structure , much more can be done to improve our inference and network analysis . Spectral methods play a key role in many network-based applications . Fundamental to spectral methods is the Laplacian , a matrix that captures the global structure of the network . Unfortunately , the Laplacian does not take into account intricacies of the network’s local structure and is sensitive to noise in the network . These two properties are fundamental to biological networks and cannot be ignored . We propose an alternative matrix Vicus . The Vicus matrix captures the local neighborhood structure of the network and thus is more effective at modeling biological interactions . We demonstrate the advantages of Vicus in the context of spectral methods by extensive empirical benchmarking on tasks such as single cell dimensionality reduction , protein module discovery and ranking genes for cancer subtyping . Our experiments show that using Vicus , spectral methods result in more accurate and robust performance in all of these tasks .
Networks are a powerful paradigm for representing relations among objects from micro to macro level . It is no surprise that networks became a representation of choice for many problems in biology and medicine including gene-gene and protein-protein interaction networks [1] , diseases [2] and their interrelations [3] , cancer subtyping [4] , genetic diversity [5] , image retrieval [6] , dimensionality reduction [7 , 8] and many other applications . Computational biologists routinely use networks to represent data and analyze networks to obtain better understanding of patterns and local structures hidden in the complex data they encode . One of the most standard graph-based methods to analyze networks is to decompose it into eigenvectors and eigenvalues , i . e . apply spectral methods to the network to understand its structure . At the heart of spectral methods is the so-called Laplacian matrix . Spectral clustering relies on the fact that the principle eigenvectors of the Laplacian capture membership of nodes in implicit network clusters . This principle is essential to clustering and dimensionality reduction . The traditional formulation of the Laplacian captures the global structure of the matrix , which is often insufficient in biology where local topologies are what needs to be sought and exploited . Moreover , recently algorithms designed to capture the local structure of the data have been shown to significantly outperform global methods [9 , 10] . These approaches aim to reconstruct each data point using its local neighbours and have been shown to be robust and powerful for unweighted networks . Weighted networks are richer representations of underlying data than unweighted networks: in biological networks weights can represent the strength of interactions or the strength of the evidence underlying each interaction , in patient networks weights represent the degree of similarity between patients [4] . In this paper , we provide a local formulation of the Laplacian for weighted networks which we call the Vicus matrix ( V + ) , from the Latin word ‘neighborhood’ . Using Vicus in place of the Laplacian allows spectral methods to exploit local structures and makes them a lot more relevant to a variety of biological applications . In this paper we introduce Vicus and compare its performance to the Laplacian across a wide range of tasks . Our experiments include single cell dimensionality reduction , protein module discovery , feature ranking and large scale network clustering . Since we consider such a diverse set of biological questions , in each case we also compare to appropriate state-of-the-art methods corresponding to each question . Spectral clustering using Vicus outperforms competing approaches in all of these tasks . Our experiments show that Vicus is a more robust alternative to traditional Laplacian matrix for network analysis .
In this section we consider predetermined 2D and 3D structures , represent them as a graph and analyze the performance of local Vicus as compared to traditional Laplacian in the task of graph-based dimensionality reduction . First , let us consider a particular type of protein fold that has a complex structure in which four pairs of antiparallel beta sheets , only one of which is adjacent in sequence , are wrapped in three dimensions to form a barrel shape . This structure known as jelly roll or Swiss roll is particularly common in viral proteins and is schematically depicted in Fig 1A . Spectral methods assume that clusters of data points can be well described by the Euclidean distance . Though it looks relatively unambiguous to a human , this task is computationally challenging since the assumption that Euclidean proximity translates to similarity does not hold in the original data space for the Swiss roll structure . As expected , standard spectral decomposition fails to find a lower dimensional representation of the data due to the inability to capture the underlying manifolds in Fig 1A . Using Vicus in place of the Laplacian matrix helps spectral decomposition to transform the original data to the latent space with reduced complexity while preserving the contiguity and the cluster memberships of the original data . Another simulation that we considered is a typical example in bioinformatic imaging , structured 3D data . A schematic of clustered signal within brain regions and connecting channels between them is captured in Fig 1B . Given five random non-overlapping clusters in 3D space connected by sparsely measured channels , Vicus maps the clusters into dense points while preserving the lines connecting them . This embedding indicates that , by considering local structures , local spectrum can highlight the obvious cluster structures without disregarding the structure of the data between clusters . By comparison , Laplacian-based embedding highlights the dense clusters while making the connectivity between them more ambiguous ( Fig 1B ) . This example sheds light on how Vicus can preserve local structure of the data . A very common structure in protein folding is a helix . Among such foldings are toroidal helices , where the helix is wrapped around a toroid . These structures have a pore in the middle that allows unfolded DNA to pass through . The toroidal helix in Fig 1C has a circle as its basic geometric shape . Our local spectrum recovers the underlying 2D circle by considering the labels in local neighborhoods while the Laplacian finds a circle distorted by similarities of points in the 3D dimension . The distortions by the global spectrum result from a fundamental limitation in descriptive power of Euclidean distance in high dimensional spaces , while our local spectrum can avoid such limitation by focusing on the local rather than the global manifold structures . Our final example is the task of sampling in 3D space , such as sampling an image of a cell shape in a cell morphology study . We sampled points from a solid bowl-shaped figure ( Fig 1D ) non-uniformly: the top of the bowl is more densely sampled , gradually reducing sampling towards the bottom of the bowl graph . The Laplacian based 2D embedding has considerable bias towards the densely sampled region while Vicus’ embedding recognizes that sampling was done on a solid shape , again by capturing the labels in the local neighbourhoods . These examples show the benefits of capturing local structure in a network ( graph ) decomposition , which gives a better understanding of patterns and neighborhoods hidden in complex networks . Single-cell RNA sequencing ( scRNA-seq ) technologies have recently emerged as a powerful means to measure gene expression levels of individual cells [11] . Quantifying the variation across gene expression profiles of individual cells is key to the dissection of the heterogeneity and the identification of new populations among cells . The unique challenges associated with single-cell RNA-seq data include large noise in quantification of transcriptomes and high dropout rates , therefore reducing the usability of traditional unsupervised clustering methods . Vicus , employing local structures hidden in high-dimensional data , is able to tackle these challenges and improve many types of single-cell analyses including visualization , clustering and gene selection . We benchmark our method on four recently published single-cell RNA-seq datasets with validated cell populations: The main reason we chose these four single-cell datasets is that their ground-truth labels have been validated either experimentally or computationally in their original studies . We formulate the problem of clustering cells from RNA-seq data in terms of networks . First , cell-to-cell similarity networks ( Materials and methods ) are constructed from single-cell RNA-seq data . The advantage of using networks to represent this data are in network’s ability to capture a set of relationship between all pairs of cells . After the construction of cell-to-cell networks , we can apply our Vicus to obtain a low-dimensional representation that contains local structures in the networks and potential cluster memberships of cells . To demonstrate the representative power of the low-dimensional representations by Vicus , we ran t-SNE [16] , the most common visualization method in single-cell studies , on the obtained low-dimensional representations and compare the 2-D visualization of both Vicus and Laplacian across the four single-cell datasets in Fig 2 . Note that we are only using t-SNE for the purpose of visualization of Laplacian and Vicus . The cells , color-coded by the ground-truth labels from original studies [12–15] , are clearly separated by Vicus ( Fig 2 ) , indicating greater power of Vicus to capture fine-grained structures in cell-to-cell similarity networks . We compare spectral decomposition using Vicus with spectral methods using traditional global Laplacian along with 6 other popular dimensionality reduction methods . The six methods include linear methods such as Principle Component Analysis ( PCA ) , Factor Analysis ( FA ) , and Probabilistic PCA ( PPCA ) and nonlinear methods such as multidimensional scaling ( MDS ) , Kernel PCA , Maximum Variance Unfolding ( MVU ) , Locality Preserving Projection ( LPP ) and Sammon mapping . We use a widely-used toolbox [16] implementing all these popular dimensionality reduction methods . Further , we also compare Vicus with three widely used state-of-the-art network-based clustering algorithms: InfoMap [17] , modularity-based Louvian [18] , and Affinity Propagation ( AP ) [19] . To compare these 11 methods we adopted two metrics: Normalized Mutual Information ( NMI ) [20] and Adjusted Rand Index ( ARI ) [21] ( Materials and methods ) , evaluating the concordance of obtained label and the ground-truth . Higher values of these evaluation metrics indicate better ability of correctly identifying cell populations . Results in Table 1 illustrate Vicus’ superior performances compared to all ten other methods in most of the considered cases . It is noticeable that Vicus outputs much better module detection results than all other methods on Buettner data set [14] . This is due to the fact that Buettner data set [14] contains cells in three different continuous cell stages which are hard to detect due to large noise . In addition , PCA performs the best on Pollen data set [12] because the ground-truth is obtained by simple clustering with PCA on a set of pre-selected genes . Further , compared with the three network-based module detection methods ( InfoMap , Louvian and AP ) , our Vicus is able to achieve much more accurate module discovery on each of the same networks . One of the major challenges in single-cell analysis is to detect rare populations of cells from noisy single-cell RNA-seq data . The signals of rare populations can be easily neglected due to the existence of various sources of noises . Our approach based on Vicus matrix is able to discover weak signals of rare populations by exploiting local structures while global Laplacian fails . We applied our method on a scRNA-seq data consisting of 2700 peripheral blood mononuclear cells ( PBMC ) . It is generated by 10x Genomics GemCode platform , a droplet-based high-throughput technique and 2700 cells with UMI counts were identified by their customized computational pipeline [22] . This cell population includes five major immune cell types in a healthy human as well as a rare population of metakaryocytes ( less than 0 . 5% abundance in PBMC ) . The processed data is available in [11] and was originally published in [22] . Vicus captures the rare population consisting of 11 cells ( Fig 3A ) while global Laplacian fails to find such rare population . Vicus is also able to detect differential genes that define each cluster ( Fig 3B ) . Note that we used Vicus score to rank important genes ( Materials and methods ) and we only show top 5 genes for each cluster . Identification of functional modules in Protein-protein interaction ( PPI ) networks is an important challenge in bioinformatics . Network module detection algorithms can be employed to extract functionally homogenous proteins . In this application , first submodules are detected and subsequently these submodules are investigated for enrichment of proteins with a particular biological function . Stability is one of the essential goals of the multi-scale module detection problem [23] . It measures how robust the employed algorithm is able to recover the most dense subnetworks enriched to certain biological functions or physical interactions . Inside the definition of stability ( Materials and methods ) , the Laplacian is used in a Markov process on the network which allows to compare and rank partitions at each iteration . To analyze the stability of our method we partition a Protein-Protein Interaction ( PPI ) network , which consists of 7 , 613 interactions between 2 , 283 Escherichia coli proteins [24] . This task is more challenging than traditional clustering problems due to the intrinsic complexity of the cell captured by the PPI network . Due to large noise in experimental measurements of protein interactions , proteins in the same pathway do not necessarily have higher density of interactions . This fact poses particular challenges to traditional network partition algorithms which usually fail to infer the true membership of proteins to their underlying pathways . Vicus-based spectrum exhibits higher stability along the Markovian timeline ( Fig 4A ) compared to the global Laplacian . Global spectrum and Vicus-based local spectrum exploit different modes of variation in the network ( Fig 4B ) . Global spectrum tends to find large components in networks to reduce the variation and increase stability while local spectrum exploits deeper substructures of the large components and detects partitions in more fine-grained fashion . One of the holy grails of computational medicine is identification of robust biomarkers associated with the phenotype of interest . Here we consider the question of identifying genes associated with cancer subtyping in 5 cancers from 6 microarray datasets . These are benchmark datasets for feature selection in computational biology from http://featureselection . asu . edu/datasets . php . Table 2 shows the statistics of these six datasets . In the standard formulation of spectral clustering , the ranking of features ( in this case , genes ) is done using Laplacian score . Laplacian Score is a score derived based on the network spectrum that is commonly used to rank features in the order of their importance and relevance to the clusters . Given a feature f , the corresponding Laplacian Score ( LS ) is defined as follows: L S ( f ) = f T L + f f T f . ( 1 ) Unfortunately , LS has difficulty identifying features that are only relevant to one of the clusters ( a certain local subnetwork ) but not the whole network . Traditional LS will prefer features that are globally relevant to all the clusters , even if they are not as strongly indicative of any cluster in particular . We thus , propose to substitute the Laplacian matrix L + with our Vicus matrix V + . We define our Vicus Score ( VS ) analogously to Laplacian Score: V S ( f ) = f T V + f f T f . ( 2 ) For each data set presented in Table 2 , we rank the features by Laplacian Score and Vicus Score . We take N highest ranked features and then apply simple k-means clustering . If the feature ranking algorithm correctly ranks the relevant features , the clustering accuracy should be higher compared to the accuracy of the method that uses the same number of chosen but less relevant features . We varied the number of chosen features and plotted the accuracy of the ranking algorithms in Fig 5 . Again , we use NMI and ARI as the evaluation metrics for the clustering results . We observe that features ranked using the Vicus matrix result in better accuracy when the number of chosen features is small , confirming that the most discriminative features are ranked among the top by Vicus .
The power of local network neighborhoods has become abundantly clear in many fields where the networks are used . Principled methods are needed to take advantage of the local network structure . In this work we have proposed the Vicus matrix , a new formulation that shares algebraic properties with the traditional Laplacian and yet improves the power of spectral methods across a wide range of tasks necessary to gain deeper understanding into biological data and behavior of the cell . Taking advantage of the local network structure , we showed improved performance in single cell RNA-seq clustering , feature ranking for identifying biomarkers associated with cancer subtyping and dimensionality reduction in single cell RNA-seq data . Further , we have shown that our method is amenable to parallelization which allows it to be performed in time comparable to the traditional methods .
Suppose we have a network G = { V , E } with a set of V nodes and E weighted edges . Let W ∈ R | V | × | V | be the weighted ajacency matrix of this network , where |V| is the number of nodes . Here , Wij represents the weight of the edge between the ith and jth nodes . Let diagonal matrix D be W’s degree matrix , where D i i = ∑ j | V | W i j . The classical formulation of the Laplacian of W is then matrix L = D - W also known as the combinatorial Laplacian . A common variant of the Laplacian L is L + = I - D - 1 / 2 W D - 1 / 2 which is called the normalized Laplacian . Traditional state-of-the-art spectral clustering [25] aims to minimize RatioCut , an objective function that effectively combines MinCut and equipartitioning , by solving the following optimization problem: min Q ∈ R n × C T r a c e ( Q T L + Q ) s . t . Q T Q = I . ( 3 ) where C is the number of clusters , n is the number of nodes and Q = [q1 , q2 , … , qC] is the set of eigenvectors , capturing the structure of the graph . Eigenvectors associated with the Laplacian matrix of the weighted network are used in many tasks ( e . g . , face clustering , dimensionality reduction , image retrieval , feature ranking , etc ) . These eigenvectors suffer from some limitations . For example , the top eigenvectors , in spite of their ability to map the data to a low-dimensional space , are sensitive to noisy measurements and outliers encoded by pairwise similarities ( S1 Fig ) [4] . Additionally , the Laplacian is very sensitive to the hyper-parameters used to construct the similarity matrices ( Materials and methods , S2 Fig ) [25] . Our Vicus Matrix ( V + ) is similar to the Laplacian ( L + ) in functionality and in addition captures the local structure inherent in the data . The intuition behind Vicus is that we use local information from neighboring nodes , akin to label propagation [26] or random walks [27] . As we demonstrate , relying on local subnetworks makes the matrix more robust to noise , helping to alleviate the influence of outliers . Let our data be a set of points {x1 , x2 , … , xn} . Then , each vertex vi , in the weighted network G , represents a point xi and N i represents xi’s neighbours , not including xi . We constrain the neighbourhood size to be held constant across nodes ( i . e . , ∥ N i ∥ = K , i = 1 , 2 , … , n ) . Our main assumption is that the labels ( such as cluster assignments 1 … C for C clusters ) of neighbouring points in the network are similar . Specifically , we assume that the cluster indicator value of the ith datapoint ( xi ) can be inferred from the labels of its direct neighbors ( N i ) . First , we extract a subnetwork G i = ( V i , E i ) such that V i = N i ∪ x i and E i = E ( V i ) which represents the edges connecting all the nodes in Vi . The similarity matrix associated with the subgraph G i is W i = W ( E i ) , representing the weights for all the edges associated with all the nodes in Vi . Using the label diffusion algorithm [28] , we can reconstruct a virtual label indicator vector p V i k such that p V i k = ( 1 - α ) ( I - α S i ) - 1 q V i k , 1 ≤ k ≤ C , ( 4 ) where α is a constant ( 0 < α < 1 , empirically set to 0 . 9 in all our experiments , as suggested in [28] ) and q V i k is the scaled cluster indicator vector of the subnetwork G i . Si represents the normalized transition matrix of Wi , i . e . , S i ( u , t ) = W i ( u , t ) ∑ l = 1 K + 1 W i ( u , l ) . Note that we do not actually perform any diffusion , since our setting is completely unsupervised . Instead we use pk to estimate qik . p V i k is a vector of K + 1 elements , where q ^ i k = p V i k [ K + 1 ] is the estimate of how likely datapoint i belongs to cluster k based on its neighbours . As we want maximal concordance between q ^ i k and q i k , we set q ^ i k = β i q V i k , where β i ∈ R K + 1 is the row of the matrix ( 1 − α ) ( I − αSi ) −1 , representing label propagation at its final state . Here , βi represents the convergence of the label propagation for the datapoint i ( Note that the original matrix was constructed as the concatenation of the neighborhood of i and datapoint i as the last row ) . Hence q ^ i k ≈ β i [ 1 : K ] q N i k 1 - β i [ K + 1 ] ; ( 5 ) where βi[1: K] represents the first K elements of βi and βi[K + 1] is the K + 1st element in βi , corresponding to the ith datapoint . We can construct a matrix B , that represents a linear relationship q ^ k ≈ B q k , ( k = 1 , … , C ) , such that B i j = { β i [ j ] 1 - β i [ K + 1 ] if x j ∈ N i and x j is the j -th element in N i 0 otherwise ( 6 ) Our objective is to minimize the difference between q ^ k and qk: ∑ i = 1 n ∑ k = 1 C ( q ^ i k - q i k ) 2 = ∑ k = 1 C ∥ q k - q ^ k ∥ 2 ≈ ∑ k = 1 C ∥ q k - B q k ∥ 2 = T r a c e ( Q T ( I - B ) T ( I - B ) Q ) ( 7 ) Setting V + = ( I - B ) T ( I - B ) , we arrive at our novel local version of spectral clustering: min Q ∈ R n × C T r a c e ( Q T V + Q ) s . t . Q T Q = I . ( 8 ) Similarly to the original spectral clustering formulation ( Eq 3 ) , our clustering results can be obtained by performing eigen-decomposition of matrix V + [25] to solve Eq 8 . The final grouping of datapoints into clusters is achieved by performing k-means clustering on Q as in [29] . Given a feature set that describes a collection of objects , denoted as X = {x1 , x2 , … , xn} , we want to construct a similarity network N ∈ R n × n in which N ( i , j ) indicates the similarity between the i-th and j-th object . The most widely used method is to assume a Gaussian distributions across pairwise similarites: N ( i , j ) = exp ( - ∥ x i - x j ∥ 2 2 σ 2 ) ; Here σ is a hyper-parameter that needs careful manual setting . More advanced methods of constructing similarity networks can be seen in [4] . Throughout the paper , we used Normalized Mutual Information ( NMI ) [20] to evaluate the consistency between the obtained clustering and the groundthuth . Given two clustering results U and V on a set of data points , NMI is defined as: I ( U , V ) / max{H ( U ) , H ( V ) } , where I ( U , V ) is the mutual information between U and V , and H ( U ) represents the entropy of the clustering U . Specifically , assuming that U has P clusters , and V has Q clusters , the mutual information is computed as follows: I ( U , V ) = ∑ p = 1 P ∑ q = 1 Q | U p ∩ V q | N log N | U p ∩ V q | | U p | × | V q | where |Up| and |Vq| denote the cardinality of the p-th cluster in U and the q-th cluster in V respectively . The entropy of each cluster assignment is calculated by H ( U ) = − ∑ p = 1 P | U p | N log | U p | N , and H ( V ) = − ∑ q = 1 Q | V q | N log | V q | N . Details can be found in [20] . NMI is a value between 0 and 1 , measuring the concordance of two clustering results . In the simulation , we calculate the obtained clustering with respect to the ground-truth . Therefore , a higher NMI refers to higher concordance with truth , i . e . a more accurate result . The Adjusted Rand Index ( ARI ) is another widely-used metric for measuring the concordance between two clustering results . Given two clustering U and V , we calculate the following four quantities: The ( normal ) Rand Index ( RI ) is simply a + d a + b + c + d . It basically weights those objects that were classified together and apart in both U and V . There are some known problems with this simple version of RI such as the fact that the Rand statistic approaches its upper limit of unity as the number of clusters increases . With the intention to overcome these limitations , ARI has been proposed in [21] in the form of A R I = ( n 2 ) ( a + d ) - [ ( a + b ) ( a + c ) + ( c + d ) ( b + d ) ] ( n 2 ) - [ ( a + b ) ( a + c ) + ( c + d ) ( b + d ) ] . Given a network on a set of N nodes with edge weights W , we first present a few related terms as follows Then the stability measure on time t is defined in terms of the clustered auto-covariance matrix R t = H T ( Σ ( I - L ) t - π T π ) H as follows: r ( t ; H ) = min 0 ≤ s ≤ t ∑ i = 1 C ( R s ) i i = min 0 ≤ s ≤ t t r a c e ( R s ) , and the stability curve of the network is obtained by maximizing this measure over all possible partitions: r ( t ) = max H r ( t ; H ) . A good clustering over time t will have large stability , with a large trace of Rt over such a time span . The variation is defined in terms of the asymptotic stability induced by going from the ‘finest’ to the ‘next finest’ partitions is: V a r i a t i o n ∼ ∑ i ∑ j λ 2 t d i d j u 2 i u 2 j where u2 is the normalized Fiedler eigenvector with its corresponding eigenvalue λ2 . We refer the mathematical details in deriving these two definitions to [23] . There are mainly three hyper-parameters in Vicus: first the number of neighbors K , the variance in network construction σ , and the diffusion parameter α . Details about the meaning of these hyper-parameters can be seen in [30] . In all our experiments , we use the same setting of hyper-parameters as follows: K = 10 , σ = 0 . 5 , α = 0 . 9 . The proposed Vicus is very robust to the choice of σ and α ( S3 Fig ) . For the choice of K , we usually increase K as the number of nodes in the networks get larger ( S3 Fig ) . We also provide a range of recommended choices for these hyper-parameters: K ∈ [ 5 , 20 ] , σ ∈ [ 0 . 3 , 0 . 6 ] , α ∈ [ 0 . 8 , 0 . 95 ] We also want to emphasize that , when performing clustering tasks , Vicus does not specify the number of clusters since Vicus is only providing a new form of Laplacian that captures local structures in the network . In our experiments of single-cell applications , we only feed the number of clusters to the clustering algorithms ( i . e , K-means algorithm ) as the true number of clusters .
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Networks are a representation of choice for many problems in biology and medicine including protein interactions , metabolic pathways , evolutionary biology , cancer subtyping and disease modeling to name a few . The key to much of network analysis lies in the spectrum decomposition represented by eigenvectors of the network Laplacian . While possessing many desirable algebraic properties , Laplacian lacks the power to capture fine-grained structure of the underlying network . Our novel matrix , Vicus , introduced in this work , takes advantage of the local structure of the network while preserving algebraic properties of the Laplacian . We show that using Vicus in spectral methods leads to superior performance across fundamental biological tasks such as dimensionality reduction in single cell analysis , identifying genes for cancer subtyping and identifying protein modules in a PPI network . We postulate , that in tasks where it is important to take into account local network information , spectral-based methods should be using Vicus matrix in place of Laplacian .
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2017
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Vicus: Exploiting local structures to improve network-based analysis of biological data
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When growing populations of bacteria are confronted with bactericidal antibiotics , the vast majority of cells are killed , but subpopulations of genetically susceptible but phenotypically resistant bacteria survive . In accord with the prevailing view , these “persisters” are non- or slowly dividing cells randomly generated from the dominant population . Antibiotics enrich populations for pre-existing persisters but play no role in their generation . The results of recent studies with Escherichia coli suggest that at least one antibiotic , ciprofloxacin , can contribute to the generation of persisters . To more generally elucidate the role of antibiotics in the generation of and selection for persisters and the nature of persistence in general , we use mathematical models and experiments with Staphylococcus aureus ( Newman ) and the antibiotics ciprofloxacin , gentamicin , vancomycin , and oxacillin . Our results indicate that the level of persistence varies among these drugs and their concentrations , and there is considerable variation in this level among independent cultures and mixtures of independent cultures . A model that assumes that the rate of production of persisters is low and persisters grow slowly in the presence of antibiotics can account for these observations . As predicted by this model , pre-treatment with sub-MIC concentrations of antibiotics substantially increases the level of persistence to drugs other than those with which the population is pre-treated . Collectively , the results of this jointly theoretical and experimental study along with other observations support the hypothesis that persistence is the product of many different kinds of errors in cell replication that result in transient periods of non-replication and/or slowed metabolism by individual cells in growing populations . This Persistence as Stuff Happens ( PaSH ) hypothesis can account for the ubiquity of this phenomenon . Like mutation , persistence is inevitable rather than an evolved character . What evolved and have been identified are genes and processes that affect the frequency of persisters .
While it is convenient to consider genetically identical populations of bacteria as collections of physiologically homogeneous cells , they are commonly composed of phenotypically different subpopulations . Usually by some form of stochastic switch , during the course of growth or at stationary phase , bacteria of one phenotype produce cells of different phenotypes [1]–[6] . From the practical perspective of antibiotic treatment , the most important of these phenotypically distinct subpopulations are the persistent cells . As first reported by John Bigger [7] , when growing populations of bacteria are confronted with bactericidal antibiotics , the majority of cells are killed but a minority survive . Upon re-culture , these surviving bacteria are as sensitive to antibiotics as the cells from whence they were derived . And , when exposed to bactericidal antibiotics , they too produced minority populations of survivors . Bigger called these phenotypically resistant but genetically susceptible subpopulations of bacteria “persisters” . In his original 1944 studies , Bigger used Staphylococcus pyogenes ( now Staphylococcus aureus ) and the beta-lactam antibiotic penicillin . Since then , persistence has been demonstrated for a number of different species of bacteria with many classes of antibiotics [8]–[15] . Indeed , persistence has been suggested to be a universal character , not only among the bacteria [16] but also fungi [17] , [18] , the cells responsible for neoplasms [19] and doubtless other somatic cells . Although it is not absolutely clear how important persister subpopulations are to the course of antibiotic treatment in human patients , at least in theory persisters can prolong the course therapy , prevent clearance and promote the generation and ascent of bacteria with heritable resistance [20] , [21] . There is in fact recent clinical evidence for persistence retarding the rate of , and possibly preventing the clearance of bacterial infections in antibiotic treated humans [22] , [23] . What are persisters ? In accord with the prevailing view , bacterial persisters are subpopulations of cells that are refractory to antibiotics because they are either not dividing or they are dividing at very low rates [16] , [24] , [25] , [26] . In this perspective , one class of persisters are generated at random by a stochastic switch from non-persisters during exponential growth , remain in that state for some time and then revert to the non-persister state [24] , [25] . It has been proposed that persisters are also generated at stationary phase as , upon transfer to fresh medium , they take longer to come out of lag ( “wake up” ) than non-persisters [27] . Further work has shown that during the first 1 . 5 hours following inoculation into fresh media , the class of non-growing persister cells generated during stationary phase are not yet completely dormant and not yet refractory to antibiotic treatment but rather complete this transition during this time period [28] . Thus , by killing the majority population of replicating cells , antibiotics increase the relative frequency of these phenotypically resistant but genetically susceptible bacteria , i . e . antibiotics reveal the existence of persister subpopulations , but have no role in their generation . A recent report by Dörr and colleagues questions the generality of this perspective on the role of antibiotics in the generation of persisters [29] . In their study with E coli , pre-exposure to the fluoroquinolone ciprofloxacin increased fraction of cells surviving exposure to bactericidal concentrations of this drug ( also see [30] ) . In this investigation , we use mathematical models , computer simulations and experiments with Staphylococcus aureus and antibiotics of four classes to determine the effects of different antibiotics and their concentrations on the level of persistence and more broadly elucidate the contribution of these drugs to the formation of persisters . We interpret the results of these experiments to be inconsistent with the hypothesis that persisters are a unique , or even very few types of subpopulations that are generated at random , and that bactericidal antibiotics only reveal their existence by killing growing cells . Our results provide evidence that the singular observation that pre-exposure of E . coli to ciprofloxacin increases the frequency of persisters in generally applicable . We show that it not only obtains with a very different species of bacteria , but also with antibiotics of different classes . Based on these theoretical and experimental results , the results of other studies of persistence , and evolutionary considerations we postulate that persisters are: ( i ) the product of many different of errors and/or processes that result in transient periods of inhibition of the cell replication cycle of individual metabolizing bacteria and/or affect the amount of time required for the transition from stationary phase to replication ( the lag ) , and ( ii ) the rates at which these processes occur are influenced by environmental stresses including the presence of antibiotics . We propose that persistence is analogous to mutation , an inevitable rather than an evolved character .
We open this report with a quantitative straw person . We use a simple mathematical model of to illustrate what would be anticipated in our experiments if persisters are generated at random at a rate that is independent of the nature and quantity of antibiotics that select for these phenotypically resistant cells , the null hypothesis . In this model , there are three populations of bacteria , one that is replicating at a high rate and two that are not replicating , persisters , with densities and designations , N , PE and PS , respectively . As shown by Balaban and colleagues [24] , we assume that these persisters are of two types: 1- those that are produced at stationary phase , PS , and those that are produced when the cells are growing exponentially , PE . These persister cells are generated from the N population at maximum rates , fs and fe , per cell per hour and return to the N state at maximum rates gs and ge , per cell per hour . To distinguish between growing and stationary phase , we assume a logistic function ( 1-NT/K ) where NT is the total density of bacteria and K the saturation ( stationary phase ) density , where NT≤K . The rate of growth of the bacteria , the rate of production of persisters from growing cells N→PE , and the rate of return from the persister to the growing state , PS→N and PE→N is proportional to ( 1-NT/K ) . The idea being that these rates are greater when the population is further from stationary phase and these processes cease when the population is at stationary phase , NT = K . The rate of production of stationary phase persisters , N→PS , on the other hand is proportional to NT/K and thereby increases as the population approaches stationary phase and continues during stationary phase . For the pharmacodynamics of the antibiotic , and the N population we use a Hill function [31] , where A is the concentration of the antibiotic , VMAX the maximum growth rate of the bacteria , VMIN the minimum growth rate , ( VMIN≤0 ) , the minimum inhibitory concentration ( MIC ) of the antibiotic and k the hill coefficient , a shape parameter . With these definitions and assumptions , the rates of change in the densities of these three states of bacteria are given bywhere NT = N+PE+PS To illustrate the properties of this model , we use a finite step size Euler method to numerically solve these differential equations . For these computer simulations , the generation and loss of persisters are stochastic processes , which we simulate by a Monte Carlo process . For example , when the rectangularly distributed random number , r ( 0≤r≤1 ) is less than the product of the fe ( 1-NT/K ) *N*Δt , a single individual of the PE persister population is generated and a single individual of the N population is lost . The step sizes of these simulations , ΔT , are chosen so that during any finite step , the probability of generating a persister is less than 1 . These computer simulations were programmed in Berkeley Madonna . Copies of this program and instructions for its used are available at www . eclf . net .
Although not direct evidence in its support , the PaSH hypothesis provides a parsimonious explanation for the existence and ubiquity of persistence . In theory and experimentally , it has been shown that if bacterial populations are periodically challenged with agents that kill growing cells , “episodic selection” [55] , a population that produces persisters can be favored in competition with an otherwise higher fitness population that does not produce them [25] , [55] . However , the conditions under which episodic selection will favor populations that produce persisters are restrictive . Unless the persister producing population has an intrinsic advantage , when it is rare it will not be able to invade and become established in a population that does not produce persisters or produces them at a lower rate . The reason for this is that the phenotype favored by episodic selection , a low rate of the production of non- or slow growing cells , would not be manifest if the number of the persister-producing cells is low . For example , if there were 102 persister-producing cells , N , in a population of 108 non-producers , M , and persisters are produced at a rate of 10−5 per cell per hour , the probability that there will be a single persister cell is 10−3 . Although random persisters may be generated , because of their low density , the N population is more likely to eliminated by a bactericidal antibiotic rather than generate persisters , which would allow for its survival . For a more quantitative consideration of the conditions under which episodic selection will favor the evolution and maintenance of persistence , we use an extension of our opening model of the random production of persisters for bacterial populations maintained in continuous culture . We now consider two populations of bacteria , one that produces persisters , P , and one that does not respectively N and M . The N , M populations grow at a rate proportional to the concentration of a limiting resource , R , which is consumed at a rate proportional to the maximum growth rate of the bacteria and a conversion efficiency parameter , e µg . [58] , [59] . The limiting resource from a reservoir where it is maintained at a concentration , C µg/ml flows into a habitat of unit volume ( 1 ml ) at a constant rate , w per hour which is the same rate at which excess resource , antibiotics wastes and the bacterial population , N , P and M flow out . In this model we assume the persister , P , population does not replicate or take up the resource . In this model , persisters are generated from N cells with a single probability f per cell per hour and revert back to the N state at a rate g per cell per hour . At each hour , there is a probability , de , that AMAX µg/ml of a bacteriocidal antibiotic will be added to the vessel . In addition to being washed out at a rate w per hour the effective concentration of the antibiotic can decay at a rate da per hour . As with the model described in the body of this report , we use a Hill function for the relationship between the concentration of the antibiotic and the rate of growth/death of the bacteria . We assume the same pharmacodynamics for the N and M populations and that the persister population is totally refractory to this drug . In our simulation , the production of persisters and the return to the N state are stochastic processes as are incidence of episodes of antibiotic introduction is stochastic processes . For this use a Monte Carlo protocol . At each time interval , Δt , the probability that a persister cell will be generated is N*f*Δt , if a random number 0<r<1 is less than this product , a persister will be added to the P population and a single cell removed from the N population . The same protocol is used for conversion of P into N , but now the probability used for comparison is P*g*Δt . For these simulations we use values of Δt and other parameters such that at any time interval these products are less than 1 . At each time interval a third random number is generated . If that number is less than de*Δt , AMAX µg/ml of the antibiotic is introduced . The Berkeley Madonna program used for these is available from www . eclf . net/programs . For each of four sets of parameters , we made 20 independent simulations each for 2000 hours . In Figure 7A we present a single simulation where the N and M populations are initially present at the same density , N = M = 5×108 ( the chemostat equilibrium ) and there are initially no persisters . At each episode of antibiotic treatment the viable cell density of the N and M populations decline as do that of the persisters . Although the latter are not killed by the antibiotics , their rate of production is reduced , due to the decline in N population and their reversion to the N state . They do , however , buffer the extent of decline of the N population and thereby provide the N cells an advantage , even though they grow at the same rate as the M population . This advantage of the N population also obtains when the initial frequency of N is somewhat lower than M ( Figure 7B ) . However , when the persister producing N population is substantially rare than the M population ( N = 103 and M = 109 ) , the N population no longer has an advantage due production of antibiotic refractory persisters . Although persisters are generated by chance , their densities are too low to generate persisters and thereby prevent the elimination of the N population when confronted with the antibiotic . This threshold for density of N necessary for persistence to provide an advantage is even greater when the probability of producing persisters is lower . This can be seen in Figure 7D , where f = 10−5 rather than 10−4 per cell per hour . Under these conditions the persister-producing N population does not have an episodic selection advantage when its initial density is 105 cells per hour . More details about the outcome of the 20 independent simulations with the four sets of parameters in Figure 7 are presented in Table 3 . In Figure 7A , 7B , and 7C , we follow the changes in density of the persister-producing N population and it's non- producing competitor M . In Figure 7A and 7B , if in addition to producing non-growing cells , the persister producing , N population has an intrinsic disadvantage in its maximum growth rate VMAXN = 0 . 995 , VMAXM = 1 . 000 . In the absence of episodic selection , the relative density of the N population declines ( Figure 7A ) . When we allow for the random introduction of a decaying antibiotic , the production of non-growing persister cells can provides the otherwise less fit persister-producing N population with an advantage . In the simulation presented in Figure 7B the population that does not produce persisters , M , is lost by 3000 hours , whilst the persister-producing N population dominates . To get a better idea of the advantage to producing non-growing , antibiotic-refractory cells , we ran this simulation 50 times and calculated the M/N ratio at 1000 hours . In 39 out of the 50 runs , the ratio of N/M exceeded 1 . 0 . If we assume a density of 0 . 1 cells per ml as the cut-off for the loss of a population , the average N/M ratio at 1000 hours for the 50 runs was 3 . 2×109 with a standard deviation of 4 . 4×109 cells per ml . The lowest N/M ratio noted for the 11 runs where the M dominated was 0 . 15 . A very different outcome is obtained when the persister-producing N population is initially rare and the production of persisters is a random process . Although this persister-producing population has an intrinsic growth rate advantage relative to the non-producing M , it is invariably lost when its initial frequency is low ( Figure 7C ) . The rare N population is also most commonly lost when is initial numbers are somewhat higher , 105 rather than 103 ( Figure 7D ) . The time before the loss of the rare persister-producing population ( N<0 . 1 per ml ) varies over a large range ( Table 3 ) . The reason for this is that at these low densities , antibiotic-refractory persister cells are rarely if ever produced . And , since this population with the capacity to produce persisters is rare , even though it has a higher intrinsic fitness , it is more likely to be eliminated by the antibiotic episodes than the more common non-persister producing M , population . If the PaSH hypothesis is correct , and persisters are largely if not exclusively the product of errors in cell replication and cell metabolism , it is no more necessary to explain the evolutionary processes responsible for the existence of persisters than it is for mutation . Like mutation , persistence is an inevitable rather than an evolved character . The high frequency persister mutants , like hipA [56] , [57] , are analogous to the mutants with defective mismatch and other repair genes , “mutator” genes like mutS . As is the case for mutation , evolution by natural selection can favor mechanisms that alter the rate at which these errors occur but it cannot eliminate them any more than mutator genes eliminate mutation . Because persistence is an inevitable rather than an evolved character , we would expect it to be ubiquitous . Indeed , persistence has been shown to occur in single celled eukaryotes [17] , [18] as well as prokaryotes [16] and the somatic cells responsible for neoplasms [19] .
The experiments herein utilize Staphylococcus aureus strain Newman which was originally isolated in 1952 from a patient suffering from tubercular osteomyelitis [32] . This strain was generously provided by Dr . William Shafer . Cultures were grown in 10 mL of Mueller-Hinton II ( MHII ) broth in 50 mL Pyrex flasks at 37°C shaking at 200 rpm . Viable cell density per ml was determined by dilution plating on Lysogeny Broth ( LB ) agar ( also incorrectly , but commonly , known as Luria Bertani broth or Luria Broth ) [33] , [34] . Antibiotic stocks were prepared to a final concentration of 10 µg/µl for ciprofloxacin , gentamicin and oxacillin while vancomycin was prepared to a final stock concentration of 15 µg/µl . All antibiotics were procured from Mediatech , Inc . ( Herndon , Va . ) and Sigma-Aldrich ( St . Louis , Mo . ) . Dilutions of requisite antibiotics were made fresh in MHII broth to the appropriate concentrations for each experiment . The minimum inhibitory concentrations ( MICs ) of the antibiotics and bacteria used in this study were estimated with the factor of two serial dilution protocol using the method recommended by the Clinical and Laboratory Standards Institute ( CLSI ) [35] . In short , overnight cultures were grown in MHII , diluted to final concentrations of ∼105 in fresh media and incubated 96 well plates with two-fold serial dilutions of each antibiotic . After incubation at 37°C for 18 hours , the OD630 nm was obtained and the MICs estimated as the lowest concentration for which there was no growth . Using methods described in Regoes et al [31] , we calculated the parameters of the pharmaocdymamic ( Hill ) functions for each combination of S . aureus and antibiotic . The bacteria were grown overnight in MHII broth with shaking ( 200 rpm ) at 37°C in 50 mL Pyrex flasks . These overnight cultures were then diluted to a final concentration of ∼2×106 or ∼2×107 bacteria ( depending on assay ) in fresh MHII media and incubated for 1 hour at 37°C shaking at 200 rpm to ensure entry into the exponential growth phase . Cultures were then inoculated with different concentrations ( multiples of the MICs ) of ciprofloxacin , gentamicin , oxacillin , or vancomycin . Densities were estimated at different times by diluting and plating on LB agar followed by incubation at 37°C for 48 hours . To control for possible confounding by antibiotic carryover on the plates , experiments were performed with a washing protocol . The antibiotic-treated cultures were centrifuged at 5 , 000 rpm for 6 minutes . The pellets obtained were re-suspended in fresh MHII media and pelleted again at 5 , 000 rpm for 6 minutes . Following the second re-suspension in fresh MHII these twice-washed cells were either plated directly , or plated following dilution in saline . While the variance in numbers of colonies observed with the washed cells was similar to those plated without washing , the estimated densities were lower , presumably because of the loss of cells or their viability during pelleting . Consequently , we elected to plate the cultures without washing . Disk diffusion ( Becton , Dickinson , and Company ) assays were used to test for changes in susceptibility following exposure to the antibiotics . For this , we grew individual colonies in MHII overnight and added 50 µl to 1 . 5 mL of LB Soft ( top ) agar which was then poured onto LB agar . Disks containing 5 µg ciprofloxacin , 10 µg gentamicin , 1 µg oxacillin , or 30 µg vancomycin were placed onto the plates and the plates were incubated overnight at 37°C . The following day , zones of inhibition were determined .
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Because of its importance to therapy , a great deal of effort has been devoted to understanding the mechanisms responsible for and the genetic basis of persistence in inherently susceptible but phenotypically antibiotic-resistant subpopulations of bacteria . Much of this research is based on the premise that persisters are produced at random from the susceptible population and the antibiotics used to detect them play no role in their generation . The results of this jointly theoretical and experimental study are inconsistent with this hypothesis . These results , along with observations reported by other investigators , including the failure to find bacteria that do not produce persisters but an abundance of genes modifying their frequency , support the hypothesis that there are many mechanisms responsible for persistence . Based on these collective theoretical and experimental results , along with evolutionary considerations , we postulate that persistence is analogous to mutation . It is an inevitable product of errors and glitches in cell replication and metabolism rather than an evolved character .
|
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"Results",
"Discussion",
"Materials",
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"medicine",
"infectious",
"diseases",
"microbial",
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2013
|
Pharmacodynamics, Population Dynamics, and the Evolution of Persistence in Staphylococcus aureus
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Culture is the gold standard for the detection of environmental B . pseudomallei . In general , soil specimens are cultured in enrichment broth for 2 days , and then the culture broth is streaked on an agar plate and incubated further for 7 days . However , identifying B . pseudomallei on the agar plates among other soil microbes requires expertise and experience . Here , we evaluate a lateral flow immunoassay ( LFI ) developed to detect B . pseudomallei capsular polysaccharide ( CPS ) in clinical samples as a tool to detect B . pseudomallei in environmental samples . First , we determined the limit of detection ( LOD ) of LFI for enrichment broth of the soil specimens . Soil specimens ( 10 grams/specimen ) culture negative for B . pseudomallei were spiked with B . pseudomallei ranging from 10 to 105 CFU , and incubated in 10 ml of enrichment broth in air at 40°C . Then , on day 2 , 4 and 7 of incubation , 50 μL of the upper layer of the broth were tested on the LFI , and colony counts to determine quantity of B . pseudomallei in the broth were performed . We found that all five soil specimens inoculated at 10 CFU were negative by LFI on day 2 , but four of those five specimens were LFI positive on day 7 . The LOD of the LFI was estimated to be roughly 3 . 8x106 CFU/ml , and culture broth on day 7 was selected as the optimal sample for LFI testing . Second , we evaluated the utility of the LFI by testing 105 soil samples from Northeast Thailand . All samples were also tested by standard culture and quantitative PCR ( qPCR ) targeting orf2 . Of 105 soil samples , 35 ( 33% ) were LFI positive , 25 ( 24% ) were culture positive for B . pseudomallei , and 79 ( 75% ) were qPCR positive . Of 11 LFI positive but standard culture negative specimens , six were confirmed by having the enrichment broth on day 7 culture positive for B . pseudomallei , and an additional three by qPCR . The LFI had 97% ( 30/31 ) sensitivity to detect soil specimens culture positive for B . pseudomallei . The LFI can be used to detect B . pseudomallei in soil samples , and to select which samples should be sent to reference laboratories or proceed further for bacterial isolation and confirmation . This could considerably decrease laboratory workload and assist the development of a risk map for melioidosis in resource-limited settings .
The Gram-negative Burkholderia pseudomallei is a soil-dwelling organism and also the cause of melioidosis , an often fatal infectious disease [1 , 2] . Melioidosis can be difficult to diagnose due to its diverse clinical manifestations . The diagnostic confirmation relies on microbiological culture , which is often unavailable in resource-restricted regions of the world [3] . Even with such facilities , B . pseudomallei may be dismissed as a culture contaminant [4] , or be misidentified by standard identification methods including API 20NE and automated bacterial identification systems [5 , 6] . The disease occurs as a result of skin inoculation , inhalation and ingestion of environmental B . pseudomallei [7] . The organism is intrinsically resistant to a wide range of antimicrobials , and treatment with ineffective antimicrobials may result in case fatality rates ( CFRs ) exceeding 70% [8 , 9] . A recent spatial modeling study estimated there to be 165 , 000 human melioidosis cases per year worldwide , of which 89 , 000 die [10] . The study also estimated that melioidosis is severely underreported in the 45 countries in which it is known to be endemic and that B . pseudomallei is likely present in a further 34 countries in which melioidosis has never been reported [10] . Defining the distribution of B . pseudomallei in countries where B . pseudomallei is likely present but melioidosis has never been reported is important , since this will provide policy makers with evidence for raising awareness of this disease among healthcare workers and microbiology laboratories in these areas [11] . Environmental sampling can be used to identify areas where people are at risk even before cases are recognized . For example , the first environmental survey around Vientiane City ( Lao PDR ) in 1998 demonstrated the presence of B . pseudomallei prior to the recognition of human disease [12] . This environmental finding resulted in an effort to identify B . pseudomallei from clinical specimens , with the first case of melioidosis being identified in 1999 [13] . Since then , more than 920 culture-positive melioidosis patients have been identified in Lao PDR [14] . Environmental sampling can also be used to confirm the endemicity of melioidosis after identifying melioidosis cases in new areas . Recent findings include the detection of environmental B . pseudomallei after case reports in Gabon [15] and Bangladesh [16] . Culture and PCR assays are commonly used to detect B . pseudomallei in the environment , but both tests require experienced microbiologists and are only available in a few research laboratories worldwide . For the culture method , soil specimens are initially cultured in selective broth for 2 days , and then the upper layer of the broth is streaked on an agar plate and incubated for a further 7 days [11] . Identifying B . pseudomallei on agar plates among other soil microbes is time-consuming and requires expertise and experience . For effective molecular detection , soil specimens are also first enriched in selective broth for 2 days prior to nucleic acid amplification [17 , 18] . Despite the higher sensitivity than culture , PCR assays also require experienced personnel and positive controls , and are relatively costly for resource-limited settings . Recently , a lateral flow immunoassay ( LFI ) was developed to detect B . pseudomallei capsular polysaccharide ( CPS ) in clinical samples [19] . In a pilot study , the LFI was shown to have 100% sensitivity for all urine samples containing B . pseudomallei greater than 1 . 2×104 CFU/ml [19] . Here , we evaluate the LFI as a tool to detect B . pseudomallei in the enrichment broth of the environmental samples . We found that the LFI had high sensitivity in our setting .
B . pseudomallei wild-type strain E08 and 300 grams of soil culture negative for B . pseudomallei collected from Nakorn Ratchasima , Northeast Thailand , were used . The strain E08 was selected as the strain has been frequently used as a representative strain of B . pseudomallei from the environment [20–22] . Soil specimens ( 10 gram/specimen ) were spiked with B . pseudomallei at an inoculum of 10 to 105 colony forming units ( CFU ) per 10 grams of soil , and incubated in 10 ml of enrichment broth ( threonine-basal salt solution plus colistin at 50 mg/liter [TBSS-C50] ) in air at 40°C . Unspiked soil samples and soil samples sterilised by autoclaving were included as negative controls . Soil samples sterilised by autoclaving , TBSS-C50 and sterile distilled water , all spiked with B . pseudomallei , were also included as positive controls . After incubation for 2 , 4 and 7 days , the upper layer of enrichment broth were tested by LFI , and evaluated by culture for quantity of B . pseudomallei . A total of nine soil specimens were used to cover the range of inoculum from 10 to 105 CFU per 10 grams of soil , while five specimens was used for the lowest inoculum of 10 CFU per 10 grams of soil ( Table 1 ) . The Active Melioidosis Detect ( AMD ) LFI developed by InBios ( Seattle , USA ) was used [19] . The evaluated LFI uses a monoclonal antibody ( mAb ) 4C4 targeting the CPS of B . pseudomallei . MAb 4C4 was sprayed onto a nitrocellulose membrane strip for the test line , and goat anti-chicken IgY ( Lampire Biological laboratories , Pennsylvania , USA ) was sprayed on the same membrane for the control line . The conjugate pad contained dried 40 nm gold particles conjugated with mAb 4C4 as well as a small amount of gold conjugated with chicken IgY ( to react with the control line ) . The conjugate pad was treated with a borate-based buffer containing a small concentration of detergent and dried for later gold conjugate application . The sample application pad was also treated similarly and dried . The LFI was assembled by combining the sprayed membrane , conjugate pad , and sample pad on top of an adhesive plastic backing . The LFI was stored in air at room temperature until use . A total of 50 μl of the upper layer of the broth was mixed with two drops of lysis buffer and two drops of chase buffer in a clean tube , in which the LFI pad was dipped . After 15 minutes at room temperature , the LFI results were read . The quantitative count of B . pseudomallei in the enrichment broth was performed as described previously with minor modifications [23] . In short , two 100 μl samples and two 10 μl samples of the upper layer of enrichment broth were spread onto four modified Ashdown’s agar plates using a rotary plater so that the individual colonies could be identified . All plates were then incubated in air at 40°C . The plates were inspected daily for 4 days , and B . pseudomallei was identified as previously described [11] . The LOD of LFI was estimated by comparing the quantitative B . pseudomallei results between samples with positive and negative LFI results . The quantitative counts of B . pseudomallei in specimens with high levels of fungus overgrowth were excluded . The optimal duration of enrichment for LFI testing was determined by selecting the duration that provided the highest proportion of LFI positivity . A total of 105 environmental soil samples were collected from 21 rice paddy fields located in 7 provinces ( Burirum , Chaiyaphum , Khon Kaen , Loei , Nakhon Ratchasima , Nong Bua Lam Phu and Udon Thani ) in Northeast Thailand during March and June 2015 . Three villages per each province were randomly selected for study . We calculated that 105 samples were needed to estimate sensitivity of LFI with the 95% confidence interval of 15% . The randomization was performed using Stata version 14 . 0 ( StataCorp LP , College Station , Texas ) . A rice field in each village was selected , and the first 5 soil samples collected in each rice field were used for the study . In each rice field , the soil samples was collected 2 . 5 meters apart . The soil sampling and culture was performed according to the consensus guidelines for environmental sampling of B . pseudomallei developed by DEBWorP [11] with slight modifications . In short , 10 grams of soil were collected at a depth of 30 cm . Each 10 grams of soils were incubated in 10 ml of TBSS-C50 enrichment broth ( threonine-basal salt solution with 50 mg/L colistin ) at 40°C in air for 7 days . After incubation for 2 and 7 days , 10 μl of the upper layer of enrichment broth were directly plated on Ashdown agar and incubated at 40°C in air further for 7 days [24] , and B . pseudomallei was then identified as previously described [11] . In addition , 50 μl of the upper layer of enrichment broth on day 7 was tested on the LFI as described above and 1 ml of the upper layer of enrichment broth on day 7 was stored for qPCR assay . Culture of the upper layer of enrichment broth on day 7 is not normally conducted , but was performed in this study in order to evaluate the presence of B . pseudomallei in soil specimens which were negative by standard culture but LFI positive . Three microbiologists performed culture , LFI and qPCR independently , and they were blinded to the results of the other two tests . A 1 mL aliquot of broth on day 7 was stored at -20°C until further processing . Prior to nucleic acid extraction , the broth was spun at 12 , 000g for 10 min , and all but the pellet and 200 μL of broth were removed . The remaining DNA pellet and broth were processed using the Qiagen Mini Kit using the bacterial extraction protocol , according to the manufacturers instructions . The qPCR assay targeting orf2 of B . pseudomallei type III secretion system ( TTS1 ) was performed as previously described [18] . Utility of the LFI was determined by comparing the results between culture of broth on day 2 , LFI of broth on day 7 and qPCR of broth on day 7 . Culture of broth on day 2 was selected for the main comparison because it is the standard method used for environmental detection of B . pseudomallei in soil samples [11] . The degree of agreement between culture and LFI was expressed using the Kappa index and its P value . This describes the level of association , both positive and negative , beyond that caused by chance , as follows: 0 . 00–0 . 20 , slight; 0 . 21–0 . 40 , fair; 0 . 41–0 . 60 , moderate; 0 . 61–0 . 80 , substantial; 0 . 81–1 . 00 , high .
A total of nine soil specimens ( 10 gram/specimen ) were spiked with B . pseudomallei at an inoculum of 10 to 105 CFU per 10 grams of soil , and incubated in 10 ml of enrichment broth in air at 40°C ( Table 1 ) . Immediately after inoculation and mixing , we found that the quantitative count of B . pseudomallei in the broth ranged from 0% to 100% of the inoculum . All 0% results were from five specimens inoculated with 10 CFU of B . pseudomallei per 10 gram of soil ( Table 1 ) . The 100% result was the specimen inoculated with 100 CFU of B . pseudomallei per 10 gram of soil , with the quantitative count after inoculation similar to the expected concentration ( 10 CFU/ml; Table 1 ) . On day 2 , the quantitative count of B . pseudomallei in the broth of five soil specimens spiked with 10 CFU ranged from 3x103 to 8 . 4x105 CFU/ml . Nonetheless , all of those five specimens were LFI negative ( Table 1 , Tube No . 5 to 9 ) . On day 4 , the quantitative count of B . pseudomallei in the broth of those five specimens ranged from 1x105 to 2 . 1x106 CFU/ml . Only one specimen ( with a quantitative count 7 . 6x105 CFU/ml ) was LFI positive , while the remaining four were LFI negative . On day 7 , B . pseudomallei count in the broth of those five specimens ranged from 9x103 to 1 . 3x108 CFU/ml . There was some difficulty obtaining quantitative counts in the two specimens with low counts ( 9x103 and 2x104 CFU/ml ) due to overgrowth with fungi . Nonetheless , four specimens were LFI positive , while the remaining one with the count of 2x104 CFU/ml was LFI negative ( Table 1 ) . S1 Table shows the results of all control specimens . Using the quantitative counts of all the specimens , we found that the highest count that had a negative LFI result was 2 . 1x106 CFU/ml ( Tube 6 ) , and the next higher count that had LFI positive was 3 . 8x106 CFU/ml ( Tube 4 , Table 1 ) . Therefore , we estimated that the LOD of the investigated LFI to detect B . pseudomallei in the enrichment broth of soil samples was about 3 . 8x106 CFU/ml . As 80% of enrichment broth on day 7 of soil specimens spiked with 10 CFU of B . pseudomallei per 10 grams of soil were LFI positive while only 20% of those enrichment broth on day 4 were LFI positive , the enrichment broth on day 7 was selected as the optimal sample for LFI . Of 105 soil samples collected in Northeast Thailand , 25 ( 24% ) were culture positive for B . pseudomallei using the standard culture method , 35 ( 33% ) were LFI positive , and 79 ( 75% ) were qPCR positive ( Fig 1 ) . Agreement of results between the standard culture and LFI was substantial ( Kappa index 0 . 72 , p<0 . 001 ) . Of 11 specimens LFI positive but the culture of broth on day 2 negative , six had broth on day 7 culture positive for B . pseudomallei , three were qPCR positive but broth on day 7 culture negative , and the other two were both qPCR and culture of broth on day 7 negative . We estimated that the LFI had 97% ( 30/31; 95% confidence interval 83%-99% ) sensitivity for detection of soil specimens culture positive for B . pseudomallei .
This study demonstrates the utility of LFI as a tool to detect B . pseudomallei in soil samples . The LFI has a high sensitivity ( 97% ) to detect soil specimens culture positive for B . pseudomallei . The test is very simple to use and does not require expertise or new equipment; therefore , LFI could be useful for inexperienced microbiologists who want to conduct an environmental survey in resource-limited settings . One explanation of the high sensitivity of the LFI is that we chose the enriched broth on day 7 as the specimen for LFI . Enrichment of soil specimens increases the concentration of B . pseudomallei in the broth exponentially , and the enrichment step ( for 1–2 days ) is already necessary for both culture and molecular detection [17 , 18] . We show that , for the evaluated LFI , enrichment of only 2 days is not sufficient , and 7 days are needed to increase the quantity of B . pseudomallei in the broth from as low as 1 CFU/ml ( equivalent to 1 CFU/gram of soil ) up to 3 . 8 x 106 CFU/ml high enough to be detected . Although culture is the gold standard for the detection of environmental B . pseudomallei , it requires expertise and experience . Colonies of B . pseudomallei on Ashdown agar plates can have seven morphologies [25] and be dismissed as other soil bacteria [4] . In addition , although Ashdown agar contains crystal violet and gentamicin to suppress the growth of other bacteria , overgrowth of other soil bacteria and fungi on agar plates is common [26] , particularly after 7 days of incubation . In this study , we report the advantage of LFI for the detection of B . pseudomallei in soil samples as the reading is simple and can be performed by microbiologists otherwise not experienced with B . pseudomallei . Preliminary detection of B . pseudomallei in enrichment media of soil samples by LFI will also reduce the workload of at melioidosis reference laboratories by focusing on the confirmation of identified LFI positives rather than screening large sample numbers . This could enhance the development of a global risk map for melioidosis , particularly in areas without melioidosis reference laboratories [11] . The LOD of LFI in soil enrichment ( 3 . 8x106 CFU/ml ) is comparatively higher than its previously reported LOD in direct urine samples ( 1 . 2 x 104 CFU/ml ) [19] . This is probably due to the presence of other organisms and other substances in the soil enrichment . The long total time required to complete the LFI test ( 7 days ) is not a major problem as the total time required for the culture method is also 9 days . The culture method requires the soil specimen to be in the enrichment broth for 2 days , and then the upper layer of the broth is streaked on plates and incubated for a further 7 days . B . pseudomallei can survive well in soil kept at ambient temperature ( 24 to 32°C ) and away from direct sunlight [11] . Therefore , culture and qPCR assays can be performed later from the soil specimens which are LFI positive . A study reported that the numbers of B . pseudomallei in soil increased moderately over 2 weeks when kept at 20°C [27] . The isolation of environmental B . pseudomallei provides definitive evidence that people in the areas sampled are at risk of melioidosis; therefore , it is important to use the test or a combination of tests with high accuracy rather than a rapid test with low accuracy . The high proportion of specimens with were culture negative but qPCR positive ( Fig 1 ) could be due to many reasons . First , culture results were based on enrichment of soil samples on day 2 , while qPCR was based on that on day 7 , resulting in higher number of B . pseudomallei . Second , qPCR has a lower detection limit compared to the culture method [18] , and could also detect non-culturable B . pseudomallei [27] . In this study , qPCR confirmed the presence of B . pseudomallei in an additional three specimens that were culture negative but LFI positive . As qPCR is more sensitive than culture and LFI , the molecular methods should be considered if absence of the organism in the environment needs to be confirmed and resources are available [28] . Nonetheless , PCR assays for environmental samples require experienced molecular microbiologists and separate facilities , which are largely not available in areas where melioidosis could be highly endemic . The LFI may have some limitations . First , the LFI could yield false positive results if a variant of B . thailandensis which produces CPS similar to B . pseudomallei is present in the soil . Second such B . thalandensis variants have been reported in soil from the USA ( Texas ) [29] , Cambodia [30] and Laos [18] , but not so far in soil from other areas . Second , the quantitative counts ( CFU/ml ) in our study could be underestimated due to overgrowth of fungi and other bacteria . Third , the use of LFI may lead to a long total time , as repeated bacterial culture would be needed for the confirmation of B . pseudomallei in the environment ( 15 days; 7 days of LFI plus 9 days of culture ) . If transportation of LFI-positive soil samples to reference laboratories is required , it would be preferred within 7-days of completing the LFI tests [27] . Sample site selection and total number of samples per site could be performed according to the consensus guidelines for environmental sampling of B . pseudomallei developed by DEBWorP [11] . Nonetheless , if rapid results are required , molecular methods should be considered [28] . Fourth , the utility of LFI may be different in other regions where the soil physicochemical properties are different , B . pseudomallei numbers lower and/or other organisms are present . Fifth , Our study sample size is not large . Further studies should also investigate the sensitivity and utility of the LFI to detect B . pseudomallei in soil outside Northeast Thailand . In conclusion , we recommend that the LFI could be used to detect environmental B . pseudomallei in new areas , particularly when the investigation is conducted by microbiologists who have limited experience with the isolation and identification of B . pseudomallei from soil specimens . LFI positive soil specimens could then be sent to reference laboratories for bacterial isolation and confirmation . This could considerably decrease laboratory workload and assist in the development of a global risk map for melioidosis .
|
Burkholderia pseudomallei is an environmental Gram-negative bacillus and the causative agent of melioidosis . Culture and PCR assays are standard diagnostic tools used to detect B . pseudomallei in the environment . However , those tests require experienced microbiologists and are regularly conducted only in a few research laboratories worldwide . In this study , we demonstrated that the prototype lateral flow immunoassay ( LFI ) developed to detect B . pseudomallei capsular polysaccharide ( CPS ) in clinical samples could be used to detect B . pseudomallei in environmental samples . We found that the LFI can be used to detect B . pseudomallei in experimentally spiked soil specimens . Next , we evaluated the sensitivity of LFI using 105 soil samples collected in Northeast Thailand . We found that the LFI had high sensitivity to detect B . pseudomallei in the soil . We propose that the LFI could be used to detect environmental B . pseudomallei in resource-limited settings . Soil samples positive for LFI could be sent to reference laboratories for confirmation with culture or molecular methods . The use of LFI could assist in the development of a global risk map for melioidosis .
|
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2016
|
Utility of a Lateral Flow Immunoassay (LFI) to Detect Burkholderia pseudomallei in Soil Samples
|
At the core of amyloid fibrils is the cross-β spine , a long tape of β-sheets formed by the constituent proteins . Recent high-resolution x-ray studies show that the unit of this filamentous structure is a β-sheet bilayer with side chains within the bilayer forming a tightly interdigitating “steric zipper” interface . However , for a given peptide , different bilayer patterns are possible , and no quantitative explanation exists regarding which pattern is selected or under what condition there can be more than one pattern observed , exhibiting molecular polymorphism . We address the structural selection mechanism by performing molecular dynamics simulations to calculate the free energy of incorporating a peptide monomer into a β-sheet bilayer . We test filaments formed by several types of peptides including GNNQQNY , NNQQ , VEALYL , KLVFFAE and STVIIE , and find that the patterns with the lowest binding free energy correspond to available atomistic structures with high accuracy . Molecular polymorphism , as exhibited by NNQQ , is likely because there are more than one most stable structures whose binding free energies differ by less than the thermal energy . Detailed analysis of individual energy terms reveals that these short peptides are not strained nor do they lose much conformational entropy upon incorporating into a β-sheet bilayer . The selection of a bilayer pattern is determined mainly by the van der Waals and hydrophobic forces as a quantitative measure of shape complementarity among side chains between the β-sheets . The requirement for self-complementary steric zipper formation supports that amyloid fibrils form more easily among similar or same sequences , and it also makes parallel β-sheets generally preferred over anti-parallel ones . But the presence of charged side chains appears to kinetically drive anti-parallel β-sheets to form at early stages of assembly , after which the bilayer formation is likely driven by energetics .
Amyloid fibrils are hallmarks of several neurodegenerative diseases including Alzheimer's , Parkinson's , and prion diseases [1] . Unlike other protein quaternary structures [2] , amyloid fibrils share a sequence independent structural motif known as the cross- β spine; individual strands from constituent proteins forming a β-sheet that runs perpendicular to the fibril axis [3] . Amyloid fibrillogenesis is a multi-staged protein aggregation process and accumulating evidence suggests that prefibrillar oligomeric species are toxic [4] . Yet pathological roles of fibrillar species cannot be undermined . Amyloid protofibrils as well as oligomers have been suggested to lead to neuronal cell death [5]–[8] . Interruption of fibril formation prevented cell damage [9] , and β-sheet rich diffusible oligomeric species of A β , the chief constituent of amyloid fibrils in Alzheimer's disease , possess cytotoxicity , which share structural similarity to mature fibrils [10] . In the case of systemic amyloidosis , sheer amount of amyloid deposition itself can be symptomatic [11] . Recent findings suggest even greater biological role of amyloid fibrils: amyloid fibrils in semen accelerated HIV infection [12]; a functional , mammalian amyloid composed of a protein Pmel17 promoted the formation of melanin [13] . Furthermore , de novo designed peptides self-assemble into amyloid-like β-sheet filaments , and hydrogels composed of these filaments hold a great potential for three-dimensional cell culture scaffold [14] , [15] . Amyloid fibrils can be formed by a wide variety of protein sequences , where partial denaturation is a common precursor to fibril formation [16] . Evolution appears to have limited protein sequences in a restricted range of physico-chemical properties , i . e . in hydrophobicity and net electrostatic charges , to keep proteins from misfolding and aggregation [17] . Molecular polymorphism is another feature of amyloid fibrillogenesis , where a given peptide or protein may self-assemble into filament structures that differ in atomistic order as well as in filament morphologies [18] . While the selection of the filament structure depends on the growth condition , which can be purely mechanical agitation , once a stable filament is formed , it continues to grow , keeping the atomistic order even if the growth condition changes [19] , [20] . The selection mechanism for the cross-β structure of amyloid fibrils is yet to be elucidated . Previous experimental approaches such as x-ray fiber diffraction [21] , solid-state nuclear magnetic resonance ( NMR ) [22] , [23] , atomic force microscopy ( AFM ) [24] , and electron microscopy ( EM ) [25] have contributed greatly to understanding molecular structures of amyloid fibrils as well as gross fibril morphology . More recently , x-ray diffraction of amyloid microcrystals enabled unequivocal determination of high-resolution atomistic structures of the cross-β spine [26] , [27] . These results suggested that cross-β spines share a common structural feature termed as the ‘steric zipper , ’ where side chains from the two β-sheets form a tightly interdigitating dehydrated interface , so that the resulting β-sheet bilayer forms a fundamental building block of fibrillar aggregates . While these experiments are essential for describing supramolecular structures of amyloid fibrils , a fundamental question remains regarding how these structures are formed . Knowledge of the assembly pathway and structural properties of these fibrils would be useful for developing therapeutic strategies against amyloidoses as well as for developing biomaterials based on peptide self-assembly into β-sheet fibrils . Computer simulations have played an important role in addressing these questions . The assembly kinetics of β-sheet rich oligomeric species was characterized by the initial hydrophobic collapse followed by reorganization of monomers to form backbone hydrogen bonds [28] , [29] . The potential of mean force of peptide dimers was calculated [30] , [31] . Aggregation prone spots in an amyloidogenic protein were identified by dividing the protein into segments and performing simulations on each [32] . Relative stability of oligomers as well as mature filaments were also studied [33] , [34] . More recently , various interaction modes between two β-sheets formed by human Islet amyloid polypeptides were studied [35] . Properties of high-resolution x-ray structures of cross-β spines [26] , [27] have also been studied computationally . Molecular dynamics ( MD ) simulations of isolated β-sheet bilayer filaments showed stability of the steric zipper while the filament developed a helical twist [36] . The stability of spontaneously formed oligomers as well as oligomeric segments of the filament has been tested [37]–[39] . A thorough structural analysis on various oligomeric β-sheet species addressed a possible toxicity mechanism via non-zipper type exposed strands [40] . An ab initio quantum mechanical as well as classical electrostatics calculation showed that energetics of β-sheet formation is cooperative up to the length of three peptides [41] . In addition to simulations of available structures , knowledge-based modeling technique exploiting the crystallographic structures was developed to identify fibril-forming segments of proteins [42] and filament symmetry was utilized to predict detailed β-sheet bilayer conformation [43] . Possible binding modes of typical amyloid markers , congo red and thioflavin-T , on these fibrils were also studied computationally [44] , [45] . Despite these advances in structural characterization , a basic question remains regarding the selection mechanism for steric zipper patterns . For a given peptide sequence , there are multiple ways of constructing β-sheets and stacking them [46] , [47] . The 13 available crystal structures of the cross-β spine ( ‘steric zipper’ ) are classified into 8 different patterns depending on , 1 ) the relative direction of successive peptides in each layer , 2 ) the choice of the face of the β-sheet making the dehydrated interface , and 3 ) the symmetry between adjacent β-sheet layers [27] . Yet it is unclear how a given peptide in this study ended in a specific bilayer pattern . Although it is expected that the crystal structure corresponds to a free energy minimum among possible filament patterns , no quantitative study exists to demonstrate this to date . Molecular polymorphism in amyloid fibrils as mentioned above further complicates the picture , where factors such as mechanical agitation [19] or different ionic strengths [22] , [48] can lead to different supramolecular structures ( reviewed in [18] ) . As a related issue , one of us found that at early stages of assembly , kinetic trapping may dominate over free energy minimization , as the conformational relaxation time for kinetically trapped oligomers is longer than the diffusional encounter time with other monomers and oligomers , supporting the possibility that kinetically trapped structure can propagate into the filament level [28] . Indeed , there are two filament structures for the peptide NNQQ , Protein Data Bank ( PDB ) IDs 2ONX and 2OLX , where their crystallization conditions differ only in the contents of the reservoir solution . It has been suggested that polymorphism is possible if there are multiple filament structures with similar thermodynamic stability [18] . However no quantitative study is available that supports this picture . It is also unclear whether similar stability between multiple possible filament structures is a requirement for polymorphism , or whether a filament structure can be chosen by a purely kinetic mechanism even if it is not the most stable one ( with a free energy barrier with the most stable one being sufficiently larger than , where : Boltzmann constant , T: temperature ) . It thus appears that , although amyloid fibrillogenesis is largely a sequence-independent phenomenon , for a given peptide sequence , the choice of specific steric zipper pattern involves intricate interactions between amino acid side chains as well as backbone hydrogen bonds . A comprehensive method that elucidates the structural selection mechanism and the stabilizing role of individual residues in the filament is thus desirable . To address these issues , we adopted methods for calculating protein-protein binding energies [49]–[51] into a computational modeling and simulation scheme that calculates the binding free energy ( ) of a monomer incorporating into a given steric zipper pattern . It employs explicit water simulations to generate the coordinate trajectory and then uses a molecular mechanics/generalized Born-surface area method [52] and normal mode analysis ( NMA ) [53] to calculate various energy terms in . We constructed a series of steric zipper patterns for a given peptide and calculated for each of them . Our results quantitatively support the qualitative argument suggested previously: the minimum free energy configuration corresponds to the native steric zipper pattern found in x-ray crystallography , and molecular polymorphism is possible when there exist similarly stable filament patterns . Furthermore , detailed characterization of individual energy terms allowed to identify key interactions driving the bilayer formation: van der Waals ( Lennard-Jones ) and hydrophobic interactions contribute the most to the selection and stabilization of steric zipper patterns . Key residues in a given peptide sequence contributing the most to were identified to be those buried in the dehydrated interface between two β-sheets , suggesting the importance of tight side chain packing at the interface . Once a β-sheet is formed , shape complementarity is the major factor determining the bilayer pattern . But we found that formation of a β-sheet type is more prone to be affected by kinetic factors . In particular , for short peptides , charged side chains change the preference from parallel to anti-parallel β-sheets , which is not necessarily energetically favorable for steric zipper formation . As the most stable filament patterns identified through our method aligned well with the corresponding x-ray structures , in addition to detailed characterization of energetics , our analysis opens the possibility of predicting the cross-β spine structure and polymorphism formed by short peptides in atomistic accuracy .
GNNQQNY and NNQQ form parallel β-sheets while VEALYL , KLVFFAE , and STVIIE form anti-parallel β-sheets . As experiments indicated that a β-sheet bilayer with a dehydrated interface between the two sheets is the basic building block of filamentous aggregates [26] , [27] , we considered possible bilayer patterns formed by two identical β-sheets . In the case of parallel β-sheet bilayers , we constructed ten possible patterns ( Fig . 2 ) . Naming schemes for these filaments are: F/B ( front/back ) : even- ( front ) or odd-numbered ( back ) side chains buried in the bilayer , P/A ( parallel/anti-parallel ) : relative direction between peptides in the two sheets . 1/2: two choices of side-chain registry in the steric zipper . FFP and BBP did not have 1 or 2 due to rotational symmetry with respect to the filament axis . Similarly , we constructed nine candidate bilayer patterns formed by two identical anti-parallel β-sheets ( Figs . 3 and 4 ) . For a single anti-parallel β-sheet , there are two possibilities , either face of a β-sheet composed entirely of even- or odd-numbered residues ( Areg ) or they alternate and appear on both faces ( Ainv ) [28] . VEALYL ( KLVFFAE ) has two possible Areg ( Ainv ) patterns with comparable number of backbone hydrogen bonds between neighboring peptides within a β-sheet , which we distinguish by 1 and 2 ( Fig . 3 ) . So there are β-sheet patterns of Ainv , Areg1 , and Areg2 for VEALYL , and Ainv1 , Ainv2 , and Areg for KLVFFAE . In forming a bilayer , an Ainv pattern can be either symmetric ( P ) or anti-symmetric ( A ) against 180° rotation along the filament axis . Furthermore , as in parallel β-sheet , there are two choices of side chain registry in the dehydrated interface of an AinvP bilayer , 1 and 2 . One potential problem with constructing β-sheet bilayer filaments is in side chain orientations . Once a peptide was within a β-sheet , its side chains did not easily rotate in simulations at 300 K , especially those buried in the dehydrated interface . This makes it difficult for the side chains to find proper orientations . For example , in the 1YJP structure of GNNQQNY , there are lateral hydrogen bonds through N2 , Q4 , and N6 in the dehydrated interface . At a higher temperature , however , side chains readily rotated to find their native-like orientations . We tested this by constructing a small BBA1 bilayer composed of 2 GNNQQNY peptides on each sheet , and performed a 2 . 5-ns simulation in implicit solvent at 350 K under a periodic boundary condition ( PBC; see Methods ) . Side chains buried within the bilayer rotated readily to form proper hydrogen bonds . Since amyloidogenic peptides generally have polar side chains , it is thus desirable to first perform a quick high temperature relaxation run for a small β-sheet bilayer and construct larger systems using the relaxed structure . Note that the 10 parallel and 9 anti-parallel β-sheet bilayer patterns that we considered do not exhaustively include all possibilities . However , they represent major sets of likely β-sheet bilayers in terms of side chain packing and backbone hydrogen bonding . These patterns cover eight classes of steric-zipper patterns proposed by Sawaya , et al . [27] ( Table S1 ) . After constructing the bilayer , peptides were in extended conformations , which subsequently relaxed during MD simulations ( e . g . , Fig . 2A , bottom; Fig . 5 . During 4-ns production runs of GNNQQNY bilayers , nearly all patterns maintained the integrity of β-sheets and dehydrated interfaces: the inter-layer distance fluctuated at most by 0 . 25 Å except for FBA2 whose sheets separated , and FBP1 that showed a significant fluctuation ( Fig . 6A ) . For those that had small root-mean-square deviation ( RMSD ) of atoms from the structure at the beginning of the production run ( Fig . 6B ) , the RMSD reached approximately steady values after about 1 ns , which was mostly less than 1 . 5 Å . By contrast , in previous simulations , less stable β-sheet configurations readily disrupted within several nanoseconds [33] , [37] . These simulations were performed at 330 K and the equilibration run was short , 50 ps . On the other hand , our simulations were at 300 K , and prior to production run we performed 2-ns MD in the GBSW implicit solvent , and then 1-ns equilibration in explicit water , to allow side chains in the bilayer to pack as much as possible . PBC provides additional stability by preventing exposure of reactive backbone hydrogen and oxygen [40] . Even so , we observed that non-native bilayer patterns have higher binding free energy than the native conformation . In the first set of simulations , we fixed the inter peptide distance ( d; Table 1 ) over the course of simulation by imposing a PBC ( see Methods ) . Among candidate patterns , BBA1 had the lowest , with the difference from the next lowest one being 1 . 11 kcal/mol ( ; K . Free energy was measured on a per peptide basis ) ( Fig . 7A , open circles ) . BBA1 corresponded to the x-ray structure; although it was constructed from initially flat β-sheets ( Fig . 2A , bottom ) , RMSD of heavy atoms from 1YJP was quite small , on average 1 . 18 Å . Hydrogen bonds between polar side chains at the dry interface ( N2 , Q4 , and N6 ) were also observed , as in 1YJP . The only major difference was in the orientation of the N3 side chain ( Fig . 8A , arrows ) . In 1YJP , it points to Q5 to form a side chain hydrogen bond . However , N3 and Q5 are exposed to water and would remain individually solvated , so the N3-Q5 hydrogen bond is likely a crystallization artifact in 1YJP . In fact , even when we imposed the N3-Q5 side chain hydrogen bond in the starting structure , it broke and the side chain of N3 rotated to the other way during the simulation . To check if the selection of BBA1 is robust , we performed additional tests . The profile of averaged locally over 1-ns intervals showed a consistent trend ( Fig . S1A ) . Although less stable or non-native patterns had slightly more variation in locally averaged over time , most patterns maintained their structural states , and the selection of BBA1 is clear from the the beginning of the production run ( cf . , Fig . S4 ) . This is likely due to the long initial preparatory simulations and the application of PBC mentioned above . We also repeated simulations for the most and a moderately stable patterns ( BBA1 and FFA1 ) with system sizes of 20 peptides instead of 12 , which resulted in no major difference ( Table 2 ) . Among other peptides that we tested , although the relative stability between a few patterns could not be distinguished , using 12 peptides gave generally satisfactory result . Note that the computational cost sharply increases with the system size , since there are several patterns to test for a given peptide , and NMA also has strong size dependence . To test the effect of helical twist [36] as well as the exposed edge , we performed additional MD simulations of the β-sheet bilayer candidates without the PBC along the filament axis . All bilayers did not dissociate and developed curvature , although the small system size made it difficult to characterize their helical pitch . BBA1 was still the most stable ( Fig . 7A , solid circles ) . Variation in energy due to changes in helical twist is less than that from different supramolecular packing patterns [46] , so the flat bilayer structure under PBC can be used to distinguish the relative stability among candidate patterns . Also note that the free energy difference between BBA1 and the rest is larger without PBC ( Fig . 7A ) , indicating that less stable structures suffer more from the edge effect . Unlike 1YJP that has one dominant energy minimum , if there are multiple minima with similar stability , molecular polymorphism may be possible . As a test , we applied the present approach to the peptide NNQQ , which has two x-ray structures differing in β-sheet packing patterns with distinct faces forming dehydrated interfaces and different inter-peptide distance , d [27] . Construction of β-sheet bilayers ( Fig . 2B ) , MD simulation , and calculation of followed the same procedure as for GNNQQNY . Candidate patterns were tested under PBC in two different sets of simulations with inter-peptide distances within a β-sheet , Å ( 2ONX ) or 4 . 92 Å ( 2OLX ) ( Note that d does not change under the rigid PBC; see Methods ) . When Å , FFA1 was the most stable pattern , although the native-like pattern was FBA1 , with a small , 0 . 66 kcal/mol difference in . ( Fig . 7B and Table S2 ) . This can be explained in terms of the interaction between two β-sheet bilayers . In the case of 1YJP , a dehydrated steric zipper is formed only between the β-sheets in the BBA1 pattern , while there are crystal water molecules outside . However , in both 2OLX and 2ONX , there is no crystal water and both sides of the β-sheet bilayer form additional steric zipper interface with neighboring sheets . In the case of FBA1 , it can repeat itself to build a laminated crystal . But when two FFA1 filaments stack , they must form a BB-pattern in between; BBP , BBA1 , or BBA2 ( Fig . S2 ) . Our calculation shows that BBA2 is the most stable among BB-patterns . Therefore , rather than individually , the average of FFA1 and BBA2 should be compared with that of FBA1 , where the former average is 0 . 48 kcal/mol higher . Although this is in agreement with the selection of FBA1 in 2ONX , the energy difference is narrower than the thermal energy at 300 K ( kcal/mol ) . When Å , the three most stable patterns were FFA1 ( −5 . 23 kcal/mol ) , FBA1 ( kcal/mol ) , and BBA1 ( −4 . 35 kcal/mol ) . The native-like patterns are BBA1 and FBA1 , whose average is only 0 . 44 kcal/mol ( 0 . 73 ) higher than that of FBA1 . Such indeterminacy is presumably due to the symmetric sequence of NNQQ , which has the same side chains in the same order on both faces . Consistent with our result , an ab initio calculation indicated similar stability of the two crystal lattices [57] . Additional simulations support the above result . To allow the system to choose the inter-peptide distance d instead of fixing it by imposing a rigid PBC , we used a constant temperature and pressure ( CPT ) dynamics , where dimensions of the simulation box parallel and normal to the filament axis were controlled to keep the pressure at 1 atm , while PBC was still maintained . Averaged over the 2-ns production run , values of d were consistent with those from crystal structures: Å ( cf . , Å ) , Å , and Å ( cf . , Å ) . The first and the second lowest configurations were FFA1 and FBA1 , respectively , consistent with results with a rigid PBC ( Fig . 7B ) . FBA1 had lower than the average between FFA1 and BBA1 by 0 . 88 kcal/mol , again comparable to . For the case where FF and BB patterns alternate ( Fig . S2 ) , we find that FFA1 is more stable than BBA1 . Thus the FFA1 bilayer may form first , which subsequently stack to form the BB-interface . As in the case of GNNQQNY , the most stable β-sheet bilayer patterns of NNQQ closely followed those of the respective x-ray structures . The RMSD of heavy atoms between the FBA1 structure at the end of the production run and 2ONX was 2 . 07 Å ( rigid PBC ) and 1 . 85 Å ( CPT ) , and the RMSD between 2OLX and BBA1 and FFA1 was on average 1 . 89 Å ( rigid PBC ) and 2 . 0 Å ( CPT ) ( Fig . 8C ) . Our approach was effective in calculating of anti-parallel β-sheet filaments as well . Out of nine candidate patterns considered ( Fig . 4A ) , the most stable configuration for VEALYL was the native-like AinvP2 , with lower than the second lowest Areg1BB by 1 . 00 kcal/mol ( Fig . 9A; Table S3 ) . As explained in Discussion , the selection of an Ainv over an Areg pattern could also be driven by electrostatics at early stages of assembly , as the negatively charged E2 side chains are further apart in an Ainv sheet . Heavy atoms of the AinvP2 filament had an RMSD of 2 . 73 Å from 2OMQ ( Fig . 8B ) . RMSD between other filament patterns and 2OMQ were significantly larger , e . g . , AinvP1 has 6 . 04 Å and Areg1BB has 9 . 10 Å . Furthermore , RMSD of the initially extended AinvP2 structure before the MD was on average 3 . 25 Å from 2OMQ , suggesting that AinvP2 structure indeed approached the x-ray structure after MD , sufficiently distinguishable from other patterns . As in GNNQQNY , calculation of over 1-ns intervals confirmed that the free energy profile across different patterns established almost from the beginning of the production run ( Figs . S1 and S5 ) . As we were able to identify the bilayer structures for peptides with known crystal structures , we applied our method to two peptides KLVFFAE and STVIIE , whose atomistic β-sheet bilayer structures are currently unknown . For KLVFFAE ( A ) , we tested pH 7 . 0 and 2 . 0 , at which the peptide self-assembles into fibers and nanotubes , respectively [48] . At pH 7 . 0 , our calculation indicated that AregFF is the most stable configuration , with 0 . 81 kcal/mol difference in to the next lowest configuration AregBB ( Fig . 9B , Fig . 10 , and Table S4 ) . Since the difference is marginal , as in the case of NNQQ ( Fig . S2 ) , AregFF and AregBB may stack to form a laminated fiber . Previously AregFB was suggested as the native β-sheet bilayer pattern [48] , where simulations were performed for 0 . 8 ns and the d/2 axial shift between two layers ( see Methods ) was not implemented . Their main criterion for selecting the bilayer pattern was the inter-layer distance , to match the 9 . 9-Å x-ray fiber diffraction peak . If we use the average distance between atoms in even- or odd-numbered residues as the inter-layer distance used in [48] , we get , on average , 9 . 9 Å ( AregBB ) , 10 . 9 Å ( AregFF ) , and 9 . 5 Å ( AregFB ) , where the last value is less than that from simulations in [48] possibly due to longer relaxation runs and the d/2 axial shift in our case . Although our result favoring the AregFF patterns differs from the AregFB pattern suggested in [48] , at least the selection of an Areg pattern is consistent with existing solid state NMR data [22] . Further experiments would be necessary to clarify the structural selection and the atomic origin of the 9 . 9-Å peak . However , when averaged over 1-ns intervals are followed , at pH 7 . 0 , energies of Ainv2P1 and Ainv2P2 decreased significantly over time , almost to the lowest levels ( Fig . S7 ) . To further test if this is due to any finite size effect , we performed additional simulations on Ainv2P1 , Ainv2P2 , and AregFF using 10 peptides per β-sheet , where the production run lasted 4 ns . Based on , Ainv2P1 and Ainv2P2 became even more stable compared to AregFF ( Table S4 ) . As explained in Discussion , although the Ainv bilayer might in fact be more stable than the Areg pattern , the latter may be kinetically selected at the single sheet level . At pH 2 . 0 , by contrast , Ainv1A was the dominant free energy minimum ( Fig . 9B ) , which nicely agrees with the result in [48] . In the case of STVIIE , candidate β-sheet bilayer configurations were similar to those of VEALYL ( Figs . 3C and 4A ) . We found that the Areg2FF pattern was the lowest in ( Figs . 9C , 10 , and Table S5 ) . The selection was again clear , since the next most stable Areg1BB was 2 . 0 kcal/mol higher in .
The binding free energy ( ) is composed of terms defined in Eq . 9 , which can be grouped into non-bonded energy ( ) , intramolecular ( ) , and entropic ( ) contributions . consists of ( 1 ) where and are respectively hydrophobic and electrostatic screening energies . Comparison among energy terms in reveals that is dominant over and entropic contributions ( Table 2 for GNNQQNY , and Tables S2–S5 for other peptides ) . This suggests that the peptide does not become internally strained or relaxed ( small ) , and it loses only small amount of vibrational entropy upon incorporating into the β-sheet bilayer: The bilayer structure is determined predominantly by non-bonded interactions . Next we compared the four terms in Eq . 1 among candidate bilayer patterns . For GNNQQNY , and contribute greatly for the native-like BBA1 pattern compared to others , while there is no particular preference for BBA1 in and ( Table 2 ) . Absence of charged side chains in GNNQQNY accounts for the little role played by electrostatic interactions ( ) . Unlike typical amyloidogenic peptides , GNNQQNY only has polar side chains , so that hydrophilic effect mediated by the surrounding shell of water molecules may play an important role in initially bringing these peptides into loosely formed aggregates , as in the assembly of collagen [58] , [59] . However , since water molecules between the two β-sheets are eventually expelled , hydrophilic effect including is not crucial for determining the side chain registry at the steric zipper interface . Therefore , although hydrophobic and van der Waals forces may not drive individual peptides in initial aggregation , they should be important in the final cross-β structure selection , where side chains within the bilayer pack by making direct contacts . For VEALYL , all three terms except for generally favor the native-like AinvP2 pattern ( Table S3 ) . An anti-parallel inverse β-sheet ( Ainv ) may have been favored since it has a single charged residue ( E2 ) placed alternatively on the two faces of a β-sheet , which was also observed for KLVFFAE at pH 2 . 0 . However , this is not a rigid rule , since STVIIE favored Areg2FF . In an Ainv-type β-sheet of STVIIE , rows of side chains are formed by T2–I5 , and V3–I4 . Due to the large size of the Ile side chain compared to those of T2 and V3 , both faces of an Ainv β-sheet are uneven , which is disadvantageous for steric zipper formation . By contrast , the Areg2FF pattern has the row of V3 side chains from each layer at the core of a tightly formed steric zipper interface ( see below ) . For NNQQ , there was no particular preference for native patterns in any of the four energy terms in ( Table S2 ) . Since the two faces of a parallel NNQQ β-sheet have identical side chains , they are nearly equally likely to form steric zipper interfaces , and native polymorphic patterns ( BBA1+FFA1 and FBA1 ) are favored only through the sum of all energy terms . The observation that van der Waals and hydrophobic interactions play a major role in selecting the bilayer pattern implies that interactions among side chains forming the dehydrated steric zipper , rather than among those exposed to water , are the major structural determinant . As an additional test , we analyzed free energy changes from a monomer to a single β-sheet , then to a β-sheet bilayer for the native-like pattern ( Fig . 11 ) . Although assembly from a monomer to a bilayer proceeds likely through multiple complex pathways , analyzing the free energy of a single β-sheet illustrates the role of steric zipper for stabilizing a bilayer . We evaluated the binding free energy of a β-sheet monolayer by ignoring one layer of β-sheet in double layer simulation trajectories . Since we use a continuum solvent model for free energy calculation , solvation effect is correctly taken into account even for the face that is originally buried in the bilayer . Similarly , in a previous study , the free energy of the monomer in a protein dimer was evaluated by ignoring the other monomer , yet the resultant free energy was comparable to that calculated from an isolated monomer simulation [51] . In contrast , MD simulation of a single Preg β-sheet of GNNQQNY ( Fig . 3 ) showed strong tendency to twist and became unstable under PBC . While other single β-sheets might be more stable , we did not further investigate this since it was not necessary for our calculation and our major focus was on the β-sheet bilayer filament as the major building block of amyloid fibrils suggested by experiments [3] , [27] . For all peptides tested , van der Waals energy ( ) was reduced the most from a monomer to a single β-sheet and then to the native bilayer . Hydrophobic energy ( ) contributed the second ( Fig . 11 ) . On the other hand , and ( the total electrostatic interaction ) changed marginally , except for KLVFFAE at pH 7 . 0 . Since the two charged side chains K1 and E7 lie on the same side of KLVFFAE , formation of an anti-parallel in-register β-sheet may be favored by electrostatic interactions , resulting in the AregFF pattern ( Table S4 ) . In the section ‘Possibility of hierarchical pattern selection in KLVFFAE , ’ we discuss how the AregFF pattern might be kinetically preferred over other potentially more stable patterns such as Ainv . Contribution by each residue to also supports that side chains at the steric zipper interface play a greater role compared to those exposed to water . For BBA1 of GNNQQNY , the residue-based profile of is consistent with its average B-factor in 1YJP ( Fig . 12A ) . The greatest contribution is by Q4 located at the core of the steric zipper , followed by N2 , revealing their stabilizing role . Odd-numbered residues facing water have comparatively higher . For G1 , is the highest , thus it plays a minimal stabilizing role . This is consistent with the similarity between 1YJP and the structure without G1 ( NNQQNY , PDB ID: 1YJO ) [26] . Similar trends were observed for NNQQ and KLVFFAE , where side chains between the bilayer had greater contributions to ( Fig . 12B and C ) . To gain further insight into the importance of side chain interactions in the bilayer interface , we tested the BBA1 bilayer formed by the Q4A mutant ( GNNAQNY ) . When comparing between patterns formed by different peptides , care should be taken since there is an uncertainty in the absolute magnitude of the free energy of monomers . To be clear , we performed two different types of monomer simulations ( see Methods ) : The 1 . 6-ns monomer simulation in explicit-water at 300 K as used for most systems ( Fig . 1 ) , and replica-exchange molecular dynamics ( REMD ) in the GBSW implicit solvent , with a total simulation time of 1 . 6 µs . In both cases , the peptides maintained mostly α-helical conformation ( Methods; see also Fig . S9 ) . Yet there were differences in the calculated energy of the monomer , which resulted in the BBA1 pattern of the Q4A mutant having lower than that of GNNQQNY by 2 . 6 kcal/mol when the monomer simulation as in Fig . 1 was used , but higher by 3 . 91 kcal/mol when the REMD was used to calculate the monomer energy . As mentioned above and further explained in Methods , due to the uncertainty in calculating the absolute magnitude of the free energy of the monomer , it is difficult to conclude whether the Q4A mutant forms a more or a less stable BBA1 bilayer . However , compared to Q4 in GNNQQNY , A4 in GNNAQNY clearly has a decreased stabilizing contribution ( increased energy per residue ) relative to other residues in the peptide ( Fig . 12A ) , which is consistent with the result in Ref . [37] on the destabilizing effect of the Q4A mutation . Yet the average inter-layer distance of the Q4A bilayer was 7 . 56 Å , which was narrower than that of the BBA1 pattern of GNNQQNY ( 9 . 25 Å ) . This is likely because the Ala side chain is smaller than the Gln side chain , making the Q4A mutant more advantageous to form a tighter steric zipper . As an additional test , for GNNQQNY and GNNAQNY , we took the structures after the production run , kept 4 peptides in each layer , and performed explicit water simulations at 330 K with 1-ns equilibration followed by 4-ns production run without imposing a PBC . No disruption was observed in both filaments , contradicting the result in Ref . [37] , where a Q4A mutant bilayer of the same size showed a strong destabilization at 330 K . This suggests that testing relative stability among candidate filaments using MD simulations at elevated temperatures does not guarantee that the most stable one survives the longest . Due to the finite ( and usually small ) system size , thermal disruption is a stochastic event , so even the most stable pattern may break earlier than less stable ones , which would be especially the case when the difference in stability is small . Conversely , as widely observed in the present study , less stable filaments may not break within the finite simulation time . For a more reliable test of stability , statistical average over a large number of simulations for a given bilayer pattern is necessary , which would be computationally very demanding . Our approach , on the other hand , uses one simulation trajectory for each pattern and provides contributions to the free energy by individual residues . Although it may not accurately predict whether a given point mutation will prevent fibril formation , it quantitatively shows how the mutated residue changes its contribution . Our result permits the possibility of the Q4A mutant assembling into a cross-β bilayer filament , whether or not the mutant filament is more or less stable compared to the original GNNQQNY bilayer . Further experiments are necessary to clarify the amyloidogenic propensity of Q4A . Overall , our analysis highlights the importance of forming a tight steric zipper interface in selection and stabilization of the bilayer pattern . The favorable van der Waals interaction stems from the tight side-chain interaction within a β-sheet as well as between two β-sheet layers . Moreover , solvent exposed surface area of each β-sheet is reduced by the formation of the steric-zipper interface [3] . It would be difficult for β-sheets with dissimilar side chains to form a tight steric zipper , which requires shape complementarity between two interfaces . This is consistent with the fact that , although amyloid fibrils can form over a wide range of amino acid sequences [60] , each fibril is composed of peptides with the same or similar sequence [61] . In a steric zipper , the row of side chains on a sheet along the filament fills the groove formed between two rows of side chains on the opposing sheet . Such a packing would be easier if side chains forming the row are identical or similar in shape , since the groove will then present a smooth interface . From the point of view of forming a steric zipper , a parallel β-sheet would thus be advantageous , which also has better side chain contacts within the row of side chains compared to an anti-parallel β-sheet , as suggested previously [62] . In contrast , as seen in VEALYL , KLVFFAE , and STVIIE , presence of charged side chains favors anti-parallel β-sheet due to electrostatic interactions . Indeed , among 15 crystal structures of cross-β spines published in [26] , [27] , [63] , 11 are parallel β-sheets , none of which has charged residues . Among 4 anti-parallel β-sheet structures , LYQLEN and VEALYL have charged residues . The remaining 2 anti-parallel β-sheet structures are polymorphic forms of the peptide MVGGVV . Although MVGGVV has no charged residue , it does not have the disadvantage in forming a steric zipper between anti-parallel β-sheets mentioned above: The identical side chains of V2 and V5 can stack sideways in an anti-parallel β-sheet , and two consecutive glycines provide enough room for side chain arrangement , which may also be relevant to its polymorphism as well . As observed in Results ( Table S4 ) , KLVFFAE had two classes of lowest free energy β-sheet bilayer configurations , which were formed respectively by Ainv2 and Areg monolayer patterns ( Fig . 3D ) . Although Ainv2 patterns could be comparably stable at the bilayer level , as a monolayer , of Areg is 1 . 84 kcal/mol ( 6-mer per layer ) or 0 . 78 kcal/mol ( 10-mer per layer ) lower than that of Ainv2 ( cf . , Fig . 11D ) . This is presumably due to the favorable electrostatic interactions between charged residues ( K1 and E7 ) in the Areg pattern , which agrees with previous experimental findings [22] , [48] . This suggests that specific β-sheet bilayer pattern may be hierarchically determined from the most favored monolayer to the bilayer pattern . As an additional test , we calculated of the parallel FFA1 pattern ( cf . , Fig . 2A ) formed by KLVFFAE at pH 7 . It can be clearly seen that side chains at the interface pack better compared to anti-parallel β-sheets ( Fig . 10 vs . Fig . S3 ) . Surprisingly , the calculated of FFA1 was −39 . 84 kcal/mol , which is 2 . 33 kcal/mol lower than that of the anti-parallel configuration , AregFF . However , when the comparison is made between β-sheet monolayers , of the FFA1 pattern is 6 . 77 kcal/mol higher than that of AregFF , mainly due to unfavorable electrostatic interactions among like charges in the parallel β-sheet ( ; Fig . 11D ) . But when an FFA1 bilayer is formed , K1 and E7 from opposing layers form salt bridges , resulting in reduced electrostatic repulsion . Therefore , based on the free energy decomposition of different β-sheet patterns , it can be seen that , although a parallel or Ainv-type β-sheets are preferred at the bilayer level for KLVFFAE at pH 7 . 0 due to better side chain packing , there may be a kinetic barrier originating from strong electrostatic repulsion at the single sheet level , resulting in the selection of the Areg β-sheet to form a bilayer . However , the difference in between the Ainv2 and Areg monolayers is marginal , especially in simulations with 20 peptides . Thus one cannot exclude the possibility of molecular polymorphism in KLVFFAE at pH 7 . 0 . It is conceivable that a mutant peptide KLVFFAQ at high pH and with multivalent ions may assemble into a parallel β-sheet filament . Since the only charged Lys residue of the mutant becomes neutralized and screened , the initial electrostatic drive for an anti-parallel β-sheet could be suppressed . However , the strong hydrophobic interactions by other residues may cause the system to collapse into amorphous aggregates . In such cases , reducing the solvent polarity , e . g . , by adding acetonitrile , could assist with β-sheet formation . In any case , once a β-sheet type is determined , the bilayer pattern can be predicted with a reasonable accuracy by comparing their . For longer peptides , side chain packing would dominate over electrostatic interactions , unless it has a proportionately large number of charged residues . Available solid-state NMR structures of amyloid fibrils composed of 40- or 42-residue long A β peptides are indeed parallel [23] , [64]–[66] . To further test the possibility that the β-sheet type is selected at the monolayer level , we calculated of other anti-parallel β-sheets , KLVFFAE at pH 2 . 0 , VEALYL and STVIIE . For KLVFFAE at pH 2 . 0 , the most stable monolayer was Ainv1 , with lower than the Ainv2 ( Areg ) pattern by 2 . 89 ( 3 . 61 ) kcal/mol . This is consistent with Ainv1A being the most stable bilayer pattern at pH 2 . 0 ( Table S4 ) . Similarly , the Areg2 monolayer of STVIIE forming the most stable Areg2FF bilayer had 1 . 24 kcal/mol lower in than Areg1 that forms the next most stable Areg1BB bilayer ( Table S5 ) . On the other hand , the Ainv monolayer of VEALYL ( forming the most stable bilayer; Table S3 ) had 1 . 59 kcal/mol higher in than Areg . Therefore , although generally the most stable β-sheet monolayer may be used to form the native-like bilayer , this is not universally applicable . As previously found [28] , structural evolution of oligomers is affected by both kinetic and energetic factors depending on the conformational relaxation time as well as the peptide concentration . It has long been suggested that backbone hydrogen bonds are major interactions in forming the amyloid cross-β structure [1] . In the CHARMM force field , the hydrogen bond energy is accounted for by the sum of electrostatic and van der Waals interactions between partially charged hydrogen bond donor and acceptor atoms . We decomposed to calculate the hydrogen bond energy of each backbone H--O pair , which is on average −1 . 98 kcal/mol for the BBA1 pattern of GNNQQNY and −1 . 28 kcal/mol for the AinvP2 pattern of VEALYL . Since there are 5–6 backbone hydrogen bonds per peptide in the native GNNQQNY and VEALYL configurations ( Fig . 3A ) , they contribute 24% ( 18% ) of of GNNQQNY ( VEALYL ) , which is indeed a significant fraction . Maximization of the number of backbone hydrogen bonds thus mainly favors in-register β-sheets over out-of-register ones . However , since we compare bilayers formed by in-register β-sheets that contain mostly the same number of backbone hydrogen bonds , hydrogen bonds cannot be a major determinant for the selection of a specific bilayer pattern . Our present analysis implies subtle roles played by kinetics and energetics in amyloid assembly . Kinetic trapping would be more relevant at early stages of assembly where basic features such as the β-sheet type ( parallel vs . anti-parallel , or Ainv vs . Areg ) are determined [28] . Once the β-sheet grows beyond the size of critical nucleus , it would be very difficult to change in any major way , such as adjusting the peptide registry within the sheet , or switching between parallel and anti-parallel types . In contrast to changes requiring major backbone hydrogen bond rearrangement , bilayer type selection would occur at a later stage , and more subject to an energetic control , because it involves shape complementarity between two faces that usually does not require any specific bond formation . Lack of specific bonds would allow conformational search and an optimal steric zipper packing would be achieved between two small β-sheets . Once such a fibril grows to a larger size , it will serve as a template for further growth , and structural rearrangement at the molecular level is unlikely , as experiments suggest [18]–[20] . Such a scenario is also consistent with a recent simulation of the aggregation of the GNNQQNY peptide , where initially formed parallel β-sheet dimers are stabilized by subsequent formation of a steric zipper bilayer [67] . The successful use of the binding free energy per peptide as the criterion for selecting the steric zipper pattern supports that the bilayer pattern is determined energetically . Molecular polymorphism would be possible if there are two or more most stable patterns with similar values of , as was seen in NNQQ . However , our analysis is valid only when a given peptide sequence forms a β-sheet bilayer , and it does not address whether the peptide forms a cross-β filament or not in a certain buffer condition , for which different approaches have been developed [42] . Nor can our approach predict unusual cross-β structures such as with a bend ( MVGGVV , PDB ID: 2OKZ ) [27] or a turn ( NNFGAIL , PDB ID: 3DGJ ) [63] . Nevertheless , the ability to calculate the binding free energy is a significant advance since detailed analysis of the contribution by different energy terms provides quantitative explanation for the selection of a particular steric zipper pattern . Our approach would also be useful for identifying the most probable structure among multiple solid-state NMR structures [23] , or for quantifying residue-specific contributions that may be therapeutically targeted for disruption of self-assembly .
Each peptide was modeled according to the experimental condition where its atomic coordinates were determined: GNNQQNY , NNQQ , VEALYL , and STVIIE had no capping moiety at both termini [27] . For KLVFFAE , the N- and C-termini were respectively acetylated and amidated [4] . Protonation status of titratable groups was determined based on pH of the corresponding experiments ( Table 1 ) . The type of a β-sheet and the inter-peptide distance d within a sheet were selected as summarized in Table 1 and Fig . 3 . Two β-sheets were put together to form a bilayer filament pattern , with an initial inter-layer distance of 10 Å . One layer was then shifted axially by d/2 to maximize the interdigitation of side chains between the bilayer . Such a shift is present in various x-ray structures [26] , [27] . Even when we started the simulation without the axial shift , it appeared spontaneously after the heating period in the implicit solvent environment , regardless of the boundary condition imposed on the filament axis . For all simulations we used CHARMM version 31 [68] with the param22 all-atom force field . We performed preparatory simulations in the GBSW continuum solvent environment incorporated in CHARMM [52] , to find proper side chain orientation . Lack of viscosity in GBSW aided rapid relaxation of side chains . Initially the system ( either a monomer or one of the bilayer patterns ) was relaxed through 3000 steps of energy minimization using the adopted basis Newton-Raphson ( ABNR ) algorithm . The system was heated from 0 K to 300 K for 60 ps , equilibrated at 300 K for 1 . 0 ns , followed by a 1 . 0 ns production run . The cutoff distance for the non-bonded interaction was 24 Å for the GBSW simulation . The final snapshot of each candidate was used as the initial structure for the explicit solvent simulation . We imposed a PBC to the filament axis by choosing the dimension of the simulation box parallel to the filament axis as Nd , where N is the number of peptides per layer . The final structure from the 2 . 06 ns implicit solvent simulation was put in an orthorhombic box containing TIP3 water molecules pre-equilibrated at 1 atm , 300 K . Water molecules were deleted whose oxygen atoms were within 2 . 9 Å from heavy atoms of the bilayer . The distance of 2 . 9 Å was chosen to ensure no water molecule was left within the bilayer after deletion , whereas the density of water was maintained by the constant pressure MD . The dimension of the solvation box was chosen large enough to prevent the interaction between the filament and its images except when a PBC was applied in the axial direction . In this case , the length of the box was the same as that of the filament . The transverse size of the box ranged between 50–66 Å , depending on the bilayer pattern tested . After putting water molecules , the system was energy minimized for 2000 steps using the ABNR method . Each configuration was heated for 100 ps then equilibrated for 1 . 0 ns . During equilibration , velocities were rescaled when temperature deviated from 300 K by more than ±5 K . A 2 . 0 to 6 . 0-ns production run followed ( Table 1 ) . When PBC was applied , the axial length of the filament ( i . e . 29 . 2 Å for a system composed of 12 GNNQQNY peptides ) was kept fixed while the transverse area of the simulation box fluctuated to maintain the constant pressure of 1 atm . In some cases , simulations were performed using the CPT dynamics where the axial length was adjusted ( Table 1 ) . Coordinates were saved every 0 . 5 ps during the production run . The cutoff distance for the non-bonded interaction was 12 Å for the explicit water simulation . We applied a similar procedure for a single peptide , which is required for the calculation of . We consider four states of a peptide: as a monomer or within a bilayer , either in vacuum or in solution: ( 2 ) is the free energy difference of the peptide in vacuum as an isolated monomer vs . in a bilayer; ( 3 ) includes covalently bonded energy terms associated with bond stretching , bond angle , and proper/improper dihedral angles . and are van der Waals and electrostatic energies in vacuum . , and are vibrational , translational and rotational entropy contributions [56]: ( 4 ) ( 5 ) ( 6 ) where is the i-th normal mode frequency , m is the mass of a single peptide , , h is Planck constant , ρ is the number density ( in units of M ) , σ is the symmetry factor of the molecule , and are three rotational moments of inertia . For the peptide within a bilayer , and were set to zero . For convenience , ρ was set to 1 M . Although this is higher than typical experimental value , ∼1 µM , a different choice only shifts overall by a constant factor without affecting conclusions of the present work . σ was unity because the peptide is an asymmetric molecule . is the solvation free energy of a monomer; ( 7 ) where is the non-polar ‘hydrophobic’ energy , proportional to the solvent accessible surface area . is the polar solvation free energy approximated by the generalized Born solvation model [52] . The GBSW facility in CHARMM was used to calculate these terms , which is known to reproduce the results calculated from the Poisson-Boltzmann equation approach within 2% errors [52] . Similarly , is the solvation free energy of the bilayer state; ( 8 ) which was again calculated using GBSW . In the above , energy terms calculated for a bilayer were divided by the number of peptides in the bilayer . Finally , , the Gibbs free energy of bilayer formation ( per peptide ) , can be calculated as , considering Eq . 2 , ( 9 ) where and . After simulation , water molecules were deleted and energy terms except for entropic contributions were calculated for each frame , and averaged over each 1 ns period . To calculate , we took 10 snapshots per each 1 ns period , each of which was energy minimized and normal modes were calculated using the distance dependent dielectric constant ( KLVFFAE and STVIIE ) or in the GBSW solvation environment . The choice of solvation model may shift at 300 K by ±2 kcal/mol , but this does not affect our conclusion regarding relative stability among different bilayer patterns in a given solvent model . Eq . 4 was used to calculate the vibrational entropy , which was averaged over the snapshots to estimate . We estimated the standard deviation of the calculated as follows . First and ( Eq . 1 ) were averaged and respective variances , and , were calculated over the production run . For the monomer , we did not consider its energy fluctuation since the energy of monomer only affects the overall magnitude of ( see below ) . Since the variance of the sum of two independent random variables is the sum of individual variances [69] , we get ( 10 ) In simulations , a monomer is more prone to conformational fluctuation than β-sheet bilayer filaments . Thus one should be careful in interpreting the magnitude of . The fluctuation in the free energy of the monomer can induce an overall shift in the profile . Thus , although our approach is effective in comparing relative stability among bilayer patterns for a given peptide , it would be difficult to use the calculated to address amyloidogenecity of a peptide , or to compare relative stability between bilayer patterns composed of different peptides . For additional comparison between the stability of GNNQQNY and GNNAQNY bilayers , we performed REMD [70] for the corresponding monomers . We prepared 16 replicas of each monomer , with simulation temperatures spanning from 275 K to 600 K . The GBSW continuum solvation model was used . Temperature swap trials were attempted every 20 ps according to the Metropolis criterion and lasted for 100 ns , with a total simulation time of 1 . 6 µs . During this period , each replica visited the lowest ( highest ) temperature at least more than 22 ( 69 ) times . was calculated by energy minimizing the 5000 REMD structures at 300 K and performing NMA on each . Compared to the all-atom explicit water simulation of a monomer , of GNNQQNY increased only by +0 . 40 kcal/mol , while for GNNAQNY , it decreased by −7 . 65 kcal/mol . Thus when the monomer energy based on the REMD simulation is subtracted ( Eq . 9 ) , increases more for GNNAQNY than for GNNQQNY , which is opposite to the case when monomer energy from the constant-temperature simulation ( Fig . 1 ) was used . The DSSP algorithm [71] allowed detailed characterization of each monomer conformation at 300 K . The most abundant conformation of GNNQQNY was α-helix , with an occurrence probability of 58% . Hydrogen-bonded turn and π--helix appeared 13% and 11% , respectively ( Fig . S9 ) . However the above secondary structures possess very similar conformation , as the inset of Fig . S9 shows . This agrees with the corresponding constant temperature MD simulation , where α-helix was the dominant conformation . Similarly , GNNAQNY had α-helix ( 51% ) , π--helix ( 14% ) , and hydrogen bonded turn ( 11% ) .
|
Accumulation of amyloid fibrils is a salient feature of various protein misfolding diseases . Recent advances in precision experiments have begun to reveal their atomistic structures . Quantitative elucidation of how the observed structures are selected over other possible filament patterns would provide much insight into the formation and properties of amyloid fibrils . Using computer simulations and structural modeling , we demonstrate that the most stable filament pattern corresponds to the experimentally observed structure , and molecular polymorphism , selection of two or more patterns , is possible when there are more than one most stable structures . Ability to predict the structure allows for more detailed analysis , so that , for example , we can identify the most important residue for stabilizing the structure that could be therapeutically targeted . Our analysis will be useful for comparing different amyloid structures formed by the same protein or when delineating roles of different intermolecular forces in filament formation .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"neurological",
"disorders/alzheimer",
"disease",
"neurological",
"disorders/prion",
"diseases",
"computational",
"biology/molecular",
"dynamics",
"biophysics/protein",
"folding"
] |
2009
|
Thermodynamic Selection of Steric Zipper Patterns in the Amyloid Cross-β Spine
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The emergence of replicases that can replicate themselves is a central issue in the origin of life . Recent experiments suggest that such replicases can be realized if an RNA polymerase ribozyme is divided into fragments short enough to be replicable by the ribozyme and if these fragments self-assemble into a functional ribozyme . However , the continued self-replication of such replicases requires that the production of every essential fragment be balanced and sustained . Here , we use mathematical modeling to investigate whether and under what conditions fragmented replicases achieve continued self-replication . We first show that under a simple batch condition , the replicases fail to display continued self-replication owing to positive feedback inherent in these replicases . This positive feedback inevitably biases replication toward a subset of fragments , so that the replicases eventually fail to sustain the production of all essential fragments . We then show that this inherent instability can be resolved by small rates of random content exchange between loose compartments ( i . e . , horizontal transfer ) . In this case , the balanced production of all fragments is achieved through negative frequency-dependent selection operating in the population dynamics of compartments . The horizontal transfer also ensures the presence of all essential fragments in each compartment , sustaining self-replication . Taken together , our results underline compartmentalization and horizontal transfer in the origin of the first self-replicating replicases .
One of the crucial questions in the origin of life is how molecules acquired the capability of undergoing open-ended Darwinian evolution [1 , 2] . A potential answer is offered by the template-directed self-replication of a replicase , a replicase that can replicate itself . To determine whether such self-replication is demonstrable in RNA , considerable effort has been devoted to the artificial evolution of RNA polymerase ribozymes [3–10] . A recent milestone in this effort is the demonstration of ‘riboPCR , ’ the exponential amplification of RNA through a PCR-like mechanism catalyzed entirely by RNA [8] . The glaring issue , however , has been that the replicases synthesized so far have limitations in processivity and fidelity , so that they can replicate only oligomers much shorter than themselves ( or long unstructured cytidine-rich polymers , which exclude the ribozymes themselves ) . As a potential solution to this problem , Mutschler et al . and Horning et al . have recently proposed the fragmentation and self-assembly of a replicase . According to their proposals , a replicase is fragmented into multiple sequences that are short enough to be replicable by the replicase and , moreover , capable of self-assembling into a functional replicase [7 , 9] . The possibility of reconstituting a functional ribozyme from its fragments through self-assembly has been experimentally demonstrated [7 , 9 , 10] , attesting the chemical plausibility of the proposals . However , the exponential amplification of multiple distinct fragments raises a question about the dynamical stability of the proposed autocatalytic system . The continued replication of fragmented replicases requires the sustained production of all its essential fragments in yields proportional to the stoichiometric ratio of the fragments in a replicase [11–13] . However , each fragment is replicated by the replicase and thus grows exponentially . If some fragments was replicated persistently faster than the others , the former would out-compete the latter , causing a loss of some essential fragments and hence the cessation of self-replication . The above consideration led us to examine whether and under what conditions fragmented replicases achieve continued self-replication . Using mathematical modeling , we discovered that the fragmented replicases fail to display continued self-replication under a simple batch condition . Replication is inevitably biased toward a subset of the fragments owing to positive feedback inherent in the replication cycle of the fragmented replicases , and the loss of fragment diversity eventually halts self-replication . To find a way to resolve the above instability , we next examined the role of compartmentalization . Our model assumes a population of protocells ( primitive cells; hereinafter referred to as “cells” ) , each encapsulating a finite number of fragments and replicases . We found that compartmentalization , in principle , allows the continued self-replication of the replicases by the stochastic correction mechanism [14 , 15] . This mechanism selects fast-growing cells with a better combination of the fragments by removing cells that cannot grow or grow only slowly . We found , however , that there is severe restriction to the number of molecules in a cell and to the number of cells , for this mechanism to work . Indeed the stochastic correction mechanism works only if the number of fragments per cell is not large and if a sufficient number of cells exists . Furthermore , with this selection , a large number of cells are discarded , and a large number of fragments is thrown out without producing functional replicases . Hence , we need some other factors beyond the stochastic correction mechanism in order to realize a robust and effective replication system . Finally , we show that horizontal transfer between cells provides an effective mechanism for the continued replication of the fragmented ribozymes . One may naively expect that such horizontal transfer impedes the stochastic correction mechanism because by molecule exchange between cells each compartment is not perfectly separated ( however , see [16] for a contrasting expectation ) . Therefore , horizontal transfer might be expected to be detrimental to the continued self-replication of the fragmented replicases . On the contrary , we found that the horizontal transfer of intermediate frequencies substantially stabilizes the system to such an extent that the parameter constraints imposed by the stochastic correction mechanism are almost completely removed .
We consider the simplest model of fragmented replicases , in which a catalyst consists of two fragments . The fragments ( denoted by X and Y ) self-assemble into the catalyst ( denoted by C ) , and the catalyst disassembles into the fragments as follows: X + Y → k f C , C → k b X + Y . ( 1 ) We assume that the catalytst cannot replicate its own copies , but can replicate its fragments because shorter templates are more amenable to ribozyme-catalyzed replication as mentioned above . Therefore , X + C → k x 2 X + C , ( 2 ) Y + C → k y 2 Y + C , ( 3 ) where the monomers are ignored under the assumption that their concentrations are buffered at constant levels , and complementary replication is ignored for simplicity . In the presence of the catalyst , each fragment replicates at a rate proportional to its copy number . Hence , the fragments undergo exponential amplification .
First , we show that the replication of the fragments X and Y are unstable in a batch condition: replication is biased toward either of the fragments even if the rate constants for X and Y are identical , and the minor fragment is gradually diluted out from the system , so that the replication of the catalysts eventually stops . In this paper , we mainly focus on the situation where the rate constants are equal ( kx = ky = k ) because our results remain qualitatively the same as long as the difference between kx and ky is sufficiently small . We assume that the reactions undergo in a well-mixed batch condition so that the dynamics of the concentrations of X , Y , and C ( denoted by x , y , and c , respectively ) are written as follows: d x d t= ( - k f x y + k b c + k x c ) - x μ , ( 4 ) d y d t= ( - k f y x + k b c + k y c ) - y μ , ( 5 ) d c d t= ( k f x y - k b c ) - c μ , ( 6 ) where μ = k ( x + y ) c . In the right-hand side of these equations , the first terms in the brackets represent chemical reactions , and the second terms multiplied by μ represent dilution . The dilution terms are defined so as to keep the total molecular mass x + y + 2c constant ( the dilution terms could alternatively be defined as μ = −kfxy + kbc + k ( x + y ) c , in which case the total concentration x + y + c , rather than the total molecular mass x + y + 2c , would be kept constant; however , results would not essentially change ) . Within the brackets enclosing the reaction terms , the first and second terms represent forward and backward reactions of ( 1 ) , respectively . The third terms , which are present only in Eqs ( 4 ) and ( 5 ) , denote the replication of X and Y through reactions ( 2 ) and ( 3 ) , respectively . By introducing variables xtot = x + c and ytot = y + c , one can write d d t ( x t o t y t o t ) = k c 2 y t o t 2 ( x t o t - y t o t ) . ( 7 ) This equation indicates that a steady-state solution satisfies xtot = ytot . This solution is , however , unstable: a small increase in , say , xtot over ytot gets amplified because k c 2 / y t o t 2 is always positive , and , as a consequence , replication is biased to X . Intuitively , when xtot is slightly greater than ytot , the amount of free fragments x must also be greater than y because the same amount of X and Y are incorporated into catalysts . Therefore , the replication of X occurs more frequently than that of Y because more templates of X are available . As a result , the increase of xtot is greater than that of ytot . Because of this positive feedback , the concentration of the minor fragment Y gradually decreases as it is diluted out from the system , and , as a consequence , that of the catalysts C also decreases . Finally , the replication reaction stops once the catalysts are lost from the system . The instability of replication under a batch condition can be generally demonstrated for catalysts composed of an arbitrary number of fragments by straightforwardly extending the above model ( S1 Text ) . The introduction of compartments and their competitions can overcome the unstable replication . When the system is compartmentalized into a number of cells , stochasticity in cell compositions , competition for growth and division of cells provide a possible solution to avoid the loss of fragments: As the cells grow and eventually split into two with fragments distributed randomly between the daughter cells , cells with both X and Y fragments continue growth , while cells without either of them cannot grow . By introducing such a stochastic correction mechanism at the cell level [14] , one expects that the instability by the positive feedback at the molecule level can be resolved . To investigate this , we assume that the fragments and their assembly to function as a catalyst are partitioned into Ncell cells: the reactions occur in each cell ( Fig 1A ) . We adopted stochastic simulation using Gillespie algorithm [17] for reactions ( 1 ) – ( 3 ) . We assume that the volume of each cell is proportional to the number of fragments inside , and as the number of fragments increases in a cell , the cell grows . When the total number of fragments reaches a threshold VDiv , the cell divides with the components randomly partitioned into two daughter cells . Here , at the division event , one randomly-chosen cell is removed to fix the total number of cells Ncell . By this cell-cell competition , cells with biased composition of X and Y are selected out because their growth is slow . The relevant parameters for controlling the effect of compartmentalization are the division threshold VDiv and the number of cells Ncell . Fig 2A shows sets of the parameters with which the stochastic correction mechanism can avoid the unstable replication , by suppressing the positive feedback and selecting cells keeping both fragments . If VDiv is very small ( of the order of 10 ) , the stochasticity of cell components is too strong to maintain both fragments continuously and either of them is lost for all cells . Hence , the system cannot continue growth . On the other hand , if VDiv is too large , stochasticity in components decreases . In each cell , the balance of fragments is broken , and the replication is biased to either of X or Y . Then , components of each cell are dominated by either of free X or Y , and the number of catalysts in dividing cells gradually decreases to one because at least one catalyst is necessary to replicate fragments ( Note that the cells with a single catalyst can divide but cannot grow because the cells cannot make two of itself ) . Even when the Ncell cells are separated into the equal number of X-dominant and Y-dominant cells , the stochastic correction mechanism does not work because VDiv is too large . Thus , the random drift will finally result in bias to either of X-dominant or Y-dominant cells . By division events , daughter cells without catalysts randomly replace remaining cells , therefore , the cells with catalysts are finally removed from the system . For values of VDiv in-between , some of Ncell cells keep both X and Y , and can continue the replication . Besides VDiv , the number of cells Ncell is also restricted , to maintain such cells keeping both fragments X and Y . At division events , dividing cells without both fragments randomly replace remaining cells . Hence , when the number of cells Ncell is small , all the cells with both fragments will be finally removed . As the number of cells Ncell increases , the probability that all the cells with both fragments are removed decreases . As a result , the range of VDiv with stable replication increases . Note that the above mechanism is based on the selection of fast-growing cells , and a number of fragments are thrown out with the removal of cells , although they are still functional if combined across the cells . Without the selection in cell population nor the restriction to VDiv , horizontal transfer of fragments between cells rescues the loss of fragments and enables continuous replication by maintaining the balance between X and Y . If the X-dominant and Y-dominant cells coexist in the cell population , the transfer between cells avoids loss of fragments for both cells by supplying fragments to each other because each fragment is in excess for cells on one side but lacking for cells on the other side . For the purpose , we consider random mutual transfers of molecules among the Ncell cells ( Fig 1B ) . To implement the transfer , we consider reactions , X → D 0 , Y → D 0 , C → D 0 so that the X , Y and C are removed from a cell , respectively , with rate in proportional to each concentration , i . e . , Dx , Dy , and Dc . This gives diffusion out of the cell . At the same time , the component is added to another randomly-chosen cell . The results are not sensitive even if the fragments are partially lost in this process ( S2 Text ) . With the transfer among cells , the replication of the fragments is stabilized when the transfer constant D is small . In fact , the constraints of VDiv and Ncell are drastically eliminated ( Fig 2B ) . As long as the parameters are not extremely small , the stable replication continues . For small positive values of D ( Fig 3B and 3C ) , the cell keeps on growing with the coexistence of X and Y molecules in each cell , even for large VDiv where only X-dominant or Y-dominant cells remain for D = 0 ( Fig 3A ) . Here , the asymmetry between the fractions of the major and minor fragments gets smaller as D increases . In addition , two types of cells , X-dominant and Y-dominant cells coexist roughly with equal population ( Fig 3B ( ii ) and 3C ( ii ) ) . As D is increased further ( Fig 3D ) , the system gets unstable and only either of X or Y remains . This is natural , because for a large D limit , the system is well mixed , and the system is reduced back to the case without compartmentalization . The above result remains valid even if kx ≠ ky . In this case , the number ratio between X-dominant cells and Y-dominant cells changes to compensate for the difference between kx and ky ( S3 Text ) . To answer why the small rates of transfer stabilizes the system , we approximate the dynamics of the population of cells by considering the dynamics of two subsystems between which the fragments are transferred . We assume an equal population of X-dominant and Y-dominant cells as two subsystems of an equal volume , denoted as subsystem 1 and 2 , respectively . We write the total concentration of X ( the total of free Xs and Cs ) of each subsystem as x t o t 1 and x t o t 2 , and the total of Y as y t o t 1 and y t o t 2 . Likewise , the concentration of free X , free Y , and C are denoted as xi , yi , and ci , respectively . In each of the subsystem i ( i = 1 , 2 ) , the dynamics of the reactions is written as Eqs ( 4 ) to ( 6 ) . Then , the time-derivative of the variable x t o t i = x i + c i is obtained by adding both sides of Eqs ( 4 ) and ( 6 ) as , d x t o t i d t = d ( x i + c i ) d t = k x i c i - ( x i + c i ) μ i , = k ( x t o t i - c i ) c i - x t o t i μ i , ( 8 ) where we assume kx = ky = k . Likewise , the time-derivative of y t o t i is obtained by adding both sides of Eqs ( 5 ) and ( 6 ) as d y t o t i d t = d ( y i + c i ) d t = k y i c i - ( y i + c i ) μ i , = k ( y t o t i - c i ) c i - y t o t i μ i . ( 9 ) The dilution rate μi is defined as μ i = k ( x i + y i ) c i = k x i c i + k y i c i = k ( x t o t i - c i ) c i + k ( y t o t i - c i ) c i = k ( 1 - 2 c i ) c i , so that the total concentration x t o t i + y t o t i is kept at one . The above definition of μi is obtained by setting the sum of the right-hand-sides of Eqs ( 8 ) and ( 9 ) to zero and x t o t i + y t o t i to one . This dilution corresponds to the decrease of concentrations due to the volume growth of a subsystem . In this section , we assume that the volumes of the two subsystems are kept identical to each other , ignoring the dynamics of the volumes ( in the next section , we relax this assumption and investigate a mechanism that maintains the balance of the volumes ) . In addition to the reactions , the components are transfered between the subsystems . Thus , the changes of x t o t i are written as x ˙ t o t 1 = d x t o t 1 d t= F 1 - D 2 x t o t 1 + D 2 x t o t 2 , ( 10 ) x ˙ t o t 2 = d x t o t 2 d t= F 2 - D 2 x t o t 2 + D 2 x t o t 1 . ( 11 ) where F i = k ( x t o t i - c i ) c i - x t o t i μ i denote the right-hand-side of Eq ( 8 ) . The second and third terms in Eqs ( 10 ) and ( 11 ) denote average out- and in-flow of the components X by the transfer , respectively . These average flows are estimated as follows: the amount of the fragment X diffusing out from the subsystem 1 is D x t o t 1 , but half of them is returned to the subsystem itself because , in our simulation , the population of cells is divided into X-dominant and Y-dominant cells with the equal population of Ncell/2 and each fragment diffusing out from a cell is randomly re-distributed into one of the cells , i . e . , half of the fragments are distributed into X-dominant cells . Thus , the effective amount of fragments transferred from subsystem 1 to subsystem 2 is D x t o t 1 / 2 . In the same manner , the effective amount of the fragment X for subsystem 1 transferred from subsystem 2 is D x t o t 2 / 2 . The diffusion terms for Y are obtained in the same manner . The fixed-point solutions of Eqs ( 10 ) and ( 11 ) are analytically obtained for kf = kb by the following approximations . We first assume that the dynamics of reaction ( 1 ) is much faster than those of reactions ( 2 ) and ( 3 ) , and transfers . Under this assumption , the condition kfxiyi = kbci holds . For kf = kb , the condition is re-written in terms of x t o t i and y t o t i as ( x t o t i - c i ) ( y t o t i - c i ) = c i . Thus , the concentration of ci is obtained as c i = 1 - 1 - x t o t i y t o t i . Moreover , because x t o t i and y t o t i are highly asymmetric , i . e . , x t o t i y t o t i ⪡ 1 , as predicted by Eq ( 7 ) and confirmed in Fig 3B and 3C , we approximate ci as follows: c i = 1 - 1 - x t o t i y t o t i ≈ x t o t i y t o t i / 2 . By substituting the expression for ci into Eqs ( 10 ) and ( 11 ) , and solving them in a steady-state condition , d x t o t i / d t = 0 , the stable fixed point is obtained as x t o t 1 = 1 2 ( 1 + 1 - 4 2 D / k ) , ( 12 ) and x t o t 2 = 1 2 ( 1 - 1 - 4 2 D / k ) . ( 13 ) Here , we assume the dominant fragment of subsystem 1 is X and that of subsystem 2 is Y: x t o t 1 > 1 / 2 and x t o t 2 < 1 / 2 . Further , only the ratio of the transfer constant D to the replication rate k matters so that we assume k = 1 without loss of generality . Eq ( 13 ) is compared with the results of stochastic simulations in Fig 4 . For large transfer constants D , Eq ( 13 ) agrees well with the simulations , attesting the validity of the approximations involved in Eq ( 13 ) . For small transfer constants , however , Eq ( 13 ) underestimates the total concentration of the minor fragment x t o t 2 in the simulations . This underestimation is due to the fact that in the simulations , cells must possess at least one catalyst molecule to divide; that is , x t o t 2 cannot decrease below 1/VDiv . The presence of such a critical value for x t o t 2 cannot be predicted by Eq ( 13 ) , which is continuous , hence the underestimation . To study further the stability of the solution , we plot the flow [a direction of the vector ( x ˙ t o t 1 , x ˙ t o t 2 ) ] of Eq ( 10 ) in Fig 5 . The steady-state solutions satisfy both x ˙ t o t 1 = 0 and x ˙ t o t 2 = 0 , therefore , they are represented as the crossing points of two nullclines [set of ( x t o t 1 , x t o t 2 ) satisfying x ˙ t o t 1 = 0 or x ˙ t o t 2 = 0 , indicated by blue and orange curves ( see left-top panel ) ] . For D = 0 ( Fig 5A ) , a solution exists at ( x t o t 1 , x t o t 2 ) = ( 1 / 2 , 1 / 2 ) ( indicated by the light-blue square ) . However , it is unstable because the flows ( arrows ) point outward from the solution . Then , the system moves away from the solution by any tiny perturbation . The flows point toward each corner of the plane ( indicated by the red triangles ) , where either of X or Y is lost and cells cannot grow . For small positive values of D ( Fig 5B to 5D ) , stable fixed points ( shown in red circles ) appear to which the flows are directed from all directions , in addition to unstable fixed points ( shown in blue squares ) and the trivial solutions ( x t o t 1 , x t o t 2 ) = ( 0 , 0 ) , ( 1 , 1 ) ( shown in red triangles ) . Note that there exist two stable fixed points ( red circles ) for each D ( Fig 5B to 5D ) , and the solution in Eq ( 12 ) corresponds to the right-bottom one . As D increases , a bifurcation occurs at D = 0 . 02 ( Fig 5E ) so that the stable fixed points for D ≤ 0 . 02 turn to be unstable ( green stars ) . To understand this bifurcation , we consider eigenvectors v1 , v2 of Jacobian matrix of Eq ( 10 ) for the eigenvalues λ1 and λ2 . At the stable fixed points , they are obtained as v1 = ( 1 , 1 ) and v2 = ( 1 , −1 ) ( see left-top panel in Fig 5 ) . The direction of v1 determines the asymmetry between X and Y in both subsystems . By moving along the v1-direction of the plane , the amount of x t o t 1 + x t o t 2 either increases or decreases while y t o t 1 + y t o t 2 = 2 - ( x t o t 1 + x t o t 2 ) decreases or increases , respectively . On the other hand , the direction of v2 corresponds to the asymmetry between subsystems 1 and 2 for the fragments of X . By moving along the v2-direction of the plane , the amount of x t o t 1 increases or decreases while x t o t 2 decreases or increases , respectively . The corresponding eigenvalues for v1 and v2 are calculated as λ 1 = 5 D - 2 D 2 and λ 2 = 4 D - 2 D 2 , respectively . As D increases , a bifurcation occurs first in v1-direction at D* = 0 . 02 which is obtained from 5 D * - 2 D * 2 = 0 . In fact , the flows ( arrows ) at the fixed point ( green stars ) are in the parallel direction of v2 , and point outward in the v1-directions as D is increased further . This corresponds to the case in which the symmetry between X and Y breaks and only either of X and Y remains in both systems . The estimated value of D* agrees with the results of our simulation ( Fig 3 ) . In the two subsystems , the bifurcation also occurs in v2-direction at D+ = 0 . 03125 , as obtained from 4 D + - 2 D + 2 = 0 , corresponding to the symmetry between subsystems 1 and 2 . At the bifurcation point , the three fixed points ( one unstable and two stable points in v2-directions; shown all in light-blue squares ) merge to one fixed point ( Fig 5G ) . The behavior of the bifurcations can be understood as follows . The system has two kinds of symmetry , one between fragments X and Y , and one between subsystems 1 and 2 . For the stable replication , the symmetry between X and Y should be maintained because both fragments are essential . On the other hand , the symmetry between subsystems 1 and 2 should be broken because each fragment should be in excess for one subsystem , but lacking for the other subsystem . The two subsystems ‘help’ each other by the transfer of molecules . The former symmetry is maintained for 0 ≤ D < D* and breaks for D > D* . On the other hand , the latter symmetry is broken in the range 0 ≤ D < D+ . To meet the two conditions for the stable replication , the values of D are restricted as 0 < D < D* = 0 . 02 because D* < D+ ( D = 0 is eliminated by the condition each subsystem should contain both fragments ) . In the previous section , we confirmed the stable replication by small rates of horizontal transfer , by assuming that the populations of two cell types are equal . Here , we show that the state of an equal volume , i . e . an equal population of X− and Y−dominant cells , is stable and selected as a result of a frequency-dependent selection . To analytically investigate the stability of the state , we consider the volume fractions of the subsystems 1 and 2 are slightly different from 1/2 , to be replaced by 1/2 + ϵ and 1/2 − ϵ , respectively , with ϵ as a small number . Then the dynamics Eqs ( 10 ) and ( 11 ) are d x t o t 1 d t = F 1 - D ( 1 2 + ϵ ) x t o t 1 + D ( 1 2 - ϵ ) x t o t 2 , ( 14 ) d x t o t 2 d t = F 2 - D ( 1 2 - ϵ ) x t o t 2 + D ( 1 2 + ϵ ) x t o t 1 , ( 15 ) where the replication and the dilution terms due to the volume growth are written as F i = - x t o t i 2 ( 1 - x t o t i ) 2 ( 1 - 2 x t o t i ) / 4 by substituting the approximation c i = x t o t i y t o t i / 2 . Below , we show that the growth rate of the minor subsystem 2 with the fraction 1/2 − ϵ ( for ϵ > 0 ) increases and the major subsystem 1 decreases , causing the fraction of the two subsystems to go back to equal . First , we write the concentrations of X at the steady state as x t o t 1 = x * + δ 1 and x t o t 2 = 1 - x * + δ 2 where x * = 1 2 ( 1 + 1 - 4 2 D ) is the solution for ϵ = 0 ( Eq ( 12 ) ) , and δ1 and δ2 are deviations caused by the introduction of ϵ , respectively , for x t o t 1 and x t o t 2 . Then , from the steady state condition of Eqs ( 14 ) and ( 15 ) , one gets δ 1 = δ 2 = D ϵ 2 / 2 - 4 D . The growth rates μi ( i = 1 , 2 ) are given by ( x t o t i - c i ) c i + ( y t o t i - c i ) c i so that μ 1 = μ * - γ ( D ) ϵ , ( 16 ) μ 2 = μ * + γ ( D ) ϵ , ( 17 ) where μ * = { 1 - x * ( 1 - x * ) } x * ( 1 - x * ) 2 is the growth rate at ϵ = 0 , and γ ( D ) = 1 - 2 2 D 2 1 - 4 2 D D > 0 . Eqs ( 16 ) and ( 17 ) show that the growth rate of subsystem 1 decreases with ϵ , whereas that of subsystem 2 increases with ϵ . When ϵ > 0 , i . e . , the volume of subsystem 1 exceeds that of 2 , the concentrations of X in both subsystems 1 and 2 increase ( δ1 = δ2 > 0 ) . For the subsystem 1 , the fragment X is majority ( x t o t 1 = x * > 1 / 2 ) , therefore , the asymmetry between X and Y is enhanced by the increase of X . On the other hand , the fragment X is the minority in subsystem 2 , and the composition of X and Y gets close to be symmetric by δ2 . Because the growth rate is maximized when the concentrations of X and Y are equal , the growth rate of subsystem 1 decreases , while that of subsystem 2 increases by the factor γ ( D ) > 0 ( see Eqs ( 16 ) and ( 17 ) ) . Consequently , the volume ratio of the two subsystems eventually goes back to equal . The above analysis indicates that the frequency-dependent selection operates if D > 0 . However , in our simulations , the replication is unstable for small values of D . This discrepancy is due to the fact that discreteness in the number of molecules is taken into account in the simulations ( S4 Text ) .
In summary , we have shown that the self-replication of fragmented replicases is unstable under a simple batch condition . Replication is biased towards a subset of the fragments and eventually stops due to the lack of an essential fragment . Although the stochastic correction mechanism induced by compartmentalization helps , substantial variations in the cell population are required to overcome the biased replication . This sufficient degree of variations postulates that the number of molecules VDiv in a cell should not be large , whereas a sufficient number of cells Ncell is needed . Hence , the stochastic correction mechanism imposes severe restrictions on the number of molecules per cell and the population size of cells . Then , we have shown that the horizontal transfer of intermediate frequencies provides a remedy: it gives an effective and favorable solution to the instability of the fragmented replicases . The horizontal transfer allows for the exchange of molecules between the two types of biased ( X-dominant and Y-dominant ) cells . Hence , this horizontal transfer relaxes the instability . Without resorting to stochastic variations , the two types of cells coexist by frequency-dependent selection . Hence , the restriction to VDiv and to Ncell is drastically reduced . The mechanism of the horizontal transfer is explained by bifurcation and frequency-dependent selection of the two deterministic subsystems . The advantage of horizontal transfer is thus demonstrated . The relevance of horizontal transfer to a rapid spread of beneficial molecules has been discussed [18] . Here , it is importance that the horizontal transfer leads to stabilization of the replication system by sustaining each fragment , and frequency-dependent selection of two types of the cells . Recent experimental studies have been challenged to use self-assembling fragmented ribozymes to synthesize each of the component fragments to achieve the RNA-catalyzed exponential amplification of the ribozyme itself [9] . The self-assembly of functional RNA polymerase ribozymes from short RNA oligomers has been demonstrated by driving entropically disfavored reactions under iterated freeze-thaw cycles [7] . Our theoretical results predict that these approaches for ( re- ) constructing RNA-based evolving systems have the serious issue: the replication of fragments is inevitably biased , so that it eventually fails to produce the copies of the ribozymes . Simultaneously , our study proposes a solution for this issue: the random exchange of fragments between loose compartments at intermediate frequencies . Recent experiments also suggest that the random exchange of contents between compartments is plausible . The freeze-thaw cycles , which enhance the assembly of fragments [7] , induce content exchange between giant unilamellar vesicles through diffusion [19] . Also , transient compartmentalization , which involves the occasional complete mixing of contents between compartments , is considered to be relevant to maintain functional replicators [20–24] . Taken together , it therefore seems natural to assume that compartmentalization is imperfect enough to allow the random exchange of fragments between compartments at the primitive stages of life . Instead of diffusion , the mechanism of fusion and fission of vesicles is also pointed out to exchange the contents [25] . Under an appropriate rate of fusion and fission , the direct exchange of contents may avoid the system to quickly enter the regime of a well-mixed state of the whole cell population . In this case , however , there would not be a clear distinction between X-dominant and Y-dominant cells as in the present study , because the contents of the two cells are mixed completely by each event of the fusion and fission . Then , it is not obvious if the frequency-dependent selection between X-dominant and Y-dominant cells can work . Detailed investigations are needed in the future to answer whether the stabilization by the fusion and fission mechanism is as robust as that of the present horizontal transfer by diffusion . The model of fragmented replicases investigated above can be conceptually compared to the hypercycle [26] , a model proposed to solve error catastrophes: Both models posit that multiple distinct sequences are integrated into an auto-catalytic system , which as a whole maintains a greater amount of information than possible by a single sequence . However , the two models sharply differ in dynamical aspects . In the fragmented replicases , the dynamics involves the positive feedback , which biases replication toward a subset of the fragments . In the hypercycle , the dynamics involves negative feedback , which balances the replication of distinct sequences on a long timescale , but also causes oscillatory instability on a short timescale . Given these comparisons , horizontal transfer as studied here will be also relevant to hypercycles . In addition , hypercycles entail evolutionary instability due to parasites [27] . It would be interesting to study the effect of parasites on the fragmented ribozymes in the future . While our study agrees with previous studies on the importance of compartmentalization for the maintenance of replicator diversity [14 , 16 , 28–31] , it demonstrates a novel possibility that horizontal transfer induces negative-frequency dependent selection among replicators , a mode of selection that is known to maintain diversity in various biological systems [32] . The mechanism by which negative-frequency dependent selection arises in our model requires three elements: horizontal transfer of replicators; selection for greater replicator diversity among compartments; and positive feedback ( i . e . , positive-frequency dependent selection ) within compartments . It remains to be investigated how generalizable the mechanism investigated here is for the maintenance of replicator diversity in prebiotic systems .
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How evolution got started is a crucial question in the origin of life . One possibility is that RNA molecules gained the ability to catalyze self-replication . Researchers recently proposed how this possibility might have been realized: a long RNA catalyst was divided into short replicable fragments , and these fragments self-assembled into the original long catalyst . Ingenious though it is , we expose a hidden flaw in this proposal . An auto-catalytic system based on fragmented catalysts involves positive feedback , which necessarily biases replication toward specific fragments and eventually halts the replication of the whole system . However , we also propose an effective remedy to this flaw: compartmentalization and content exchange among compartments generate negative feedback , which tightly coordinates the replication of different fragments .
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] |
2019
|
Horizontal transfer between loose compartments stabilizes replication of fragmented ribozymes
|
Transmission of Yersinia pestis by flea bite can occur by two mechanisms . After taking a blood meal from a bacteremic mammal , fleas have the potential to transmit the very next time they feed . This early-phase transmission resembles mechanical transmission in some respects , but the mechanism is unknown . Thereafter , transmission occurs after Yersinia pestis forms a biofilm in the proventricular valve in the flea foregut . The biofilm can impede and sometimes completely block the ingestion of blood , resulting in regurgitative transmission of bacteria into the bite site . In this study , we compared the relative efficiency of the two modes of transmission for Xenopsylla cheopis , a flea known to become completely blocked at a high rate , and Oropsylla montana , a flea that has been considered to rarely develop proventricular blockage . Fleas that took an infectious blood meal containing Y . pestis were maintained and monitored for four weeks for infection and proventricular blockage . The number of Y . pestis transmitted by groups of fleas by the two modes of transmission was also determined . O . montana readily developed complete proventricular blockage , and large numbers of Y . pestis were transmitted by that mechanism both by it and by X . cheopis , a flea known to block at a high rate . In contrast , few bacteria were transmitted in the early phase by either species . A model system incorporating standardized experimental conditions and viability controls was developed to more reliably compare the infection , proventricular blockage and transmission dynamics of different flea vectors , and was used to resolve a long-standing uncertainty concerning the vector competence of O . montana . Both X . cheopis and O . montana are fully capable of transmitting Y . pestis by the proventricular biofilm-dependent mechanism .
Once fleas had been implicated as important vectors of the plague bacillus , Yersinia pestis , attention quickly turned to the mechanism and dynamics of flea-borne transmission . The first experiments , conducted between 1904–1907 by members of the Indian Plague Research Commission and others , characterized what is now termed early-phase transmission [1–4] . When fleas that had just fed on a rodent with terminal plague bacteremia were collected and used to challenge naïve rodents , they remained infective for about a week , with the transmission rate peaking about three days after the infectious blood meal and then waning . Transmission very rarely occurred from challenge with a single flea , but challenges with 10 to 25 fleas resulted in a transmission rate of 20 to 70% . Because simultaneous challenge with multiple fleas was required , early-phase transmission has also been referred to as mass transmission . Although long assumed to be a form of mechanical transmission , recent studies suggest that it is not that simple [5]; however , the mechanism of this early-phase transmission has yet to be determined . In 1914 a second mode of transmission was discovered [6 , 7] . This later phase of transmissibility occurs after Y . pestis forms a dense biofilm in the proventriculus , a valve in the flea foregut [8] . As the biofilm grows and consolidates it interferes with the valvular function of the proventriculus . Because the infected valve is unable to close completely , blood flowing into the midgut can flow back out again , carrying bacteria along with it , and be regurgitated into the bite site . The proventriculus can eventually become completely blocked in some fleas , preventing any blood from reaching the midgut . Inflowing blood is stopped by the bacterial mass that fills the proventriculus , and transmission occurs when blood mixed with bacteria from the surface of the biofilm is refluxed back into the bite site . As they begin to starve , such completely blocked fleas make continuous , persistent attempts to feed , thereby increasing the probability of transmission . Each feeding attempt by a single blocked X . cheopis flea has a 25 to 50% transmission success rate [9–13] . The ecology of plague is complex , involving many different rodent-flea transmission cycles . The relative competence of several flea vectors has been examined to varying degrees ( reviewed in [5] ) . In general , these studies indicate that different flea species vary in their potential to become infected and subsequently transmit Y . pestis . The comparative early-phase transmission efficiency of different flea vector species has been systematically evaluated in several recent studies [14–17] . However , comparisons of the ability to transmit after the early phase are problematic because a variety of experimental methods have been used . This has led to variable and sometimes contradictory results . For example , some studies indicate that the North American ground squirrel flea Oropsylla montana transmits very poorly by the proventricular biofilm-dependent mechanism [9 , 10 , 18 , 19] , whereas other studies show it to transmit as efficiently as Xenopsylla cheopis , the flea often cited as the most efficient transmitter by that mechanism [12 , 20] . A second unresolved issue is the relative ecologic importance of the two transmission mechanisms . Transmission efficiency studies to date have concentrated on either the early-phase or the proventricular biofilm mechanism , making direct comparisons of their relative efficiency problematic , again due to the variety of experimental conditions used . To begin to address these shortcomings , we developed a standardized , carefully controlled experimental model system to infect cohorts of fleas and to characterize infection , proventricular blockage , and transmission dynamics during a four-week period following a single infectious blood meal . Comparative results for O . montana and X . cheopis establish that both develop proventricular blockage and transmit efficiently via the biofilm-dependent mechanism . For both species , transmission efficiency in the early phase was lower than at later times after infection .
Y . pestis KIM6+ was inoculated from frozen stocks maintained at the Rocky Mountain Laboratories ( RML ) into 5 ml of brain-heart infusion ( BHI ) broth supplemented with 10 μg/ml hemin and incubated at 28°C . After overnight incubation , 1 ml of this culture was used to inoculate 100 ml of BHI that was incubated at 37°C for 18 to 19 hours without aeration . The KIM6+ strain lacks the Yersinia virulence plasmid and is attenuated in mammalian virulence but infects fleas normally [21] . O . montana were from a laboratory colony originally established at the CDC , Fort Collins [22]and maintained at RML since 2011 . X . cheopis colonies were derived from fleas collected in Los Angeles , CA or Baltimore , MD and have been maintained at RML for ~ 10 to 25 years , respectively . Fleas were pulled randomly from colonies and starved for four days . Groups of about 300 fleas were allowed to feed through a mouse skin affixed to an artificial feeding device [21] containing 5 ml of heparinized mouse blood ( collected from RML Swiss Webster mice on site ) containing ~1 x 109/ml Y . pestis KIM6+ , or with KIM6+ that had been transformed with pAcGFP1 ( Clontech; Mountain View , CA ) , a plasmid containing a constitutively expressed green fluorescent protein ( GFP ) gene . After the 1-hour feeding period , fleas were collected and immobilized by placing the tube containing them on ice . Fleas were then arrayed on a chill table ( BioQuip; Rancho Dominguez , CA ) under a dissecting microscope , and only those that had taken an infectious blood meal , evidenced by the presence of fresh red blood in the midgut , were kept and used for experiments . A sample of 20 female fleas was placed at -80°C for later determination of the initial infectious dose . In conjunction with experiments to assess proventricular blockage , a second group of fleas from the same cohort used for infection was allowed to feed the same day on sterile mouse blood to serve as uninfected controls . After feeding , ~100 infected and control fleas ( equal numbers of males and females ) were put into separate flea capsules [23] that were kept at 21°C and 75% relative humidity . These fleas were allowed to feed on neonatal mice for 1 hour on days 2 , 6 , 9 , 13 , 16 , 20 , 23 , and 27 after infection . An additional sample of 20 infected female fleas was maintained identically but collected and placed at -80°C on day 7 after infection . A shallow layer of sterile sawdust was added to the bottom of capsules containing O . montana . Following each maintenance feed , infected fleas were collected and examined microscopically for evidence of proventricular blockage , diagnosed by the presence of fresh red blood in the esophagus only , with none in the midgut . Blocked fleas were segregated into a separate capsule after diagnosis for separate maintenance . Mortality was also recorded on each maintenance feed day . A sample of 20 surviving female fleas was placed at -80°C when the experiment was terminated on day 28 . Three independent blockage experiments were conducted for both O . montana and X . cheopis . After being diagnosed as blocked , O . montana fleas infected with the GFP+ strain were dissected and examined by fluorescence microscopy to verify proventricular blockage . Dilutions of the infectious blood meal were plated on blood agar to determine the Y . pestis CFU/ml . Samples of fleas that had been collected at different times after infection and stored at -80°C were thawed , surface sterilized , individually triturated , and then dilutions were plated in BHI soft agar overlays [24] . All plates were incubated at 28° C for 48 hours prior to colony counts . For transmission experiments , fleas that took an infectious blood meal were housed as described above . On days 3 , 10 , 17 , 24 , and 31 , ~200 fleas ( approximately equal numbers of males and females ) were allowed to feed on sterile defibrinated rat blood ( BioreclamationIVT , New York ) in the artificial feeding system . After 90 min , fleas were collected and examined microscopically as described above to determine how many had fed , and of those , how many were completely or partially blocked . A sample of 20 unblocked female fleas that had fed were placed at -80°C . Day 3 was the first feeding opportunity after the infectious blood meal and represents early-phase transmission . In addition to the transmission test artificial feedings , fleas were also fed on neonatal mice on days 6 , 13 , 20 , and 27 . Immediately after each transmission test feeding , blood was removed and the interior of the feeder was washed ten times with 3 ml PBS . The entire volume of blood was distributively plated on blood agar plates . Pooled washes were centrifuged 9 , 800 ×g for 30 min , most of the supernatant removed and the remainder was mixed thoroughly to resuspend any bacteria and spread onto blood agar plates . During the period of peak transmission by O . montana ( 10 to 24 days after infection ) dilutions of the blood and wash samples , instead of the entire sample , were plated . The external ( haired ) surface of the mouse skin membrane was disinfected with ethanol , cut into small pieces that were added to 1 ml PBS in a lysing matrix H tube and subjected to 2 min treatment in a FastPrep homogenizer ( MP Biomedicals , Santa Ana , CA ) to dislodge any transmitted bacteria associated with the interior surface of the mouse skin . Skin sample supernatants were pooled and centrifuged at 20 , 000 ×g for 15 min . Most of the supernatant was discarded and the remainder was vigorously mixed to resuspend any bacteria and then plated on blood agar . Three independent transmission experiments were conducted with both O . montana and X . cheopis . Fleas were placed in PBS on a glass microscope slide and dissected with a set of fine forceps . The flea exoskeleton was removed and an 18 x 18 mm glass cover slip was gently placed over the top of the digestive tract . Images of flea digestive tracts were obtained using a Nikon Eclipse E800 microscope with an Olympus DP72 camera and cellSens imaging software . To calculate an esophagus:proventriculus ( E/PV ) width ratio , the width of the base of the esophagus was measured above the proventricular spines , where the musculature ends , and the proventriculus was measured at its widest position , near the midpoint of the valve . All analyses were performed using GraphPad Prism 6 ( GraphPad Software Inc . , La Jolla , Ca . ) . Statistical tests used and relevant P values are indicated in the figure legends . All experiments involving animals were approved by the Rocky Mountain Laboratories , National Institute of Allergy and Infectious Diseases , National Institutes of Health Animal Care and Use Committee ( protocols 13–036 and 14–006 ) and were conducted in accordance with all National Institutes of Health guidelines .
Groups of fleas were infected by allowing them to feed on highly bacteremic blood in an artificial feeding device , and thereafter provided twice-weekly maintenance feeds on uninfected mice for four weeks . Immediately after each maintenance feed , fleas were examined individually for the development of proventricular blockage , indicated by the presence of fresh red blood in the esophagus but not in the midgut . Contrary to some previous reports , we found that O . montana readily became blocked , but at a lower incidence than X . cheopis ( mean blockage rates 22% and 36% , respectively; Fig 1A and Fig 2 ) . However , O . montana tended to become blocked sooner ( mean = 9 . 6 days after infection ) than X . cheopis ( mean = 14 . 7 days ) and to survive longer after becoming blocked ( Fig 3 ) . Blocked O . montana fleas survived up to 2 weeks after being diagnosed ( range = 1 to 16 days; mean = 7 days; median = 4 days ) , whereas blocked X . cheopis survived a maximum of 4 days ( range = 1 to 4 days; mean = 2 days; median = 1 day ) . The mortality rate of uninfected control fleas at 28 days was 6 to 7% for both species ( Fig 1B ) . Since blocked fleas die from starvation , the excess mortality of infected fleas is a surrogate indicator of blockage . The majority of the mortality of infected X . cheopis ( 60% ) and O . montana ( 28% ) was in fact due to the death of blocked fleas , and reflects the difference in blockage rate between the two species . The average number of Y . pestis that the fleas acquired in the infectious blood meal was 1 . 3 to 6 . 4 x 105 for all experiments ( Fig 1D ) . By 7 days after infection , a significantly greater percentage of O . montana than X . cheopis had eliminated the infection ( Fig 1C ) . However , the average bacterial load in the infected fleas was equivalent for both species at all time points ( Fig 1D ) . In other experiments , groups of infected fleas were allowed to feed from a sterile blood reservoir at different times after infection . After feeding , fleas were collected and examined for evidence of feeding and proventricular blockage , and the number of Y . pestis that the fleas had transmitted was determined . The transmission test at 3 days after infection was the first feeding opportunity after the infectious blood meal , when transmission by the early-phase mechanism is maximum [1 , 2 , 5] . Between each weekly transmission test thereafter , fleas were provided a separate maintenance feed on an uninfected mouse . By the second transmission test on day 10 the fleas had had two uninfected blood meals since infection , and were beyond the early-phase transmission period . Thus , transmission on days 10 to 31 after infection was via the proventricular biofilm-dependent mechanism . Few Y . pestis were transmitted by the early-phase mechanism , and no transmission was detected in some experiments , even though over 100 fleas fed ( Fig 4 , Tables 1 and 2 ) . Additional experiments were done to further evaluate early-phase transmission only ( 3 days after an infectious blood meal ) . The number of Y . pestis transmitted was below the detection level in 7 of 16 trials and ranged from 1 to 164 CFU in the nine others ( Fig 4C ) . Fleas of both species transmitted many more bacteria after the early phase , peaking 10 to 24 days after infection at ~104 CFU for X . cheopis and >105 CFU for O . montana . Maxima of Y . pestis CFU transmitted ( 10 to 17 days after infection for O . montana and 17 to 24 days for X . cheopis ) correlated with the mean time for proventricular blockage to develop ( Fig 3A and 3B ) . At most time points after infection , the cumulative number of Y . pestis CFU transmitted by O . montana cohorts was 10-fold or more higher than the number transmitted by X . cheopis ( Fig 4 , Tables 1 and 2 ) . While examining the digestive tracts of dissected fleas , we noticed that the base of the esophagus , where it joins the proventriculus , appeared to be wider in O . montana than in X . cheopis . This was especially evident in blocked O . montana , in which the base of the esophagus was often grossly distended ( Figs 2 and 5 ) . To evaluate this quantitatively , we measured the esophageal and proventricular widths in uninfected and blocked specimens of the two species and calculated a ratio . The esophagus:proventriculus ( E/PV ) width ratio was significantly greater for O . montana than X . cheopis ( Fig 5E ) . In blocked fleas of both species , the Y . pestis proventricular biofilm extended into the esophagus . This expanded the relative width of the esophagus in blocked vs . unblocked O . montana to a greater extent than in blocked vs . unblocked X . cheopis . In some blocked O . montana , the esophagus was nearly as wide as the proventriculus ( mean E/PV ratio = 0 . 71; range = 0 . 60 to 0 . 84 ) .
In 1911 , McCoy demonstrated that O . montana was able to transmit Y . pestis during the early phase , and more recent work has indicated that O . montana and X . cheopis are comparable in early-phase transmission efficiency [16 , 25–30] . X . cheopis has also consistently been shown to become blocked and transmit after the early phase . However , conflicting results have been reported for the rate at which O . montana becomes blocked after infection and its corresponding ability to transmit by the proventricular biofilm-dependent mechanism . Wheeler and Douglas found that O . montana was an even better vector than X . cheopis during the four-week period after the early phase [12 , 20] , but other studies of O . montana reported little or no blockage and transmission during this time frame [9 , 10 , 18 , 19] . Based on the negative data , the recent literature now states that O . montana rarely becomes blocked [26 , 31] . A first objective of this study was to resolve the contradictory conclusions of the earlier studies by using standardized methods to directly compare the post-infection blockage rates of O . montana and X . cheopis . We found that obvious , complete blockage of O . montana is not a rare but a regular occurrence ( Fig 2 ) . Differences among O . montana strains used for infection experiments has previously been suggested as a potential cause for conflicting results [10] . However , our O . montana colony originated from one used for a study that reported a 0% blockage rate [19] . A more likely reason for discordant results stems from the very high mortality rate associated with the studies that report negative results for O . montana blockage . Two studies that reported a 0 to 3% blockage rate also recorded a 23 to 59% die-off during the first week after the infectious blood meal , 58 to 79% at two weeks , and few fleas surviving after four weeks [10 , 19] . In initial experiments , we also noted high mortality , even of uninfected control O . montana , and low and inconsistent blockage rates . Adult fleas maintained in proper conditions should live for a period of months , and we had previously observed that results with unhealthy X . cheopis cohorts ( indicated by a 4-week mortality rate of >25% for uninfected control fleas ) were unreliable and grossly underestimated the normal blockage rate observed for healthy X . cheopis ( mortality rate of uninfected controls <10% ) . We found that unless O . montana were kept on a layer of sawdust , they became entangled via the long , curved pretarsal claws at the end of their legs , which led to stress-related mortality . The addition of a sawdust substrate to capsules containing O . montana ( which is not necessary for X . cheopis ) eliminated this problem and reduced uninfected control flea mortality at four weeks from 60 to 75% to <10% . Infected O . montana mortality was 20% at two weeks and 28% at four weeks after infection , correlating with the timing and incidence of blockage-induced mortality ( Fig 1 ) . A high mortality rate during the first week after infection indicates that the fleas were unhealthy or stressed , complicating the interpretation of the results . For this reason , experiments designed to compare long-term infection and transmission dynamics should include uninfected control fleas to verify normal viability . Different flea species may require different maintenance conditions to maintain health . The consistent high blockage and transmission rates reported for X . cheopis compared to other species may in part be due to the fact that X . cheopis is more easily adaptable to the laboratory . A second objective was to compare the vector efficiency of O . montana and X . cheopis by the proventricular biofilm-dependent mechanism . The vector efficiency of a given flea species is a product of several factors , including infection potential ( the percentage of individuals that become infected after feeding on blood containing Y . pestis ) , vector potential ( the % of infected fleas that develop a transmissible infection; i . e . , the % of infective fleas ) , and the transmission potential ( the average number of transmissions effected by a group of infective fleas ) [12 , 32] . We measured the infection potential directly and found that it was lower for O . montana ( 45 to 75% ) than for X . cheopis ( 87 to 100%; Fig 1C ) . This is consistent with previous reports that O . montana clears itself of infection at a higher rate than X . cheopis [10 , 19 , 20] . Although complete blockage is not required for transmissibility , the blockage rate is often used as a surrogate marker for vector potential by the proventricular biofilm-dependent mechanism . Like the infection rate , the blockage rate was also lower for O . montana ( 17 to 25% ) than for X . cheopis ( 30 to 40% ) ; however , blockage rates of stably infected fleas ( rather than the total that took an infectious blood meal ) were roughly equivalent for the two species . In this study , we monitored transmission by a population of infected fleas , which allowed an overall comparison of transmission dynamics and kinetics . To directly calculate the vector efficiency , it will also be necessary to monitor the vector potential and transmission potential of individual fleas after infection . We are currently adapting our model system for this purpose . Despite its lower infection and blockage rates , O . montana transmitted greater numbers of Y . pestis than did X . cheopis in mass transmission experiments , and transmission peaked earlier . Several factors could account for these results . O . montana developed proventricular blockage sooner than X . cheopis , and blocked O . montana survived significantly longer than blocked X . cheopis . The blocking-surviving potential , defined as the mean day of death after becoming blocked divided by the mean day of becoming blocked after an infectious blood meal , has been described as an important component of flea vector efficiency [11 , 18] . Based on the results shown in Fig 3 , the calculated blocking-survival potential of O . montana ( 0 . 73 ) is 5-fold higher than that of X . cheopis ( 0 . 14 ) . It is also important to note that , as mentioned previously , complete blockage is not required for transmission—partially blocked fleas can also transmit efficiently [7] . Partially blocked fleas , with fresh blood in the esophagus but some also in the midgut , were observed frequently during transmission trials ( Tables 1 and 2 ) . Complete blockage appeared to develop more gradually in O . montana or was more ephemeral , as sometimes a small amount of blood appeared to seep through into the midgut when a flea previously diagnosed as completely blocked fed again . Differences in foregut anatomy may also enhance transmission efficiency of O . montana . The esophageal-proventricular junction is much broader in blocked O . montana than in blocked X . cheopis ( Fig 5 ) . The increased esophageal distension observed in fully blocked O . montana would be expected to expose a greater surface area of the infectious biofilm to contact with incoming blood during a feeding attempt , potentially enhancing regurgitative transmission . This may in part account for the greater number of CFUs recovered from O . montana mass transmission experiments . We have hypothesized that foregut anatomical differences may also account for the larger numbers of Y . pestis transmitted by X . cheopis than by the cat flea Ctenocephalides felis [24] . A third objective of this study was to evaluate , in the same cohorts of infected fleas , the relative importance of the two transmission mechanisms . The transmission efficiency of both O . montana and X . cheopis , defined here as the number of Y . pestis transmitted per infective flea , was very low by the early-phase mechanism compared to later proventricular biofilm-dependent transmission . In half of the day-3 early-phase transmission trials , no Y . pestis CFUs were recovered from the blood reservoir fed upon by >100 fleas . Based on trials in which we added known numbers of Y . pestis to the blood in the feeding system , the expected recovery rate is ~95% . Thus , we estimate that 0 to 5 CFU were transmitted in the negative early-phase transmission experiments . Our results are in line with previous work indicating that early-phase transmission is inefficient . In 1907 the Indian Plague Commission reported that only 1 of 67 fleas that had fed on a septicemic plague rat and then individually placed on naïve rats transmitted plague in the early phase [1] . Recent studies estimate a ~0 to 10% probability of a single O . montana or X . cheopis flea transmitting by the early-phase mechanism [16 , 26–30] . These estimates were based on both disease incidence and seroconversion following challenge by groups of ten fleas , indicating that the number of Y . pestis transmitted was sometimes at or below the LD50 , estimated at 1 to 10 CFU for the highly susceptible laboratory mice used . The bite of a single blocked X . cheopis results in transmission 25 to 50% of the time [9–12] . The number of CFU transmitted by the bite of a blocked X . cheopis is highly variable , ranging from <10 to several thousand [10 , 13] . No data are available on the transmission efficiency of partially blocked fleas or the number of CFUs they transmit . Our system allowed us to monitor transmission by cohorts of infected fleas ( “mass” transmission ) . This allowed an overall comparison of transmission dynamics and kinetics and vector potential at the population level , but not the transmission rate ( the percentage of fleas that transmitted ) or transmission potential ( defined above ) . Consistent with our results , however , a recent study estimated that the percent transmission efficiency of an individual O . montana flea is lower in the early phase compared to later time points after infection [28] . Early-phase transmission has been proposed to largely account for epizootic spread by flea vectors that purportedly do not readily become blocked [33 , 34] . However , it is recognized that comparative data regarding transmission by the proventricular biofilm ( “blockage” ) mechanism are limited and problematic , and warrant reexamination [5 , 20 , 35] . A variety of experimental conditions and designs have been used with respect to infectious blood meal source , infectious dose , and flea maintenance conditions , all of which are known to influence infection and transmission dynamics . In some cases , small numbers of fleas were used that were likely poorly adapted to laboratory conditions . As in the case of O . montana , results have sometimes been inconsistent . Furthermore , early-phase transmission has only been demonstrated from fleas that fed on blood with a very high bacteremia to highly susceptible laboratory rodents ( ID50 <10 CFU ) [36] . The California ground squirrel , the major host of O . montana , reportedly has an ID50 of >250 CFU [37] . Based on the previous reports that O . montana rarely blocks or transmits beyond the early phase , a recent study that modeled O . montana-ground squirrel plague made the assumption that only early-phase transmission was important , and that transmission beyond that was negligible [38] . Our results indicating that very few bacteria are transmitted early ( less than the reported ID50 of ground squirrels ) , but that subsequent transmission is robust , suggest that the converse assumption is probably more realistic . However , factors specific to different ecological settings and host and vector populations may also affect transmission dynamics . In this study , we present a standardized , stringently controlled model system to more reliably compare vector efficiency and to monitor transmission dynamics of a population of infected fleas . Key elements of this experimental system and their rationale include: 1 ) Fleas are infected on a specific blood source containing comparable concentrations of Y . pestis . Both the type of blood used and the bacteremia level significantly affect the infection potential and incidence of blockage [13 , 19 , 39] . Use of an artificial feeding device allows the infectious blood meal to be matched in different experiments . 2 ) A subset of the same flea cohort used for infection is fed on sterile blood of the same type for use as uninfected controls . Infected and uninfected fleas are kept in the same environment and provided the same type and frequency of maintenance feeds . The mortality of uninfected control fleas after 4 weeks should be low . If not , the fleas were physiologically stressed and results based on them are unreliable . Mortality of infected fleas should also be recorded , as it is a surrogate indicator of blockage-induced starvation . 3 ) Fleas are individually examined immediately after the infectious blood meal and only those that took a full blood meal are included in the study . The mean initial infectious dose acquired by the fleas is determined from a sample of these fleas , collected immediately after the infectious blood meal . Some flea species may be reluctant to feed on other than their natural blood source , resulting in a lower infectious dose and subsequent lower infectivity rate . 4 ) Fleas are examined microscopically immediately after each maintenance feed for evidence of partial or complete proventricular blockage using good quality optics and light source . Fluorescence microscopy of the digestive tract dissected from fleas infected with GFP-expressing Y . pestis is very useful to determine proventricular blockage status . This is particularly helpful for fleas that have a darkly pigmented exoskeleton that is less transparent to direct microscopic visualization of the esophagus , proventriculus , and midgut [24] . 5 ) Infection rate and bacterial load are monitored by plate counts of flea samples collected at different times after the infectious blood meal . 6 ) Transmission dynamics are monitored for a population that received the same infectious blood meal during a timeframe that encompasses both modes of transmission . Our results resolve a long-standing controversy about the susceptibility of O . montana to become blocked and to transmit Y . pestis by the proventricular biofilm-dependent mechanism . We previously used this experimental system to show that C . felis , normally a poor vector by either mechanism , readily becomes blocked and transmits if its usual daily feeding behavior is altered [24] . The transmission dynamics of other flea vector species can be systematically reevaluated by using this system , with the important prerequisite that appropriate laboratory maintenance conditions can be established for them .
|
The ecology of plague is complex and its epidemiology is enigmatic . Many different flea species are able to transmit Yersinia pestis , the plague bacillus , and they can transmit in two different ways . Early-phase transmission can occur during the first week after a flea has fed on a diseased animal . Thereafter , transmission occurs only as bacterial growth in the flea foregut interferes with and eventually blocks blood feeding . Comparisons of the relative ability of different flea vectors to transmit have been problematic , and contradictory results have been reported for the ability of the ground squirrel flea Oropsylla montana to transmit beyond the early phase . Our results show that O . montana readily develops foregut blockage , and transmission by that mechanism was as good as or better than observed for Xenopsylla cheopis , a flea known to block at a high rate . In contrast , very few bacteria were transmitted in the early phase by either of these fleas compared to later times after infection , suggesting that early-phase transmission is pertinent only to highly susceptible animals . Improved characterization of the transmission patterns of different flea vectors will aid in modeling plague incidence in its various natural settings .
|
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2017
|
Comparative Ability of Oropsylla montana and Xenopsylla cheopis Fleas to Transmit Yersinia pestis by Two Different Mechanisms
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Obstacles to bacterial survival and replication in the cytosol of host cells , and the mechanisms used by bacterial pathogens to adapt to this niche are not well understood . Listeria monocytogenes is a well-studied Gram-positive foodborne pathogen that has evolved to invade and replicate within the host cell cytosol; yet the mechanisms by which it senses and responds to stress to survive in the cytosol are largely unknown . To assess the role of the L . monocytogenes penicillin-binding-protein and serine/threonine associated ( PASTA ) kinase PrkA in stress responses , cytosolic survival and virulence , we constructed a ΔprkA deletion mutant . PrkA was required for resistance to cell wall stress , growth on cytosolic carbon sources , intracellular replication , cytosolic survival , inflammasome avoidance and ultimately virulence in a murine model of Listeriosis . In Bacillus subtilis and Mycobacterium tuberculosis , homologues of PrkA phosphorylate a highly conserved protein of unknown function , YvcK . We found that , similar to PrkA , YvcK is also required for cell wall stress responses , metabolism of glycerol , cytosolic survival , inflammasome avoidance and virulence . We further demonstrate that similar to other organisms , YvcK is directly phosphorylated by PrkA , although the specific site ( s ) of phosphorylation are not highly conserved . Finally , analysis of phosphoablative and phosphomimetic mutants of YvcK in vitro and in vivo demonstrate that while phosphorylation of YvcK is irrelevant to metabolism and cell wall stress responses , surprisingly , a phosphomimetic , nonreversible negative charge of YvcK is detrimental to cytosolic survival and virulence in vivo . Taken together our data identify two novel virulence factors essential for cytosolic survival and virulence of L . monocytogenes . Furthermore , our data demonstrate that regulation of YvcK phosphorylation is tightly controlled and is critical for virulence . Finally , our data suggest that yet to be identified substrates of PrkA are essential for cytosolic survival and virulence of L . monocytogenes and illustrate the importance of studying protein phosphorylation in the context of infection .
Intracellular pathogens are responsible for a majority of the world’s most devastating infectious disease . A subset of intracellular pathogens , known as cytosolic pathogens , survive and thrive in the host cell cytoplasm . Recently , it has become clear that some traditionally extracellular or vacuolar pathogens such as Staphylococcus aureus [1] and Mycobacterium tuberculosis [2] , respectively , spend at least part of their life in the cytosol . For canonical cytosolic pathogens like Listeria monocytogenes and Francisella tularensis , mutants that either cannot access the cytosol [3 , 4] or fail to survive [5 , 6] and replicate [7 , 8] within the cytosol are avirulent . Importantly , the cytosol is restrictive to bacterial replication , as both non-pathogenic bacteria [9] , and even some pathogens that normally localize to other cellular compartments [10] , are unable to replicate when localized to the cytosol , suggesting that specific adaptions are required for cytosolic survival . While the stresses and their cognate bacterial responses present in the phagosome have been extensively studied , obstacles to bacterial survival and replication in the cytosol are relatively unexplored . Potential barriers to survival and replication in the cytosol include cell wall stresses [5] , metabolic restriction [11] and direct antimicrobial effectors such as ubiquidicin [12] and Guanylate Binding Proteins ( GBPs ) [13 , 14] . The mechanisms cytosolic pathogens use to sense and respond to these obstacles in order to survive in this restrictive niche are also unknown . In addition to avoiding direct antimicrobial activities of the host cell cytosol , evading detection by the innate immune system is another major challenge for cytosolic pathogens . The last decade has seen an explosion of knowledge about how host cells detect invading bacteria and viruses in the cytosol , and in some cases , pathogen strategies for avoiding detection [15 , 16] . Specifically , bacteriolysis in the cytosol triggers both cGAS/STING-mediated type I interferon responses and AIM2-dependent inflammasome activation [17 , 18] . In the case of AIM2 , DNA released during bacterolysis directly binds to AIM2 , leading to oligimerization with ASC and Caspase 1 to form a functional inflammasome complex [19] . Activation of the inflammasome leads to a robust inflammatory response and the loss of the intracellular replication niche through a lytic , inflammatory cell death called pyroptosis [20–22] . As such , avoiding bacteriolysis is crucial for evasion of the host immune response and the ultimate success of cytosolic pathogens [22] . Listeria monocytogenes is a gram-positive , facultative intracellular pathogen that lives as a saprophyte in the soil and is most commonly contracted through contaminated food products leading to systemic Listeriosis [23] . Listeriosis is a significant contributor to total fatalities caused by food borne illness in the United States and elsewhere , with a fatality rate of up to 30% even with antibiotic treatment [24] . L . monocytogenes is also an ideal model to study cytosolic pathogens and host responses as it is genetically tractable , has a well-defined intracellular lifecycle and has robust ex vivo and in vivo models to study pathogenesis [25 , 26] . After ingestion , L . monocytogenes is either phagocytosed by professional phagocytes or induces entry into epithelial cells by receptor mediated endocytosis [27 , 28] . To escape the vacuole the bacteria secrete the pore forming toxin Listeriolysin O ( LLO ) encoded by the gene hly [3] . Accessing the cytosol is crucial for L . monocytogenes to cause disease as mutants that lack hly are avirulent [3 , 29] . Once inside the cytoplasm the bacteria replicate to high numbers and hijack host actin to propel themselves into neighboring cells [30] . Upon spread to neighboring cells , L . monocytogenes again expresses hly and a pair of phospholipases to escape the secondary vacuole and continue its infectious lifecycle [31 , 32] . While the mechanisms used by L . monocytogenes to access the cytosol and spread from cell-to-cell are well defined , the mechanisms by which L . monocytogenes senses and adapts to the cytosolic environment are not well understood . One way bacteria respond to their environment is through sensor kinases that reversibly phosphorylate effector proteins . Classically , bacterial signal transduction through phosphorylation has been thought to be primarily mediated through two-component systems[33] . However , it has recently become clear that in many Gram-positive bacteria the eukaryotic-like kinases such as the Penicillin-Binding Protein And Serine/Threonine Kinase-Associated Protein ( PASTA ) kinases phosphorylate proteins to regulate diverse cellular processes ranging from cell wall synthesis [34 , 35] and cell division [36] , to central metabolism [37] , biofilm formation [38] , and virulence [39] . PASTA kinases are composed of extracellular penicillin-binding-protein domains which , upon binding of peptidoglycan fragments , facilitate dimerization of the intracellular kinase domains , autophosphorylation and ultimately phosphorylation of downstream effector proteins [40 , 41] . PknB , the PASTA kinase in M . tuberculosis is essential and has been investigated as a potential drug target [42–44] , while the PASTA kinases in Staphylococcus epidermidis and Streptococcus pyogenes have been shown to regulate biofilm formation , production of virulence factors and cell wall homeostasis [38 , 45] . We previously demonstrated that the L . monocytogenes PASTA kinase , PrkA , is required for β-lactam resistance , however , its role in infection and virulence is unknown [46] . A variety of PASTA kinase substrates have been defined in different organisms . One conserved substrate in M . tuberculosis and B . subtilis is YvcK ( also known as CuvA in M . tuberculosis ) , a highly conserved protein of unknown function [36 , 47] . In M . tuberculosis and B . subtilis YvcK is singly phosphorylated on a threonine residue near the C-terminus , but the site of phosphorylation is not precisely conserved [36 , 47] . Although the biochemical function of YvcK is unknown , like the PASTA kinases themselves , YvcK is important for both carbon metabolism and cell wall homeostasis [48–50] . In B . subtilis and M . tuberculosis YvcK is required for growth on gluconeogenic substrates , pentose phosphate pathway/citric acid cycle intermediates and cholesterol , respectively [48 , 50] . Additionally , both B . subtilis and M . tuberculosis ΔyvcK mutants display morphology defects under gluconeogenic growth conditions [48 , 50] . Although M . tuberculosis CuvA localizes to the division septum and the poles , whereas B . subtilis YvcK localizes in a helical pattern , in both organisms CuvA/YvcK is required for Pbp1 localization under gluconeogenic growth conditions [49 , 50] . Finally , in B . subtilis , YvcK mediated localization of Pbp1 was dependent on phosphorylation by the PASTA kinase , [47] . L . monocytogenes YvcK is required for cytosolic survival and evasion of the AIM2 inflammasome and ΔyvcK mutants are hypersusceptible to lysozyme in vitro [17 , 51] . The interaction of L . monocytogenes YvcK with PrkA , as well as its role in cell wall homeostasis , carbon metabolism or virulence are unknown [17] . In this study , we characterize the function of PrkA in cell wall homeostasis , metabolism , cellular infection and ultimately virulence . Similar to what was previously observed in M . tuberculosis and B . subtilis , PrkA is important for dealing with both metabolic and cell wall stress in L . monocytogenes . Furthermore , PrkA is required for intracellular replication , cytosolic survival , evasion of the AIM2 inflammasome and ultimately virulence in murine models of listeriosis . In addition , we find that the conserved PASTA kinase substrate YvcK is a PrkA substrate in L . monocytogenes , but that the sites of phosphorylation are significantly different from those previously described in other organisms . Furthermore , similar to ΔprkA mutants , ΔyvcK mutants are also sensitive to metabolic and cell wall stress and are required for cytosolic survival and ultimately virulence in vivo . Surprisingly however , despite the fact that both PrkA and its substrate YvcK are required for cytosolic survival and virulence , phosphomimetic negative charges at the phosphorylation sites on YvcK inhibit its functions during infection . These data suggest that spatial and temporal regulation of YvcK phosphorylation is critical , and further suggest that alternative PrkA substrates are required for cytosolic survival and ultimately virulence . Given the highly conserved nature of both PrkA and YvcK in a variety of high impact pathogens , as well as their essential role in virulence in both M . tuberculosis and L . monocytogenes , compounds that interfere with this signaling axis may represent a promising new approach to antibiotic development .
We had previously demonstrated , similar to what has been observed for PASTA kinase mutants in other organisms , that the L . monocytogenes PASTA kinase PrkA was required for resistance to β-lactam antibiotics , but not vancomycin [46] , suggesting that PrkA regulates specific steps in cell wall synthesis and/or remodeling . To further characterize the function of PrkA in cell wall homeostasis , we determined the MIC of wild-type and ΔprkA mutant L . monocytogenes to a variety of additional cell wall inhibitors ( Table 1 ) . Similar to what we had observed with β-lactams , we found that the ΔprkA mutant is highly susceptible to tunicamycin ( ~100-fold increased sensitivity ) , an inhibitor that prevents the attachment of peptidoglycan and wall teichoic acid precursors to their lipid carrier . Complementation of the ΔprkA mutant restores wild type levels of tunicamycin resistance ( S1A Fig ) . Additionally the ΔprkA mutant was slightly more susceptible to Bacitracin and lysozyme , inhibitors that target undecaprenal recycling and the β- ( 1–4 ) -glycosidic bonds between N-acetylmuramic acid and N-acetylglucosamine in peptidoglycan , respectively . Increased sensitivity to the human antimicrobial peptide LL-37 suggests a role for PrkA in regulating teichoic acid biosynthesis/modification [52] . These data suggest that PrkA has a role in regulating multiple steps in cell wall synthesis and maintenance . In many organisms , PASTA kinases phosphorylate central metabolic enzymes . Glycerol and phosphorylated glucose are the two primary carbon sources used by L . monocytogenes during intracellular growth [53 , 54] . Therefore , to assess potential metabolic deficiencies in ΔprkA mutants that could be relevant to virulence , we assayed growth of wild type and ΔprkA mutants in minimal media with either glucose-6-phosphate or glycerol as the primary carbon sources . As L . monocytogenes only expresses the glucose-6-phosphate transporter ( hpt ) when the master virulence regulator PrfA is active we used strains with constitutively active PrfA ( prfA* ) to assess growth on glucose-6-phosphate [11] . There was no detectable difference in growth between wild type and ΔprkA mutants in rich media ( Fig 1A ) , or in Improved Minimal Media ( IMM ) with glucose-6-phosphate ( Fig 1B ) . Importantly , despite increased sensitivity to cell wall acting antibiotics , there were no defects in bacterial morphology ( Fig 1C , S1 Fig ) or cell wall thickness ( 24 . 2 ± 1 . 43nm for wild-type vs 24 . 9 ±2 . 47 nm for ΔprkA ) in rich media or minimal media with glucose-6-phosphate . Conversely , ΔprkA mutants were essentially unable to replicate in minimal media with glycerol as the sole carbon source with a doubling time of 16 . 3 hours compared to 4 . 5 hours for wild-type ( Fig 1B ) . Complementation of prkA expression in trans restored growth in minimal media with glycerol ( S2B Fig ) . Consistent with a pleiotropic role in both carbon metabolism and cell wall homeostasis , ΔprkA mutants displayed morphological defects in minimal media with glycerol ( Fig 1C , S2 Fig ) . Taken together , these data suggest that , similar to its role in other organisms , the PASTA kinase PrkA is required for cell wall homeostasis and central metabolism in L . monocytogenes . Defects in both cell wall homeostasis and growth on cytosolic carbon sources suggested that PrkA may be important for intracellular replication and cytosolic survival . To test this hypothesis , we infected bone marrow derived macrophages with wild type L . monocytogenes , and ΔprkA mutants and quantified intracellular growth . Wild type L . monocytogenes thrived in the macrophage cytosol while the ΔprkA mutant not only failed to grow , but by 5 hours post infection began to be killed , displaying a 90% loss in viability between 5 and 8 hours post infection ( Fig 2A ) . Complementation of prkA expression in trans restored growth in bone-marrow-derived macrophages ( BMDMs ) ( S1C Fig ) . The loss of viability from 5–8 hours post infection suggested that ΔprkA mutants were being killed in the host cell cytoplasm . To test this hypothesis , we assayed cytosolic bacterial lysis using a luciferase-based bacteriolysis reporter system as previously described [17] . Escape from the phagosome is required for luciferase production as Δhly mutants induced no detectable luciferase production whereas the control strain holin-lysin engineered to lyse upon entry in to the host cytosol lyses ~100-fold more frequently than wild type L . monocytogenes , respectively ( Fig 2B ) [17] . Despite significantly decreased bacterial loads , ΔprkA mutants lyse ~7-fold more frequently that wild type bacteria in the host cytosol ( Fig 2B ) consistent with lysis levels of L . monocytogenes mutants previously described to lyse in the macrophage cytosol [17] . Bacteriolysis of ΔprkA mutants in the host cell cytosol suggests that PrkA may be required for avoiding detection by the AIM2 inflammasome and subsequent pyroptotic host cell death [17] . To test this hypothesis , we quantified cell death of macrophages following infection with wild type L . monocytogenes and ΔprkA mutants . As previously described , wild type bacteria induced very limited host cell death while bacteria that lyse in the cytoplasm induced high levels of host cell death ( Fig 2C ) . Infection with ΔprkA mutants resulted in significantly increased host cell death; albeit not to the levels induced by the holin-lysin strain ( Fig 2C ) . As expected , complementation of prkA expression reduced host cell death to wild-type levels ( S1D Fig ) . Recent reports suggest that host guanylate-binding proteins ( GBPs ) play a critical role in cytosolic bacteriolysis of Francisella tularensis subsp . novicida [13] . To determine if GBPs were required for L . monocytogenes , and more specifically ΔprkA mutant cytosolic bacteriolysis , we infected GbpChr3 KO macrophages and assayed for host cell death . GBP deficiency did not result in decreases in host cell death following infection with any strains of L . monocyctogenes . Instead , we observed small , but statistically significant increases in host cell death following infection with strains that activate the AIM2 inflammasome including wild type , holin-lysin and ΔprkA L . monocytogenes . Importantly , strains that induce either Naip5/Nlrc4 inflammasome activation ( L . p . -FlaA ) [21] or inflammasome independent cell death ( LLO S44A L461T ) [55] ( Fig 2C ) did not demonstrate elevated cell death in GBP deficient macrophages , suggesting that in the context of L . monocytogenes infection GBPs may act as negative regulators of AIM2 inflammasome activation . Given its role in intracellular growth , cytosolic survival , inflammasome avoidance , and general cell wall and metabolic stress responses , we hypothesized that PrkA would be required for L . monocytogenes virulence . To test this hypothesis , mice were challenged with 1x105 L . monocytogenes wild-type or ΔprkA mutants , bacterial burdens in spleens and livers were measured two days post infection . Wild type infected mice harbored high burdens of bacteria in both their livers and their spleens with bacterial burdens reaching 108 bacteria per organ . Strikingly , ΔprkA mutants were essentially cleared , demonstrating 4–5 logs of attenuation ( Fig 2D ) . A constitutive expression complementation construct was able to significantly rescue virulence in vivo ( S1E Fig ) . ΔprkA mutants demonstrated lysozyme sensitivity , suggesting that their virulence defect could be due to killing in the blood during intravenous infection . To test this hypothesis , we assayed killing of wild type , ΔprkA mutants in whole blood [51] . While previously described lysozyme sensitive ΔpgdA/ΔoatA mutants [56] were killed rapidly in whole blood , wild type L . monocytogenes and ΔprkA mutants survived ( S3 Fig ) , suggesting that sensitivity to lysozyme in the blood is not likely responsible for the virulence defects observed in vivo . Taken together , these data suggest that PrkA is required for intracellular growth , cytosolic survival avoidance of the AIM2 inflammasome and ultimately virulence in vivo . To begin to understand how PrkA regulates virulence potential , we hypothesized that specific PrkA substrates may regulate metabolic or cell wall stress responses required for virulence in vivo . The requirement of PrkA for intracellular growth , cytosolic survival and evasion of the inflammasome was reminiscent of phenotypes previously ascribed to a ΔyvcK mutant [17] . Furthermore , recent reports demonstrated that in M . tuberculosis and B . subtilis , YvcK homologues are PASTA kinase substrates [36 , 47] . We hypothesized that YvcK may also be involved in cell wall homeostasis and/or carbon metabolism . Indeed , in M . tuberculosis and B . subtilis , YvcK is required for growth on gluconeogenic substrates and for maintenance of cell wall homeostasis . We evaluated the sensitivity of the ΔyvcK mutant to the same cell wall stresses as previously described for the ΔprkA mutant ( Table 1 ) . As was previously described , ΔyvcK mutants were hypersusceptible to lysozyme in BHI similar to ΔprkA mutants , a phenotype that could be complemented by inducible expression of yvcK ( S4A Fig ) [57] . Additionally , although the magnitude of sensitivity was not as severe as in the ΔprkA mutant , ΔyvcK mutants were hypersusceptible to all of the same cell wall stresses with the exception of tunicamycin ( Table 1 ) . Despite increased sensitivity to some cell wall acting agents , no change in cell wall thickness was observed in the ΔyvcK mutant ( 24 . 2 ±1 . 43 nm wild-type vs 25 . 0 ±0 . 56 nm ΔyvcK ) consistent with our previous observations with a ΔprkA mutant . Similarly , although ΔyvcK mutants grew normally in rich media or minimal media with glucose-6-phosphate as the primary carbon source , they demonstrated severe growth defects in minimal media with glycerol as the primary carbon source ( Fig 3A and 3B ) . The growth defect in glycerol could be complemented by expression of yvcK in trans ( S4B Fig ) . In minimal media with glycerol we also observed instances of severe morphology defects in the ΔyvcK mutant and other morphology changes similar to the ΔprkA mutant ( Fig 3C ) . Given the similarities in phenotypes between the ΔprkA and ΔyvcK phenotypes in vitro , we assessed virulence of ΔyvcK mutants ex vivo and in vivo . Again , consistent with both with the phenotypes observed with a ΔprkA mutant and the phenotypes previously reported , ΔyvcK mutants were attenuated for intracellular replication ( Fig 3D ) , cytosolic survival ( 3E ) and avoidance of inflammasome activation ( 3F ) . Complementation in trans restored intracellular growth and host cell death to wild-type levels ( S4C and S4D Fig ) . Finally , ΔyvcK mutants were also severely attenuated for virulence in a murine model of disseminated Listeriosis ( Fig 3G ) and attenuation could be rescued by constitutively expressed yvcK ( S4E Fig ) . Taken together these data suggest that YvcK , like PrkA , is required for cell wall homeostasis , glycerol metabolism and virulence both ex vivo and in vivo . We next hypothesized , based on previous reports in B . subtilis and M . tuberculosis , combined with the congruent observations with the ΔprkA and ΔyvcK mutants , that YvcK would be a substrate of the PrkA kinase in L . monocytogenes . To test this hypothesis , we performed in vitro phosphorylation assays with [γ-32P]ATP using purified His-tagged YvcK and GST-tagged PrkA . L . monocytogenes YvcK is phosphorylated by PrkA in vitro , ( Fig 4A ) and subsequent MS/MS of the phosphorylated YvcK indicated that , unlike what was previously observed in M . tuberculosis and B . subtilis , there were two independent sites of phosphorylation on YvcK , threonine 252 and threonine 256 ( Fig 4B ) . Not only was the double phosphorylation unique , but the phosphorylation sites map to a different location on the predicted tertiary structure of the protein compared to M . tuberculosis and B . subtilis whose phosphorylated threonines are located close to the C-terminus . To confirm the sites of phosphorylation , we constructed single or double threonine to alanine phosphoablative point mutants at T252 and T256 . Both single point mutants were phosphorylated , although the T252A mutation reduced phosphorylation to a greater extent than the T256A mutation . The double T252A/T256A mutation completely abolished phosphorylation ( Fig 4A ) . Mapping of the PrkA autophosphorylation sites revealed phosphorylation at serine 62 , threonine 290 , and threonine 308 as sites of autophosphorylation with 91% coverage of the predicted cytosolic region of the protein ( S5A–S5C Fig ) . The PrkA T290 and T308 phosphorylation sites map to a putative unstructured region between the kinase domain and the membrane spanning region that is consistent with previous autophosphorylation sites in M . tuberculosis [58] . While enriching for phosphopeptides yielded two additional autophosphorylation sites on PrkA S213 and T289 ( S2D and S2E Fig ) , no additional sites were revealed on YvcK . Additionally , we expected to observe phosphorylation in the putative activation loop , as this has been observed for the PASTA kinases in M . tuberculosis and Bacillus anthracis [58 , 59] . Although we did observe a quadruply phosphorylated fragment ion that contained the putative loop region , the large size of the fragment ion prevented our ability to map specific phosphorylation sites . Taken together these data suggest that YvcK is a PrkA substrate in L . monocytogenes and indicate novel phospho-regulatory sites on both PrkA and YvcK . Given the conserved phenotypes of the ΔprkA and ΔyvcK mutants , combined with the observation that PrkA phosphorylates YvcK , we hypothesized that phosphorylation of YvcK by PrkA would influence virulence . To test this hypothesis we generated phosphoablative T252A/T256A and phosphomimetic T252E/T256E point mutants in the native yvcK locus and assayed glycerol metabolism , cell wall stress responses , cytosolic survival and inflammasome activation ex vivo and ultimately virulence in vivo . Both the ablative and mimetic mutant versions of YvcK were expressed at similar levels to wild type YvcK as indicated by western blot , although the mimetic version of the protein migrates slightly slower in SDS-PAGE ( Fig 5A ) . We observed upregulation of YvcK in the ΔprkA background and when strains were grown in a sub-inhibitory concentration of lysozyme ( Fig 5A ) . We found that mutation of the phosphorylated threonines to either phosphoablative alanines or phosphomimetic glutamic acids had minimal effects on the ability of L . monocytogenes to withstand cell wall stresses in vitro ( Table 2 ) . The effects phospho-mutations during metabolic stress were more nuanced as the phosphoablative mutant phenocopied wild type L . monocytogenes while the phosphomimetic mutants demonstrated a moderate growth defect in glycerol , though not to the level of a full ΔyvcK or ΔprkA mutant ( Fig 5B ) . Even more surprisingly , we found that the T-A phosphoablative YvcK mutant was indistinguishable from wild type when we assessed cytosolic survival ( Fig 5C ) , inflammasome avoidance ( Fig 5D ) or virulence in vivo ( Fig 5E ) , whereas the T-E phosphomimetic mutant essentially phenocopied a ΔyvcK mutant . Taken together , these data suggest that phosphorylation of YvcK inactivates at least some functions of the protein and that regulation of the YvcK phosphorylation state is critical for the virulence of L . monocytogenes . Furthermore , given that the phosphomimetic mutations did not affect cell wall stress responses , these data suggest that the attenuation of the ΔyvcK mutant is unlikely to be due to defects in cell wall stress responses . The condition ( s ) under which YvcK phosphorylation is beneficial remain to be determined . Similarly , the substrates of PrkA that mediate cytosolic survival and ultimately virulence in vivo are similarly yet to be discovered .
In addition to the classically defined cytosolic pathogens , it is becoming increasingly clear that a number of extracellular and vacuolar pathogens spend at least a portion of their lifecycle in the cytosol of host cells . Furthermore , the identification of extensive arrays of cytosolic innate immune sensing machinery and its role in controlling bacterial infections suggest that the host must survey and defend its cytosol . Indeed , the delivery of non-pathogenic or non-cytosol adapted bacteria leads to their detection and ultimate elimination from the cytosol , however , the mechanisms by which cytosol adapted pathogens sense and respond to the unique environment of the host cytosol is largely unknown . Here , we demonstrate that L . monocytogenes utilizes its highly conserved PASTA kinase , PrkA , to facilitate cell wall homeostasis , metabolic adaptation , cytosolic survival , inflammasome avoidance and ultimately virulence . Furthermore , we identified a highly conserved protein of unknown function , YvcK , as a substrate of PrkA , which is also required for cell wall homeostasis , metabolic adaptation , cytosolic survival , inflammasome avoidance and ultimately virulence . Finally , although the function of YvcK in metabolic and cell wall stress responses was largely independent of phosphorylation by PrkA , adding phosphomimetic , nonreversible negative charges at the YvcK phosphorylation sites inhibited virulence ex vivo and in vivo . While the threonine to glutamic acid mutation can sometimes support a relatively simple phosphorylation model , biochemically they are similar but not equivalent [60] . Additionally , the activity of Ser/Thr kinases may be more complicated than an on/off switch [61] which highlights the need to determine substrates under specific environmental conditions . Nevertheless , our data suggest that regulation of YvcK phosphorylation is critical for virulence while additional , yet to be identified substrates of PrkA must be phosphorylated in vivo to promote virulence . A large number of PASTA kinase substrates in a variety of other organism have been identified , however , the complete PrkA phosphoproteome of L . monocytogenes has not been determined . Lima et al previously identified 62 proteins that interact with PrkA , although validation of any of these proteins as bonafide phosphor-substrates of PrkA was not determined [62] . Eight interacting proteins are known PASTA kinase substrates in B . subtilis , S . pneumonia , or M . tuberculosis [35 , 63–66] while an additional sixteen of these PrkA interacting proteins are also phosphorylated on Ser/Thr residues in L . monocytogenes [62 , 67] . Several of these , including PTS system mannose-specific factor IIAB ( MptA ) , fructose-1 , 6-bisphosphate aldolase ( FbaA ) , glyceraldehyde-3-phosphate dehydrogenase ( Gap ) , pyruvate dehydrogenase ( PdhC ) , and redox-sensing transcriptional repressor ( Rex ) are directly involved in central metabolism . These data , along with our findings , suggest that PrkA plays a major role in regulating central metabolism in L . monocytogenes . Lima et al . also observed MreB , but not other cytoskeletal proteins such as DivIVA or FtsZ as interacting partners , even though many of these are conserved substrates in multiple other species [36 , 68 , 69] . The lack of these proteins in the interactome study may be due to the propensity for these proteins to form insoluble membrane bound complexes during lysis [68 , 70] . YvcK was also not identified as an interacting protein with PrkA potentially due to a limit of detection issue from low levels of the protein in the extract . Finally , a ActA was previously identified as a PrkA substrate , however the effect of phosphoablative or phosphomimetic mutations has not been determined [62 , 71] . Although lack of phosphorylation of ActA may explain part of the virulence defect of a ΔprkA mutant , ΔactA mutants do not have an intracellular growth or lysis defect so phosphorylation of ActA is not likely to be responsible for these phenotypes in a ΔprkA mutant [71] . Importantly , as our data demonstrate , identification of phosphorylation events in vitro , even when correlated strongly with phenotypes in vivo , does not necessarily demonstrate the relevance of phosphorylation ex vivo or in vivo . As such , we are currently developing novel approaches to characterize the PrkA specific phosphoproteome during intracellular growth . Regulation of cell wall homeostasis is critical for virulence and PASTA kinases in a variety of other organisms are involved in cell division and cell wall homeostasis [40 , 72] . Although our results demonstrated that there were no gross defects in cell wall thickness in ΔprkA mutants , we cannot rule out the possibility that there are defects specifically in either the abundance of peptidoglycan/wall teichoic acid and/or the structure of these polymers . Indeed , our results suggest that PrkA is selectively involved in multiple , but not all , aspects of cell wall homeostasis as indicated by increased sensitivity of the ΔprkA mutant to β-lactams , tunicamycin , antimicrobial peptides and lysozyme but not vancomycin , consistent with the phenotypes of PASTA kinase mutants in S . aureus and S . pyogenes [45 , 73 , 74] . In S . aureus Δstk1 mutants have reduced levels of UDP-MurNAc as well as other downstream peptidoglycan precursors [75] . β-lactams function by inactivating PBPs and by reducing the peptide crosslinking in peptidoglycan [76 , 77] . ΔprkA mutant sensitivity to β-lactams may be due to changes in peptidoglycan precursors caused by phosphorylation of GlmU [35] or changes in transcription of proteins in the MurA-G operon [78] . Inhibition of MurE and MurF or other early steps in peptidoglycan synthesis cause increased sensitivity to β-lactam antibiotics in S . aureus [79–81] . Alternatively , the increased β-lactam sensitivity of the ΔprkA mutant may be due to mislocalization of PBPs , potentially through the misregulation of MreB , a putative PrkA interacting protein that has been found to be phosphorylated on a serine [62 , 67 , 82] . MreB activity has been linked to β-lactam sensitivity [83] . The increased tunicamycin sensitivity of the ΔprkA mutant may be caused by perturbations in the peptidoglycan synthesis pathway through its inhibition of MraY [84] or in wall teichoic acid biosynthesis through its inhibition of TagO [84] . Although MraY has not been identified as a PrkA substrate the next enzyme in the pathway ( MurG ) is a putative interacting partner with PrkA [62] and proteins involved in MraY regulation , DivIVA/Wag31 [85] , are conserved PASTA kinase targets . While inhibition of wall teichoic acid ( WTA ) synthesis through TagO is not detrimental to growth in S . aureus [86] , this may require an unknown compensatory mechanism which is non-functional in a ΔprkA mutant . Additionally , in L . monocytogenes , resistance to antimicrobial peptides is linked to rhamnosylation of WTA [87] . Lmo1081 ( RmlA ) , required for rhamnosylation of WTA , is a PrkA interacting protein and Ser/Thr phosphoprotein [62 , 67 , 87] , potentially linking the increased sensitivity of the ΔprkA mutant to tunicamycin and LL-37 . Finally , effect of the ΔprkA mutation of lysozyme resistance may be caused by several different mechanisms including peptidoglycan precursor abundance , mislocalization of pbps , or regulation of PgdA and OatA , peptidoglycan modification enzymes [5 , 56] . Although a detailed understanding of PrkA regulation of cell wall remains to be determined , pharmacologic targeting of PrkA could result in synergistic activity with already existing antimicrobials to provide a new approach to combating gram-positive pathogens [46] . PknB in M . tuberculosis is essential while Stk1 in S . aureus is important for growth in some nutrient limiting conditions [88 , 89] . Consistent with these observations , we found that PrkA is required for growth in minimal media with glycerol as the primary carbon source . This effect may be due to direct modulation of glycolysis/gluconeogenesis by PrkA . Several conserved PASTA kinase substrates identified as both PrkA interactors and Ser/Thr phosphorylated proteins in L . monocytogenes are glycolytic enzymes [37 , 62 , 67] . Phosphorylation of glycolytic enzymes , such as FbaA , Gap , or PykA , could control their activity in order to regulate flux between competing pathways ( eg . The pentose phosphate pathway ) or to regulate flux between opposing pathways ( eg . Gluconeogenesis ) . Furthermore the master virulence regulator , PrfA , is regulated by [91] and regulates [11 , 90] central metabolism . How posttranslational regulation of central metabolism by PrkA may affect PrfA activity is unknown . For example , glycerol metabolism is required for optimal PrfA activity , therefore regulation of glycerol metabolism by PrkA may directly affect virulence factor expression . In addition to energy and carbon input into the cell PrkA may also have a major role in regulating energy expenditures . EF-Tu and EF-G are conserved PASTA kinase substrates [66 , 90] and interacting-Ser/Thr phosphorylated proteins [62 , 67 , 91] required for protein translation [92] , a process that can account for >50% of the ATP consumption by a cell [93] . Thus PrkA is likely to be crucial for matching energy intake and expenditure of the cell . Intracellular pathogens have evolved specific metabolic strategies to avoid disruption of host glycolysis to avoid innate immune detection and maintain their replicative niche [94] . Therefore , the regulation of central metabolism by PrkA could be considered an essential virulence factor . PrkA may also be important for regulating the production of specific metabolites . PASTA kinase mutants are defective in purine biosynthesis in multiple organisms [75 , 89 , 95] . This specific auxotrophy is not likely to be responsible for the PrkA growth defect as adenine is a normal component of IMM but may play a role in growth in vivo [8] . Metabolism is essential for bacterial pathogenesis and the use of novel metabolomics approaches will further elucidate how PrkA regulates metabolism to promote virulence . In both M . tuberculosis and B . subtilis YvcK homologues are required for cell wall homeostasis , cell morphology and gluconeogenic metabolism , but the enzymatic function of YvcK remains unknown [47–50] . We found that L . monocytogenes YvcK was similarly required for growth on glycerol , a gluconeogenic substrate for L . monocytogenes as well as for cell wall homeostasis and normal morphology . YvcK is similar to the 2-phospho-L-lactate transferase CofD found in M . tuberculosis and some Archea [48 , 50] . However many of the YvcK containing bacteria do not produce coenzyme F420 , the product of CofD [48 , 50] . Furthermore although structures of CofD and multiple YvcK homologues have been solved , thus far structural analysis has not predicted a specific interacting metabolite for YvcK [96] . In three different bacterial species YvcK has been shown to be important in metabolism and more specifically gluconeogenesis . Although carbon sources utilized for gluconeogenesis by each of these species are diverse , the conserved requirement for YvcK suggests that it regulates a central process in gluconeogenesis . YvcK is a conserved PASTA kinase substrate in several organisms , however , the exact sites and numbers of phosphorylations are different [36 , 47] . In M . tuberculosis CuvA ( YvcK ) -dependent phenotypes were phosphorylation independent [50] whereas in B . subtilis , cell wall phenotypes were rescued by phosphomimetic mutants [47] . Contrary to both of these results , in L . monocytogenes phosphoablative YvcK mutants phenocopied wild-type under all conditions whereas phosphomimetic mutations were detrimental for growth in glycerol and virulence in vivo . Taken together , our results suggest that while PASTA kinases regulate YvcK homologues in a variety of organisms , the mechanism of regulation is species specific . Additionally , while PASTA kinases are unique to gram-positive organisms , YvcK homologues are found across eubacteria and archaea [48] , therefore uncovering YvcK’s metabolic role could have broad implications for our understanding of central metabolism and virulence in a wide variety of YvcK containing pathogens . We found that both PrkA and its substrate YvcK were required for cytosolic survival and avoidance of the AIM2 inflammasome . The specific stresses leading to bacteriolysis of bacteria in the cytosol of host cells are currently unknown . Recently , Broz and colleagues demonstrated that GBPs were involved in cytosolic lysis of F . novicida and contributed to AIM2 activation , potentially through direct lysis of cytosolic bacteria [13] . In addition , previous work from Coers and colleagues suggested that GBPs were involved in activation of caspase-11-dependent inflammasome activation [97] . Our analysis of macrophages lacking the GBPs on chromosome 3 , including GBP1 , 2 , 3 , 5 , and 7 [98] , suggests that these GBPs are not required for cytosolic lysis and subsequent inflammasome activation following infection with either wild type L . monocytogenes or mutants with increased bacteriolysis in the cytosol , including ΔyvcK and ΔprkA mutants . Counterintuitively , we found that GBP deficient macrophages demonstrated increased host cell death following infection with strains that specifically activated AIM2 , suggesting that during L . monocytogenes infection GBPs act as a negative regulator of AIM2 activation . Given that this effect was only seen with L . monocytogenes strains that activate the AIM2 inflammasome and not with strains that activate the Naip5/Nlrc4 inflammasome , perhaps GBPs negatively regulate AIM2 inflammsome activation through masking or sequestering of DNA following bacteriolysis . It is possible that the GBPs located on chromosome 5 , including GBP4 , 6 , 8 , 9 , 10 and 11 [98] , could be required for L . monocytogenes bacteriolysis or that L . monocytogenes lyse due to some other stress or antimicrobial mechanism . Previously , a cationic antimicrobial peptide , ubiquicidin , had been purified from the cytosol of IFNγ activated macrophages and had been demonstrated to have anti-Listeria activity in vitro [12] . Similarly , lysozyme has recently been demonstrated to be able to access the cytosol and kill bacteria in this compartment , leading to inflammasome activation [56] . Consistent with these as potential causes of cytosolic bacteriolysis , both the ΔprkA mutant and the ΔyvcK mutant demonstrated increases in LL-37 and lysozyme sensitivity in vitro . Finally , ΔyvcK mutants and ΔprkA mutants demonstrate defects in morphology during growth on cytosolically available carbon sources , suggesting that lysis of these mutants may be due to metabolic defects that result in impaired cell wall homeostasis . Identification of additional PrkA substrates and the specific enzymatic activity of YvcK may further elucidate cell wall homeostasis and/or metabolic pathways required for cytosolic survival and virulence . In conclusion , we have demonstrated that the L . monocytogenes PASTA kinase PrkA and its conserved substrate YvcK play essential roles in cell wall homeostasis , metabolism , and ultimately virulence . Surprisingly , despite the exquisite conservation of phenotypes of the two null mutants , phosphorylation appears to inhibit the function of YvcK such that phosphomimetic YvcK mutants are highly attenuated in vivo while phosphoablative mutants phenocopy wild type L . monocytogenes . Importantly , in addition to identifying two novel essential virulence factors in L . monocytogenes , our work highlights the importance of identifying PrkA substrates during infection . Finally , given the high conservation of these proteins in a number of important pathogens and their conserved roles in virulence , targeting PASTA kinases and/or YvcK function represents a novel and exciting avenue for the development of new antimicrobials .
All L . monocytogenes strains used were 10403s background and the ΔyvcK and ΔprkA mutants were previously described [17 , 46] . The yvcK complementation vector pPL2e_riboE_yvcK was constructed as previously described [46] . Briefly , the theophylline inducible riboswitch E was added to yvcK by SOE PCR [99] , and cloned in pPL2e [100] . Point mutations in yvcK were made by designing the desired mutations into gBlocks ( IDT ) and subcloned in pPL2e_riboE_yvcK . YvcK inserts from the wild type or mutated pPL2e_riboE_yvcK constructs were then used as the source for subsequent cloning into pET20b to facilitate His-tagged purification from Rosetta pLysS E . coli and into pKSV7 for subsequent reintegration into the native locus of the ΔyvcK mutant . Constitutive expression of yvcK and prkA from the pHelp promoter was achieved by cloning into pIMK2 [101] . For ΔprkA complementation , the resulting pHelp_prkA construct was cloned into a new pPL1 vector ( pPL1k ) . pPL1k was constructed by removing the chloramphenicol resistance cassette from pPL1 [100] with restriction enzymes ApaLI and PvuI and inserting the kanamycin resistance cassette from pIMK2 [101] . The pPL1k_prkA construct was conjugated into a new phage-cured ΔprkA strain . For a complete list of strains see S1 Table . For all assays overnight cultures of L . monocytogenes strains were grown to stationary phase at 30°C with no shaking in Brain-Heart Infusion ( BHI ) . Overnight cultures grown to stationary phase at 30°C with no shaking in BHI media were inoculated at a 1:50 ratio into 96-well plates containing BHI with growth inhibitors in 2 fold serial dilutions up or down from 1μg/mL . Plates were grown at 37°C with continuous shaking for 12 hours in an Eon or Synergy HT Microplate Spectrophotometer ( BioTek Instruments , Inc . , Winooski , VT ) and OD600 was read every 15 minutes . The MIC was defined as the lowest concentration at 12 hours that gave an equivalent OD600 to the starting inoculum . All growth assays were performed with at least 5 biological replicates and the mean MIC was selected [50] . Overnight cultures grown to stationary phase at 30°C with no shaking in BHI were washed in PBS and inoculated at a 1:50 ratio into BHI or Improved Minimal Media ( IMM ) [102] and grown at 37°C with continuous shaking . For BHI OD600 was read at 1 hour time points for 9 hours . IMM was made with either 55mM , Glycerol , or Glucose-6-Phosphate . For IMM OD600 was read at 6 hour time points for at least 54 hours . All growth assays were performed with 3 biological replicates . Cultures were harvested at an OD of 0 . 5 in BHI or after 6 hours of growth in IMM . Cells were fixed , washed , dried , infiltrated , and sectioned as previously described [103] . Sections were imaged with a Phillips CM120 STEM microscope . Cell wall thickness was measured with ImageJ software . Ten measurements at the mid-cell of ten bacteria were taken for each of 3 biological replicates . Bone marrow derived macrophages ( BMDMs ) were prepared from C57BL/6 mice as previously described [104] . BMDMs were plated at 5*106 cells per 60mm dish with coverslips and allowed to adhere overnight . BMDMs were infected at an MOI of 0 . 2 and infection was quantified as previously described [17] . The growth curve is representative of 3 biological replicates . Intracellular lysis was measured as previously described [17] . Briefly , immortalized INFAR-/- BMDMs [105] were plated at 5*105 cells/well in 24 well plates overnight . Cells were infected at an MOI of ten with strains containing the pBHE573 reporter construct . At 1 hour post infection , media was removed from the plate and replaced with fresh media containing gentamycin . At six hours post infection cells were lysed in TNT lysis buffer . Cell supernatants were mixed with luciferase reagent as previously described . Luciferase activity was measured in a Synergy HT Microplate Spectrophotometer ( BioTek Instruments , Inc . , Winooski , VT ) . A representative experiment from 3 biological replicates is shown . Induction of host cell death by L . monocytogenes infection was measured by the lactate dehydrogenase ( LDH ) assay as previously described [106] . Briefly , 5*105 BMDMs were pre-treated with Pam3CSK4 ( Invivogen tlrl-pms ) in 24-well plates overnight . BMDMs were infected at a MOI of five . At ½ hour post infection , media was removed from the plate and replaced with fresh media containing gentamycin . For the experiment with LLO S44A L461T gentamycin was removed after an hour . Six hours post infection , macrophage cell death was determined by measuring LDH release into the culture supernatant . 100% lysis was determined by addition of Triton X-100 to a final concentration of 1% . All LDH assays are the average of 3 biological replicates . Acute mouse IV infections were performed according to IAUCC approved protocol as previously described [21] . Briefly , 6 to 8-week-old female C57BL/6 mice were infected IV with 1×105 CFU . 48 hours post-infection , livers and spleens were harvested , homogenized in PBS with 0 . 1% NP-40 , and plated for CFU . Two independent replicates of each experiment with 5 mice per group were performed . PrkA was purified as previously described [46] . For purification of YvcK , overnight cultures of Rosetta pLysS pET20b constructs were inoculated at a 1:50 ratio and grown to an OD of ~0 . 5 at 37°C 250rpm . IPTG was added to a final concentration of 1mM and rpm was lowered to 180 . At 3 hours post induction cultures were pelleted , resuspended in PBS , and stored at -80°C . Pellets were thawed , lysed , and pelleted . The supernatant was collected and mixed with NTA-Nickel resin ( Pierce ) for 30 minutes at 4°C . Resin was pelleted , washed , and the protein was eluted with 250mM Imidazole in 20mM Tris pH 7 . 4 100mM NaCl . Elutions were concentrated and further purified using a Sephadex 75 size exclusion column ( GE Healthcare ) on an ÄKTA purifier FPLC ( GE Healthcare ) . Protein was eluted using an isocratic method in a buffer containing 150mM NaCl and 10mM Tris pH 8 . 0 . Non-aggregated fractions indicated by UV absorbance were visualized on SDS-PAGE and fractions of >98% purity were pooled and used for biochemical assays . Protein concentration was determined by BCA assay ( Pierce ) according to manufacturer’s protocols . Phosphorylation assays were performed by mixing 3 μg of kinase with 2μg YvcK in a 30-μl reaction mixture containing 50 mM Tris-HCl ( pH 7 . 4 ) , 1 mM dithiothreitol ( DTT ) , 5 mM MgCl2 , 250 μM ATP , and 1 μCi of [γ-32P]ATP , followed by incubation at room temperature overnight . The reactions were terminated by the addition of 5× SDS loading buffer to the mixture . Samples were separated by SDS-PAGE , fixed , dried , and analyzed by autoradiography Phosphorylation of YvcK was performed with 10μg of kinase and 100μg of YvcK in 100mM ATP in containing 50 mM Tris-HCl ( pH 7 . 4 ) , 1 mM dithiothreitol ( DTT ) , 5 mM MnCl2 . The reaction was digested with trypsin . Digests were cleaned with OMIX C18 SPE cartridges ( Agilent , Palo Alto , CA ) according to the manufacturer’s protocol . Where indicated , phosphopeptides were enriched with titanium dioxide coated beads and eluted from the beads with ammonium hydroxide . Peptides were analyzed by nanoLC-MS/MS with a Agilent 1100 nanoflow system coupled to a hybrid linear ion trap-orbitrap mass spectrometer ( LTQ-Orbitrap Elite , Thermo Fisher Scientific ) equipped with an EASY-Spray electrospray source . Raw MS/MS data was converted to mgf file format and used to search against the L . monocytogenes RefSeq database with a list of common lab contaminants using the Mascot search engine 2 . 2 . 07 ( Matrix Science ) . Protein annotations , significance of identification , and spectral based quantification was done with help of Scaffold software ( version 4 . 3 . 2 , Proteome Software Inc . , Portland , OR ) . Overnight cultures grown to stationary phase at 30°C with no shaking in BHI media were inoculated at a 1:50 ratio into 96-well plates containing BHI with Staurosporine at 20μM . Plates were grown at 37°C with continuous shaking for 12 hours in an Eon or Synergy HT Microplate Spectrophotometer ( BioTek Instruments , Inc . , Winooski , VT ) and OD600 was read every hour . Growth curve is representative of 3 biological replicates . Overnight cultures grown to stationary phase at 30°C with no shaking in BHI were inoculated at a 1:50 ratio into BHI with or without 250μg lysozyme and grown to an OD of 0 . 5 at 37°C shaking . 10mLs of culture was pelleted , washed in PBS , and resuspended in lysis buffer ( 50mM Tris pH 7 . 4 , 5mM DTT , 0 . 1% SDS ) . Pellets were bead beat for 2 minutes and beads and cell debris were pelleted . Lysate was filtered through a 0 . 2 micron filter and total protein level was quantified by BCA assay . Equivalent protein concentrations were run on a SDS-PAGE gel and transferred to a Hybond-ECL membrane ( GE ) . Custom polyclonal anti-YvcK antibody was used to assess protein levels together with 2° anti-rabbit DyLight 800 and a Li-Cor Odyssey 9120 . Quantification was performed with attached Odyssey software . Mice were cared for according to the recommendations of the NIH , published in the Guide for the Care and Use of Laboratory Animals . All techniques used were reviewed and approved by the University of Wisconsin-Madison Institutional Animal Care and Use Committee ( IACUC ) under the protocol M02501 . Prism 6 ( GraphPad Software ) was used for statistical analysis of data . Means from two groups were compared with unpaired two-tailed Student’s T-test . Means from more than two groups were analyzed by one-way ANOVA with a post-hoc LSD Test . Medians from two groups were compared with Mann-Whitney Test . * indicates a statistically significant difference ( P is less than 0 . 05 ) .
|
Infection with intracellular pathogens causes a majority of the global infectious disease associated mortality . A number of intracellular pathogens must directly access the host cytosol in order to cause disease; however , non-cytosol adapted bacteria do not survive or replicate upon access to the cytosol . The mechanisms cytosolic pathogens use to adapt to this niche are largely unknown . The model cytosolic bacterial pathogen Listeria monocytogenes contains a single penicillin-binding-protein and serine/threonine associated ( PASTA ) kinase , PrkA . In other bacteria , PASTA kinases bind cell wall fragments and phosphorylate downstream effectors involved in cell wall synthesis , central metabolism , virulence , cell division , and biofilm formation . We demonstrate that in L . monocytogenes , PrkA is required for cell wall homeostasis , growth under nutrient limiting conditions , survival and replication in host cells , and virulence in vivo . Furthermore , we identify a highly conserved protein of unknown function , YvcK , as a PrkA substrate . We demonstrate that L . monocytogenes YvcK is similarly required for cell wall stress responses , growth on glycerol , cytosolic survival and virulence in vivo . Surprisingly , a phosphomimetic , nonreversible negative charge at the phosphorylation sites on YvcK inactivates functions of the protein related to intracellular survival and virulence , suggesting that the identification of PASTA kinase substrates phosphorylated during infection will be critical to our understanding of this central regulator metabolism , cell wall homeostasis and ultimately virulence .
|
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2016
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The Listeria monocytogenes PASTA Kinase PrkA and Its Substrate YvcK Are Required for Cell Wall Homeostasis, Metabolism, and Virulence
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Visceral leishmaniasis is a lethal parasitic disease transmitted by phlebotomine sand flies . The largest focus of VL in Ethiopia is located in the lowland region bordering Sudan , where the epidemiology is complicated by the presence of thousands of seasonal agricultural workers who live under precarious conditions . We conducted two parallel case-control studies to identify factors associated with VL risk in residents and migrants . The studies were conducted from 2009 to 2011 and included 151 resident cases and 157 migrant cases , with 2 matched controls per case . In multivariable conditional regression models , sleeping under an acacia tree at night ( odds ratios ( OR ) 5 . 2 [95% confidence interval 1 . 7–16 . 4] for residents and 4 . 7 [1 . 9–12 . 0] for migrants ) , indicators of poverty and lower educational status were associated with increased risk in both populations . Strong protective effects were observed for bed net use ( OR 0 . 24 [0 . 12–0 . 48] for net use in the rainy season among residents , OR 0 . 20 [0 . 10–0 . 42] for any net use among migrants ) . For residents , living in a house with thatch walls conferred 5-fold and sleeping on the ground 3-fold increased risk . Among migrants , the risk associated with HIV status was borderline significant and sleeping near dogs was associated with 7-fold increased risk . Preventive strategies should focus on ways to ensure net usage , especially among migrant workers without fixed shelters . More research is needed to understand migration patterns of seasonal labourers and vector bionomics .
Visceral leishmaniasis ( VL ) is a lethal parasitic disease transmitted by phlebotomine sand flies . The typical clinical picture is a chronic systemic illness with fever , weight loss , splenomegaly , hepatomegaly , and bone marrow suppression with pancytopenia . East Africa is second only to the Indian subcontinent in annual VL incidence [1] . Although most transmission is thought to be anthroponotic , Leishmania donovani has also been found in dogs and wild mammals in Sudan and Ethiopia [2]–[4] . The risk of acquiring the disease is mediated through poor housing conditions , lack of personal protective measures against the vector and economically-driven migration that brings non-immune hosts into VL-endemic areas [5] . In 2005 , a new VL epidemic was reported in Libo Kemkem in the highlands of north-western Ethiopia , where only a handful of cases had been reported in the past [2] , [6] . The parasite was thought to have been introduced by agricultural labourers returning from seasonal work in the Humera-Metema lowland area bordering Sudan , a hypothesis supported by the high prevalence of positive leishmanin skin tests found in returned migrant workers and molecular tracking of strains from the two regions [2] , [7] . The lowland border area hosts the largest VL focus in Ethiopia and is adjacent to VL-endemic areas of Sudan [5] . The main vector on both sides of the border is Phlebotomus orientalis , found in association with cracked black cotton-clay soils and acacia-balanites forest [8] , [9] . Hundreds of thousands of seasonal agricultural workers migrate into the Humera-Metema area each year , staying months to years and often living under precarious conditions , and VL occurs in both long-term residents and migrants [10] , [11] . In this complex epidemiological setting , understanding risk factors for VL is crucial for the design of appropriate interventions . We conducted two parallel case-control studies to identify factors associated with VL risk in residents and migrants .
The studies were conducted in Kafta-Humera district , Tigray regional state , bordered on the west by Sudan , and on the north by the Tekezé River which separates Ethiopia from Eritrea ( Figure 1 ) . Kafta-Humera had an estimated total population of 92 , 144 in 2007; 32 . 8% were urban dwellers . The reported HIV prevalence in the district was 4 . 6% in 2009 . Before the 1970s , the region reported only sporadic cases of VL . The Kafta-Humera district is one of Ethiopia's most fertile agricultural zones with large scale of farming of cash crops such as sesame , maize , cotton and sorghum . Since 1970 , linked to an extensive program of agricultural development and the consequent increase in the influx of migrant workers , the number of VL cases has rapidly increased . Migrants travel to Kafta-Humera to work in the sowing and harvest seasons , mainly from October to February and May to July . Seasonal agricultural work attracts approximately 250 , 000 migrant laborers each year from non-endemic parts of Tigray and neighboring regions . The study protocols were approved by the ethical review committees of the Ethiopian Public Health Association ( resident protocol ) and the Tigray Regional State Health Bureau ( migrant protocol ) . Each adult participant provided written informed consent . A parent or guardian provided consent on behalf of child participants . All research activities adhered to the principles expressed in the Declaration of Helsinki . Approval to record existing hospital data on HIV status was given only for the migrant protocol; these data are therefore missing for the resident protocol . Because the resident and migrant populations differed substantially in terms of practical options for valid control selection and some aspects of exposure history , we performed the two investigations independently . An effort was made to maintain key variables in both investigations , but the difference in living circumstances required some questions to be worded differently . Cases were ascertained for both studies at the Kashay Aberra Hospital ( KAH ) which contained a VL treatment center operated by Médecins Sans Frontières ( MSF ) -Holland until to June 2009 . After June 2009 , responsibility for VL treatment was assumed by regular hospital staff . This hospital is the main VL treatment facility for the area and treated 348 VL cases in 2009 , 505 in 2010 and 405 in 2011 . For both studies , VL case definitions followed the hospital diagnostic algorithm . A past VL case was a patient with at least 2 weeks of fever plus weight loss and/or splenomegaly , diagnosed by KAH based on positive DAT ( antigen from the Royal Tropical Institute , Amsterdam , The Netherlands ) or rK39 dipstick and treated with antileishmanial drug with clinical resolution of symptoms; or a patient with Leishmania amastigotes demonstrated in splenic aspirate . A current VL case was a patient diagnosed with illness characterized by at least 2 weeks of fever plus weight loss and/or splenomegaly , plus Leishmania amastigotes in the relevant aspirate and/or positive DAT or rK39 dipstick result . Both studies included 2 controls per case matched by sex and age group ( <5 , 5–14 , 15–39 and ≥40 years ) . A meta-analysis estimated rK39 and DAT sensitivities of 79% and 93% in East African studies , with specificities of 85% and 96% respectively [12] . A recent article from the study hospital showed rK39 rapid test and DAT sensitivities of 84% and 94% , respectively [13] . Treatment was administered by the hospital staff following standard MSF and Ethiopian national treatment guidelines; in general , first-line treatment for non-severe VL consisted of 30 days of sodium stibogluconate 20 mg/kg/day whereas severely ill VL patients , relapses and those with HIV co-infection were treated with either conventional amphotericin B 1 mg/kg/day on alternate days for 30 days or AmBisome 5 mg/kg in 4 to 6 on alternate days to reach a total dose of 20 to 30 mg/kg . Treatment and outcomes at this hospital were described in an earlier publication [14] . We used a community-based strategy to recruit controls for the resident study . Because a community-based approach was impossible for the migrant study due to the transience of the migrants , we recruited controls for this study in the hospital on services other than the VL treatment service . The two studies were therefore conducted independently , although an effort was made to include key variables in both questionnaires . The target sample size was determined in PS ( Power and Sample Size Calculation Software Package , Vanderbilt University , Nashville , TN ) . Based the assumption of 30% exposure among controls , we estimated that 150 cases and 300 controls were required for each study to achieve 90% power , with 95% confidence , and 5% precision to detect a variable with an odds ratio of 1 . 9 . Details specific to each study are included below . Structured questionnaires were used to collect the data . For the resident study , trained field workers administered the questionnaire under the supervision of study team members . For the migrant study , trained hospital nurses familiar with VL diagnosis and treatment procedures administered the questionnaires . One hospital supervisor coordinated data collection in collaboration with the study team . Data were single-entered in databases designed using Epidata® software and managed by the study data manager . All questionnaires were reviewed in the field and the database and questionnaire compared by two independent observers . The data were analysed using univariable and backwards stepwise multivariable conditional regression with Wald 95% confidence limits , to account for the matched design in SAS 9 . 2 ( SAS Institute Inc , Cary , NC ) . The Kruskal-Wallis test was used for comparison of continuous variables . Variables with p<0 . 10 in the univariable analyses was tested in multivariable model . Interaction between variables tested using interaction terms .
Each study recruited slightly more than 150 VL cases ( 151 residents , 157 migrants ) with two matched controls per case ( Table 1 ) . Among residents , 87% of case-control sets were male , while 99% of migrant sets were male . In both studies , the mean age was early to mid-twenties . However , resident cases included 5 ( 3 . 3% ) children younger than 5 years , 29 ( 19 . 2% ) 5–14 years old and 13 ( 8 . 6% ) adults 40 years or older , while all migrant cases except one were between 15 and 39 years old . Of the 151 resident cases , 125 ( 83% ) were treated in the past at KAH , 24 ( 16% ) were under treatment and 2 ( 1% ) were new cases diagnosed during the survey . All 157 migrant cases were under treatment at the time of recruitment . Migrant cases were significantly more likely than their controls to come from Amhara regional state , whereas controls were more likely to be from Tigray ( Table 1 ) . In univariate analyses . factors associated with elevated risk in both studies included sleeping outside , on the ground or under an acacia tree ( Tables 2 , 3 and 4 ) . Bed net use during the rainy season was associated with significant protection among residents ( Table 3 ) . Seasonal net use data were not collected for migrants since they were not usually in the site year-round; however , ever having used a bed net was found to be protective with the same odds ratio ( 0 . 20 ) as rainy season net use in residents ( Table 4 ) . Indicators of household poverty and lower educational status were associated with higher VL risk in both studies ( Table 2 and 4 ) . Some significant risk factors were found only in one of the studies . In the resident study , having house walls constructed from grass thatch was associated with an odds ratio of 4 . 5 compared to walls made from earth or other materials ( Table 2 ) . Data on HIV status were available only in the migrant study; HIV infection was associated with a 4-fold increase in VL risk ( Table 4 ) . Increased risk was also seen for migrants who slept near cattle , dogs or termite mounds , ate meat less than once per month , and those that ate porridge rather than injera as a staple food . Risk was higher for migrants who had spent a year or more in Humera compared to those with shorter duration of exposure there , but higher for those coming from Amhara than from Tigray , where Humera is located . In multivariable conditional regression models , both datasets demonstrated elevated risk associated with sleeping under an acacia tree at night ( odds ratio ( OR ) 5 . 22 [95% confidence interval 1 . 66–16 . 42] and 4 . 74 [1 . 88–11 . 99] for residents and migrants respectively ) , indicators of poverty ( OR 3 . 22 [1 . 42–7 . 33] for low income for residents , OR 4 . 65 [2 . 33–9 . 29] for use of porridge as the staple among migrants ) and lower educational status ( Table 5 ) . Strong protective effects were observed in both studies for bed net use ( OR 0 . 24 [0 . 12–0 . 48] for use in the rainy season for residents , 0 . 20 [0 . 10–0 . 42] for ever used net among migrants ) . For residents , living in a house with thatch walls ( Figure 2 ) rather than earth conferred 5-fold and sleeping on the ground close to 3-fold increased risk . Among migrants , the risk associated with HIV status was borderline significant and sleeping near dogs was associated with 7-fold increased risk .
Although the northern lowland border with Sudan has long been known as the most important VL-endemic zone in Ethiopia , our data represent the first systematic evaluations of risk factors for disease in the area . Several overarching themes emerge from both the resident and migrant data . Poverty and low educational status are strong underlying factors in both studies . Environmental associations also emerged , including sleeping under acacia trees and in more exposed settings ( in houses with thatch walls , outside or on the ground ) . A protective effect of bed net use was seen in both studies , and supports the policy of net use as a control strategy . The massive scale-up of insecticide-treated nets for malaria control launched by the Ministry of Health in 2005 may therefore have collateral benefits for VL control [15] . Humera is located near the Eritrean and Sudanese borders , acting as a node for cross-border trade and traffic . An estimated 250 , 000 migrant workers come to the area each farming season ( personal communication , Kafta-Humera District Health Office , 2010 ) . Many migrants come from the highlands , where until recently there was no reported VL transmission . Return of newly infected migrant workers was hypothesized to have triggered the VL epidemic in Libo Kem Kem and Fogera in the mid-2000s [2] . Even now , transmission is focal and uncommon compared to the lowland areas , so most highland residents are likely to have no pre-existing immunity to VL . Population mobility and socioeconomic vulnerability also contribute to HIV transmission in the border region , where VL is a major opportunistic infection . Several years prior to our study , the reported prevalence was 11–13% in counseling and testing centers and 29% among VL patients [13] . In 2008 , KAH treated a total of 376 VL patients of whom 39% were migrant workers; 23% of migrants and 40% of residents were HIV positive ( MSF-H , unpublished data ) . The HIV prevalence seen in our migrant VL cases was substantially lower than in previous reports , reflecting a parallel decrease in HIV prevalence at national level and at KAH ( 2010 , 7 . 5%; 2011 , 6 . 5%; 2012 , 8 . 2% ) , as well as the decentralization of ART services in the region . Risk associated with nocturnal outdoor exposure was also seen in our earlier study in Libo Kem Kem [16] . In the current study , the constellation of factors associated with elevated risk suggests that sand fly feeding occurred predominantly outside of houses , or in thatch-walled houses that are highly vulnerable to sand fly entry and resting . Based on sand fly trapping data , P . orientalis has generally been considered exophilic and exophagic [8] , [9] . However , in a study in villages with apparent domestic VL transmission in eastern Sudan , 88% of flies were trapped inside houses or grain storerooms , raising the possibility that feeding may occur outdoors or indoors depending on local housing and environmental conditions [9] , [17] . In our study , sleeping under an acacia tree during the day or at night conferred 4- to 7-fold increased risk of VL . Acacia trees appear to have a special relationship with P . orientalis , with high sand fly densities found in association with acacia-balanites forest [8] , [9] . Experts hypothesize that this may be due to the microclimate around acacia thickets and/or the presence of specific sugar meal sources such as the balanites fruit or the secretions of aphids and coccids found in these settings [9] . We found no association with animal ownership in our resident analysis , in contrast to findings in Libo where dog ownership was linked to increased risk and Sudan where both dogs and cattle appeared as risk factors for VL [16] , [18] . In univariate analyses , we found increased risk for migrants who reported sleeping near cattle , in contrast to the protective association found for proximity to cattle in studies in Kenya and Bangladesh [19] , [20] . Nevertheless , in the migrant multivariable model , there was no significant risk associated with sleeping near cattle while sleeping near dogs was associated with nearly 7-fold increased risk . Whether this represents a direct risk from infected dogs , or whether this variable represents a proxy for other outdoor exposures such as increased vector density , is unclear . Although transmission is thought to be predominantly anthroponotic in these settings , infected dogs have been found by serology or PCR in several studies in Ethiopia as well as Sudan [2] , [3] , [21] . However , the role of the dog as an epidemiologically important infection reservoir host in this area is not yet clear . The two studies we performed used different recruitment methods for logistical reasons , and this difference may have affected characteristics of the control groups , in particular . The use of village controls , as in the resident study , is generally considered optimal in terms of comparability of case and control groups . We chose resident controls with no history of kala-azar in the household in the previous 5 years , because of the strong household clustering seen in VL , the fact that many of the factors evaluated are household-level variables , and the high likelihood that household members of kala-azar cases might have undetected subclinical VL infection [19] , [22] , [23] . Thus , inclusion of controls from affected households could bias results for household-level variables toward the null hypothesis . At the same time , exclusion of these households could lead to a control group with lower likelihood of VL and bias results for individual-level variables in the opposite direction . A solution to this dilemma would be to perform serology and leishmanin skin testing on all potential controls , allowing separate analyses of VL disease and infection [22] . However , the requirement for extra manpower , resources and time precluded this option . There was no practical way to recruit appropriate community controls for the migrant study . Most migrants were sleeping in temporary shelters that moved along with the farm work . Although the use of hospital-based controls may have introduced some biases with respect to exposures , these factors could bias either toward the null hypothesis ( e . g . if those who are ill are more likely to share risk factors with the case group ) or away from it ( if ill persons are more likely to use personal protective measures ) . Our inability to include existing HIV data in the resident analysis due to ethics committee-imposed restrictions constituted a further limitation . Nevertheless , the consistency of our findings between the two studies , as well as their agreement with many of the risk factors expected based on the known transmission characteristics of VL in this region , support the validity of our findings . Finally , in our studies as in many others , VL risk was strongly associated with poverty and factors linked to poverty [24] . Although the details of the variables differed between residents and migrants , low household income , poorer dietary options and lower educational attainment were all strongly associated with higher risk . The dietary variables in the migrant study are of particular interest . Ethiopians , especially those from the highlands , prefer injera over other staple foods; eating porridge made from sorghum was associated with almost 5-fold increased risk of VL , but it is unclear whether this was due to lower nutritional value or simply because eating porridge is a potent indicator of poverty . Eating meat at least once per month was associated with significantly lower risk , possibly because , in the setting of nutritionally impoverished diets , even meager consumption of meat translates into slightly better micronutrient status and lower risk of progression to clinical disease [22] . Although VL rapid tests and treatment drugs are provided free of charge , other hospital costs are not . Health care access is impeded for migrant workers by the need for a supporting letter from their administrative office of origin in order to access free services from the hospital . This leads to delays in diagnosis and treatment with poor outcomes . The extreme vulnerability , especially of migrant workers , must be taken into account when designing and implementing control strategies . For example , strategies that depend on house spraying will have little impact when a significant proportion of transmission is outdoors and many migrants have no fixed abode . A better approach might be innovative bed net deployment that makes net use practical for those sleeping outside and moving from place to place [25] . Programs to protect migrant workers from VL will yield additional dividends if future introductions of the disease into non-endemic areas can be prevented .
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Visceral leishmaniasis is a lethal parasitic disease transmitted by sand flies . The largest focus of VL in Ethiopia is located in the lowland region bordering Sudan , where hundreds of thousands of agricultural workers migrate for work every year during the planting and harvest seasons . We conducted two parallel studies in residents and migrants to determine the living conditions and behaviors that put people at higher risk of VL risk . We found that sleeping under an acacia tree at night , indicators of poverty and lower educational status were associated with increased risk in both populations . Sleeping under a bed net was protective . For residents , living in a house with thatch walls and sleeping on the ground increased risk of VL . Among migrants , the risk associated with HIV status was borderline significant and sleeping near dogs was associated with increased risk . Preventive strategies should focus on ways to ensure net usage , especially among migrant workers without fixed shelters . More research is needed to understand migration patterns of seasonal labourers and vector behavior .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[] |
2013
|
Risk Factors for Visceral Leishmaniasis among Residents and Migrants in Kafta-Humera, Ethiopia
|
Planar cell polarity ( PCP ) is the mechanism by which cells orient themselves in the plane of an epithelium or during directed cell migration , and is regulated by a highly conserved signalling pathway . Mutations in the PCP gene Vangl2 , as well as in other key components of the pathway , cause a spectrum of cardiac outflow tract defects . However , it is unclear why cells within the mesodermal heart tissue require PCP signalling . Using a new conditionally floxed allele we show that Vangl2 is required solely within the second heart field ( SHF ) to direct normal outflow tract lengthening , a process that is required for septation and normal alignment of the aorta and pulmonary trunk with the ventricular chambers . Analysis of a range of markers of polarised epithelial tissues showed that in the normal heart , undifferentiated SHF cells move from the dorsal pericardial wall into the distal outflow tract where they acquire an epithelial phenotype , before moving proximally where they differentiate into cardiomyocytes . Thus there is a transition zone in the distal outflow tract where SHF cells become more polarised , turn off progenitor markers and start to differentiate to cardiomyocytes . Membrane-bound Vangl2 marks the proximal extent of this transition zone and in the absence of Vangl2 , the SHF-derived cells are abnormally polarised and disorganised . The consequent thickening , rather than lengthening , of the outflow wall leads to a shortened outflow tract . Premature down regulation of the SHF-progenitor marker Isl1 in the mutants , and accompanied premature differentiation to cardiomyocytes , suggests that the organisation of the cells within the transition zone is important for maintaining the undifferentiated phenotype . Thus , Vangl2-regulated polarisation and subsequent acquisition of an epithelial phenotype is essential to lengthen the tubular outflow vessel , a process that is essential for on-going cardiac morphogenesis .
Malformations affecting the outflow of the heart are a major cause of morbidity and mortality in childhood . While many of these malformations occur sporadically , studies of families with congenital heart defects , alongside animal studies , have revealed that phenotypically discrete heart malformations can have diverse causes . These can involve disruption of a number of different genes , embryonic lineages or developmental processes . Furthermore , dissimilar malformations , including double outlet right ventricle , common arterial trunk , and tetralogy of Fallot , may appear in offspring sharing the same genetic defect and can therefore be considered within a spectrum of malformation with similar underlying causes [1] . Clarifying the fundamental processes that underpin cardiovascular development is essential to understand this complexity . The primitive heart tube is derived from the cardiac crescent , or first heart field , at embryonic day ( E ) 8 . 5 of mouse development . Subsequently , the second heart field ( SHF ) , which lies dorso-anteriorly to the primary cardiac crescent , adds cells to both the venous ( inflow ) and arterial ( outflow ) poles to lengthen the primitive heart tube [2] , [3] , [4] . The outflow tract develops as a bi-layered tube composed of an outer layer of myocardium , with an inner endocardial lining , both derived from the SHF [5] . This is connected proximally to the common ventricle and to the developing pharyngeal arch arteries at its distal end . Studies in chicken have shown that there is a focus of proliferative cells in the dorsal pericardial wall that act as a source of cells for both poles of the heart [6] . Moreover , these studies support the idea that the cells move into the outflow as an epithelial sheet , rather than as individually migrating cells . Although the precise morphogenetic mechanisms underpinning outflow development are still being elucidated , the targeted disruption of a number of genes within the SHF , including Isl1 , Fgf8 , Tbx1 , and Tbx20 , give rise to outflow tract malformations in mice ( reviewed in [7] ) . Furthermore , mutations in TBX1 [8] and common variants in ISL1 [9] have been found in human patients with outflow tract malformations , indicating a developmentally conserved role for the SHF during cardiac development . Detailed studies have shown that the transcriptional network involving Tbx1 and Isl1 is required to maintain SHF cells in a proliferative , progenitor-like state before they are added to the outflow tract [10]; in their absence the outflow tract is shortened . Following outflow tract septation , this results in mal-alignment of the aorta and pulmonary trunk with the ventricular chambers [5] , [11] . Despite insight into the transcriptional network that regulates the maintenance of SHF progenitors before they reach the poles of the heart , there is limited information about the characteristics and behaviour of the cells as they add to the outflow tract . Endocardial cushions form along the length of the developing outflow tract and by way of complex processes of cell migration , growth and remodelling , result in the separation of the initially single vessel into the aortic and pulmonary trunks ( reviewed in [12] ) . Neural crest cells ( NCC ) , originating in the cranial neural tube , migrate long distances into the pharyngeal arches and endocardial cushions and are involved in septation and alignment of the outflow vessels [13] , [14] , [15] . The processes regulating NCC migration have been studied in some detail , and at least in frogs and zebrafish , contact inhibition of locomotion , regulated at least in part by the planar cell polarity signalling pathway ( see below ) is implicated [16] , [17] . Thus , a variety of cell types and complex morphological processes contribute to the developing outflow tract . The planar cell polarity ( PCP ) pathway is a non-canonical Wnt pathway , which acts to regulate cell polarity within the plane of an epithelium . Studies in Drosophila wing , eye and abdomen have shown that polarity between adjacent cells is co-ordinated by the asymmetrical localisation of core PCP factors . Vangl2 ( Strabismus ) , Flamingo and Prickle accumulate proximally in polarised cells , while Dishevelled , Frizzled , Flamingo , and Diversin ( Diego ) accumulate distally [18] . PCP signalling has also been implicated in the regulation of apico-basal polarity , and directional cell migration [19] , [20] , [21] . Outflow tract malformations , including septation defects , are common in mice following the disruption of PCP genes ( reviewed in [22] ) , although the lineage requirement for PCP signalling in heart development remains unclear . Loop-tail ( Lp ) mice carry a mutation in the Vangl2 gene , encoding a key component of the PCP pathway . Lp/Lp mice display a number of malformations associated with disrupted PCP signalling , including misorientation of stereocilia in the cochlea and utricle [23] , [24] and craniorachischisis [25] . We have previously shown that Lp/Lp embryos have a spectrum of cardiac defects that affect the outflow region of the heart [26] , including double outlet right ventricle , common arterial trunk , abnormal patterning of the pharyngeal arches and ventricular septal defects . The mutant embryos also have abnormalities in the ventricular wall that include the coronary arteries [27] . Thus , the spectrum of defects seen in Lp/Lp mice could result from disruption of several cell types . Here we investigate the role of Vangl2 during outflow tract development using a novel tissue-specific knockout of the Vangl2 gene . We highlight a role for Vangl2 specifically within the SHF and show that Vangl2 is essential for forming the epithelial distal component of the elongating outflow vessel .
We have shown previously that Lp/Lp embryos present a number of cardiac anomalies [26] . To begin to characterise early heart formation in these mutants and as a prelude to lineage-based analyses , cardiac-specific markers were analysed by in situ hybridization , in order to determine whether the chambers formed properly . The expression patterns of the chamber markers Mlc2a , Mlc2v and Nppa , and outflow markers including Tbx20 , were examined in embryos from Lp litters at E10 . 5 . Whilst none of these markers showed reproducible expression differences between control and Lp/Lp embryos ( n = 2–3 for each gene examined ) , aberrant heart looping could clearly be seen in the latter; the outflow tract was shorter and the right ventricle was hypoplastic , although to a varying extent , in all Lp/Lp when compared with controls ( >50 embryos examined in total; Fig . 1 A , B , S1 Fig . ) . Transverse sections of Lp/Lp hearts at E14 . 5 revealed double outlet right ventricle ( Fig . 1 C , D ) as previously described [26] . To begin to establish the cell type that requires Vangl2 signalling for outflow tract development , we first crossed the Lp mice with the Wnt1-Cre line . However , no defects in NCC migration were observed in Lp/Lp embryos from E10 . 5 , with the distribution of Wnt1-Cre-positive cells indistinguishable from that of control littermates ( Fig . 1 E–H ) . Isl1 is expressed by all SHF progenitor cells and thus can be used for lineage tracing of the SHF [5] . We therefore asked whether Isl1-Cre-expressing SHF derivatives contribute normally to the heart in Lp/Lp embryos . Comparison with stage-matched controls revealed no abnormalities in the overall distribution of SHF-derivatives in the pharyngeal and cardiac regions of the Lp/Lp embryos ( Fig . 1 I–L ) . As development progresses , the continued expression of Isl1 protein is confined to non-differentiated SHF progenitors; it is down-regulated as they differentiate [5] . At E9 . 5 , Isl1-expressing cells localised to the mesenchyme of the dorsal wall of the aortic sac and the distal outflow tract ( Fig . 1M ) . Whilst Isl1 staining was broadly similar between wild type and Lp/Lp littermates , Isl1-expressing cells appeared disorganised in the distal outflow tract in the mutant embryos ( Fig . 1 compare N′ with M′ ) . This subtle anomaly was highly reproducible ( n = 10 ) . Together , these data suggested that there could be an abnormality in SHF-derived cells in the distal outflow tract . The gross morphological defects , including craniorachischisis and axial rotation defects , together with the loss of Vangl2 in all body cells , limit the use of Lp for studying the causes of the cardiac malformations . To clarify the role that Vangl2 plays during heart morphogenesis , we produced a floxed allele of Vangl2 , in which exon 4 , encoding the trans-membrane domains , is flanked by loxP sites ( Fig . 2A ) . A neomycin selection cassette , flanked by FRT sites , was placed downstream of exon 4 . Vangl2neo/neo mice , which retain the neomycin selection cassette within the construct , were hypomorphic for Vangl2 , with 2/3 displaying craniorachischisis and 1/3 spina bifida only ( S2 A–H Fig . ) . The neoR cassette was subsequently removed by crossing the Vangl2neo mice with FLPe mice , to give Vangl2flox mice . Recombination of the resulting construct following the expression of Cre is predicted to produce a premature stop codon that gives rise to a small 8 KDa protein , which lacks the four trans-membrane domains and C-terminal PDZ-binding domain required for interaction of Vangl2 with other proteins [28] , [29] . Vangl2flox mice were crossed with Sox2-Cre and PGK-Cre mice to produce embryos containing the truncated Vangl2 construct in all cells ( Fig . 2 B–K ) . Inter-crossing Vanglflox/+; Sox2-Cre or Vangl2flox/+; PGK-Cre mice with Lp/+ mice generated Vangl2flox/Lp; Sox2-Cre and Vangl2flox/Lp; PGK-Cre embryos that were indistinguishable from stage-matched Lp/Lp embryos , showing craniorachischisis and heart malformations ( n = 4; S2 I–P Fig . ) . Thus , Lp and the Vangl2 deletion allele failed to complement . We next asked whether global knockout of Vangl2 with Sox2-Cre recapitulated the Lp/Lp phenotype ( Fig . 3 A–H ) . Indeed , Vangl2flox/flox; Sox2-Cre embryos had a shortened body axis and craniorachischisis ( Fig . 3 compare G with C ) . Sectioning at E14 . 5 confirmed that the Vangl2flox/flox; Sox2-Cre embryos had heart malformations , including double outlet right ventricle , ventricular septal defects and pharyngeal arch remodelling defects , as are seen in Lp/Lp embryos ( Fig . 3H and S3 A , B Fig . compare with Fig . 3D and [26] ) . Similar results were obtained using the PGK-Cre line in place of Sox2-Cre ( S3 E–H Fig . ) . Thus , our novel Vangl2flox allele , when knocked out globally , recapitulated the phenotypes observed in Lp/Lp mutants and consequently was a potentially useful tool for genetically dissecting the Lp/Lp phenotype . In order to determine the tissue-specific requirement for Vangl2 during heart development , we used a number of lineage-specific Cre driver lines to delete Vangl2 in a targeted manner . Although our studies in Lp/Lp had suggested that NCC deficiency was unlikely to be the cause of the outflow defects , we could not rule out more subtle defects in their function . Therefore , to exclude the possibility that Vangl2 is required in NCC for outflow tract development , we inter-crossed Vangl2flox mice with the Wnt1-Cre line . Vangl2flox/flox; Wnt1-Cre embryos were indistinguishable from control littermates with both a normal external appearance and normal hearts at E14 . 5 ( n = 6; Fig . 4 A , B , E , F ) . Moreover , Vangl2flox/flox; Wnt1-Cre animals ( n = 3 ) were viable and indistinguishable from their control littermates at 28 days after birth . Indeed , close analysis of the expression pattern of Wnt1-Cre compared with that of Vangl2 suggested that Vangl2 is not expressed by NCC , and that there was no change in the expression pattern of Vangl2 in the Vangl2flox/flox; Wnt1-Cre embryos at least at the stages when NCC are migrating into the heart ( S4 Fig . ) . Thus , Vangl2 does not appear to be required by NCC for normal development of the outflow tract of the heart . In order to test directly our hypothesis that Vangl2 is required in the SHF , we inter-crossed the Vangl2flox mice with Isl1-Cre and confirmed loss of Vangl2 in the outflow tract by immunofluorescence at E9 . 5 ( S5 A–I Fig . ) . In contrast , Vangl2 expression was maintained outside the Isl1-Cre expression domain ( S5I Fig . ) . While externally , the E14 . 5 Vangl2flox/flox; Isl1-Cre embryos were indistinguishable from their control littermates ( Fig . 4 C , G ) , histological sectioning revealed double outlet right ventricle in 14/15 of the mutant embryos . Moreover , they all had a sub-aortic ventricular septal defect ( Fig . 4 D , H and S5 N–S Fig . ) . An abnormality in the myocardialisation of the outflow cushions was also observed in Vangl2flox/flox; Isl1-Cre embryos ( S6 Fig . ) , as was seen in Lp/Lp [30] . However , there were no abnormalities in pharyngeal arch patterning or the ventricular wall ( S5 Fig . ) . Subsequent analysis at earlier stages ( E9 . 5–E10 . 5 ) showed that the embryos had a markedly shortened outflow tract ( S5 J–M Fig . ) . These data exclude the possibility that the outflow tract anomalies are secondary to the gross abnormalities in body patterning seen in Lp/Lp . They do , however , support the idea that Vangl2 plays a specific role in the SHF . We asked whether the role of Vangl2 is restricted to the SHF or might play a more general role in cardiac progenitor populations . To test this idea , we crossed the Vangl2flox mice with Nkx2 . 5-Cre mice; Nkx2 . 5-Cre is expressed throughout the primitive heart tube but also in the cells derived from the SHF [31] , [32] . Surprisingly , analysis of Vangl2flox/flox; Nkx2 . 5-Cre embryos at E15 . 5 revealed no obvious structural cardiovascular abnormalities ( n = 4; Fig . 4 I , J , M , N ) . Our analysis of the Nkx2 . 5-Cre expression pattern largely confirmed previous reports , although we observed patchy expression in the outflow tract , compared with much broader and higher level expression in the left ventricle and atria ( S7 Fig . ) . Moreover , Vangl2 was maintained in the distal outflow of Vangl2flox/flox; Nkx2 . 5-Cre embryos at E9 . 0 ( S7 Fig . ) . This suggests that Nkx2 . 5-Cre , at least in our hands , may not be driving high enough levels of Cre to fully delete Vangl2 in the outflow tract . Finally , as we had previously shown abnormalities in the outflow tract myocardium in Lp mutants [30] , and because Isl1-Cre also drives expression in endocardial cells in the outflow tract ( Fig . 5B ) , we wanted to establish whether Vangl2 is required in differentiated cardiomyocytes or the endocardium . To investigate this , we inter-crossed the Vangl2flox mice with Mlc2v-Cre mice , to drive Cre in the outflow and ventricular myocardium . At E9 . 5–E10 . 5 , Mlc2v-Cre was not expressed in the outflow tract myocardium although it was found in this tissue by E12 . 5 ( S6 C , D Fig . ) . Deletion of Vangl2 in the Mlc2v-Cre expression domain resulted in embryos with a normal outflow ( n = 6; Fig . 4 K , L , O , P ) . Thus , although Vangl2 expression is maintained in the outflow myocardium at E12 . 5 ( S6 E , F Fig . ) , this is not required for outflow tract lengthening and alignment of the the great arteries with the ventricles . Intercrossing of Vangl2flox mice with Tie2-Cre mice , to selectively knock out Vangl2 in the endocardium , also resulted in mice with normal outflow tract development ( S8 Fig . ) . Thus , these data support the idea that Vangl2 is required in undifferentiated SHF cells , rather than playing later roles in outflow tract remodelling . Having established that Vangl2 is required in undifferentiated outflow tract precursors ( expressing Isl1 ) , derived from the SHF , we next investigated its role in this population . Normally , undifferentiated SHF cells move from the mesothelial dorsal pericardial wall into the distal outflow tract where they begin to differentiate into cardiomyocytes . Thus , we defined a transition zone in the distal outflow tract where SHF-derived cells lose their progenitor phenotype and differentiate ( Fig . 5A ) . We hypothesised that Vangl2 may be playing a key role in this region , regulating this transition from the progenitor to differentiated phenotype . We focussed our studies at E9 . 0–E9 . 5 , as this is the period just before ( E9 . 0; approximately 17 somite pairs ) and as the first abnormalities in the outflow tract wall become apparent ( at E9 . 5; approximately 23 somite pairs ) . Vangl2 protein , shown by immunofluorescence , was present in all the cells of the dorsal pericardial and distal outflow tract walls , into the differentiated myocardium , and also in the endocardium ( Fig . 5 B–F ) . However , the spatial distribution of Vangl2 was not uniform throughout this region at E9 . 0–E9 . 5 . Within the dorsal pericardial and the most distal outflow tract walls , Vangl2 was enriched in the basal cell membrane , as shown by its colocalisation with β-catenin ( Fig . 5 B , C , E ) . Further proximally however , Vangl2 was seen diffusely throughout the cytoplasm ( Fig . 5 B , D , F ) . As mutations that lead to loss of membrane localisation disrupt Vangl2 function and result in the Lp/Lp phenotype [33] , this suggests that Vangl2 is likely to be playing its major roles in the dorsal pericardial wall or distal outflow tract . To determine at which stage SHF cells change their expression profile from that of a progenitor to a differentiated cardiomyocyte , relative to the distribution of Vangl2 , we analysed the expression of cardiac troponin I , which is expressed in differentiated cardiomyocytes ( Fig . 5 G–J ) . At E9 . 0 , cardiac troponin I immuno-reactivity was found at low levels in the most distal outflow tract wall , but became increasingly abundant proximally as the cells differentiated into cardiomyocytes ( Fig . 5I ) . At the same stage , all cells in the dorsal pericardial wall and in the transition zone expressed Isl1 protein , localised within the nucleus . However , nuclear-localised Isl1 was abruptly lost in more proximal cells , where Vangl2 became cytoplasmic ( Fig . 5 K–N ) . Thus , whereas all three markers overlapped in the most distal outflow tract , loss of membrane-associated Vangl2 corresponded to the loss of nuclear Isl1 , defining the proximal boundary of the transition zone where the cells had become differentiated cardiomyocytes . We next wanted to clarify how Vangl2 , as a PCP protein that would normally act within the plane of an epithelium , is functioning in the distal outflow tract . We first investigated the distribution of the adherens junction protein β-catenin , which marks the baso-lateral compartment of epithelial cells , together with the extracellular matrix protein laminin , which is found associated with the basal lamina of epithelial cell layers . At E9 . 0 , the distal outflow wall appeared less organised in Vangl2flox/flox; Isl1-Cre embryos than their control littermates , with reduced staining of β-catenin and laminin ( Fig . 6 A–H ) . This was before outflow shortening was apparent in the mutant embryos . By E9 . 5 , whereas the transition zone had the phenotype of an organised pseudo-stratified epithelium in the controls , the region was markedly disorganised and was becoming thickened in the mutant embryos ( Fig . 6 P , R , compare with L , Q , and S5 K , M Fig ) . β-catenin was lost from the lateral walls of cells of the transition zone in the mutants ( Fig . 6N , compare with J ) , and laminin was absent in some areas of the mutant transition zone and was no longer basally restricted in others ( n = 3; Fig . 6P , compare with L ) . A similar abnormality in the distribution of fibronectin was also observed in the Vangl2flox/flox; Isl1-Cre embryos ( S9 Fig . ) . Interestingly , laminin was always basally distributed more proximally in the Vangl2flox/flox; Isl1-Cre outflow tract , where the wall is composed of differentiated cardiomyocytes ( S9 Fig . ) . These findings were highly reproducible . Thus , whereas the cells of the transition zone in the control distal outflow tract had the appearance of a pseudo-stratified epithelium and had polarised expression of epithelial markers , the epithelium was disorganised and thickened in the Vangl2flox/flox; Isl1-Cre embryos . Moreover , polarised expression of markers was disrupted . In contrast , comparable analyses of Vangl2flox/flox; Nkx2 . 5-Cre outflow tract showed only a mild phenotype in the mutant embryos ( S9 Fig . ) , supporting the lack of structural outflow tract abnormalities in this cross . As asymmetrical cell division is regulated by Vangl2 in some tissues [34] we asked whether disruption of this process might account for the disorganised and thickened epithelium of the transition zone in the mutant embryos . However , we found little or no cell division in the distal outflow tract at E9 . 0–E9 . 5 , as suggested previously [6] , indicating that this is unlikely to be the mechanism underlying the disorganisation observed ( S10 Fig . ) . In order to investigate the possibility of disrupted polarity in more detail , we immuno-stained the microtubule organising centre ( MTOC; recognised by γ-tubulin ) which is polarised in epithelial cells and localised to the apical side of the cell in the transition zone of control embryos ( Fig . 6 Q , S ) . In contrast , this was much more variable in the transition zone cells of Vangl2flox/flox; Isl1-Cre embryos ( Fig . 6 R , T ) , with MTOCs commonly found on the basal side of the cell layer . Statistical analysis ( Chi Square ) showed this difference was highly significant ( p<0 . 001 ) . This suggests that PCP and/or apical-basal polarity is disrupted in the distal outflow wall in the absence of Vangl2 . We studied the phenotype of the SHF-derived cells more closely as they moved from the mesothelial dorsal pericardial wall into the outflow tract , by looking at other markers associated with polarised epithelia that are expressed in an apical-basal polsarised manner . E-cadherin , an adherens junction-associated protein of epithelial cells was expressed only at low levels in the dorsal pericardial wall , while a related junctional protein , N-cadherin , was not detected in this tissue ( Fig . 7 A–D ) , supporting the idea that this is not a typical epithelium . Within the pseudo-stratified epithelium of the distal outflow tract however , both of these proteins were expressed in a polarised manner , being enriched at the apical-basal boundary ( Fig . 7 E , G , L , M ) . This suggests that as cells move into the distal outflow tract they take on a more overt epithelial phenotype . As in control embryos , E-cadherin expression was up-regulated in the distal outflow of Vangl2flox/flox; Isl1-Cre mutants at E9 . 0–E9 . 5 , in comparison to the dorsal pericardial wall . However , it was mislocalised with staining found throughout the baso-lateral compartment of the cells ( Fig . 7 E , G , H , J and S11 Fig . ) . The subcellular localisation of N-cadherin expression was also perturbed in the distal outflow tract of Vangl2flox/flox; Isl1-Cre embryos , with loss of enrichment at the apical-basal boundary ( Fig . 7 L , M , O , P ) . We analysed other proteins known to be compartmentally restricted in epithelial cells . Analysis of PKCζ in the distal outflow tract revealed that whilst the protein was markedly enriched in the apical compartment of cells in the transition zone of control embryos , this apical enrichment was lost in Vangl2flox/flox; Isl1-Cre embryos ( Fig . 7 F , G , I , J and S11 Fig . ) . Scrib has been implicated in regulating epithelial cell adhesion and has been shown to physically interact with Vangl2 in epithelial cells [35] , although it does not do so in the early myocardium [36] . Scrib was localised to the baso-lateral compartment of cells within the distal outflow tract wall in control embryos , but was enriched at the apical-basal boundary . In Vangl2flox/flox; Isl1-Cre embryos , Scrib was no-longer enriched at the apical-basal boundary ( arrows in Fig . 7 K , M , N , P ) . This abnormality was restricted to the distal outflow tract , with normal Scrib expression seen in the dorsal pericardial wall and pharyngeal endoderm although both of these tissues also express Isl1-Cre ( S11 Fig . ) , and in epidermis , which does not express Isl1-Cre . Thus , deletion of Vangl2 from SHF cells results in the disruption of both PCP and apical-basal polarisation and loss of epithelial phenotype in the distal outflow tract wall . We lastly wanted to establish how the disruption of polarity and loss of epithelial phenotype impacted on the fate of the cells as they move through the distal outflow walls . We investigated whether the expression of either cardiomyocyte differentiation markers or Isl1 was altered in the distal outflow wall of Vangl2flox/flox; Isl1-Cre mutants at E9 . 0–E10 . 5 . At E9 . 0 we observed that cardiac troponin I was expressed initially at very low levels within the distal outflow wall of control embryos , becoming progressively more strongly expressed proximally ( as shown in Fig . 5 ) . In contrast , high-level expression appeared more distally in Vangl2flox/flox; Isl1-Cre littermates ( Fig . 8 A , D ) . Desmin , which is expressed at elevated levels by cardiomyocytes , but is also found at lower levels in smooth muscle cells at this stage , was also expressed at higher levels more distally in the mutant embryos ( Fig . 8 B , E ) . As expected , we observed a similar disorganisation of Isl1-expressing cells in the distal outflow of Vangl2flox/flox; Isl1-Cre as was observed in Lp/Lp embryos ( Fig . 8 C , F ) . However , it was striking that nuclear Isl1 localisation was lost more distally in the distal outflow wall of Vangl2flox/flox; Isl1-Cre than in control embryos . Notably there was a significant amount of non-nuclear Isl1 observed in cells in control and mutant outflow walls ( Fig . 8 C , F ) . To confirm the findings of premature differentiation in the distal outflow tract of Vangl2flox/flox; Isl1-Cre , we examined the expression of other differentiation markers at E10 . 5 . Similar results to those observed for cardiac troponin I were found using an antibody to myosin heavy chain ( MF20; Fig . 8 H , J ) . Moreover , when we examined the expression of αSMA , which at these stages labels both smooth muscle cells and differentiating cardiomyocytes , it was also expressed at high levels more distally in the mutant embryos than in control littermates ( Fig . 8 G , I ) . Thus , Vangl2flox/flox; Isl1-Cre cells lose their progenitor phenotype and differentiate earlier than those of control embryos . Together , our data suggest that Vangl2 is required to establish the epithelial phenotype of cells in the distal outflow tract wall and that this epithelialisation is required to maintain SHF-derived cells in an organised , polarised state which maximises outflow tract lengthening . This allows normal alignment of the great arteries with the ventricular chambers
The spontaneously occurring Lp mutant has been used to study the role of Vangl2 in cardiac development [26] , [27] , [30] . However , the gross morphological defects that are characteristic of Lp/Lp , including craniorachischisis and incomplete axial rotation , limit its use in dissecting out the role of Vangl2 during heart morphogenesis . We originally suggested that the cardiac looping defects observed in the Lp/Lp may be secondary to the severe neural tube closure and axial turning defects [26] . Indeed , the aortic arch abnormalities that are highly penetrant in Lp/Lp and in the Vangl2flox/flox; Sox2-Cre and Vangl2flox/flox; PGK-Cre mutants described here , may be secondary to these gross abnormalities in body form , as they are only observed in the presence of craniorachischisis . In contrast , we show that the outflow tract anomalies are primary defects . The complexity added by the presence of other embryonic defects , together with the contributions of a number of different cell populations to the developing outflow tract , makes analysis of a global loss of Vangl2 inadequate for the purpose of establishing its precise role in heart development . Here we have described the generation of a conditionally targeted deletion of Vangl2 , which when expressed globally recapitulates the outflow anomalies observed in Lp/Lp . Using this model , we have shown that the expression of Vangl2 in the Isl1-Cre expressing SHF is essential for the normal development of the outflow tract . Our data suggests that it is the expression of Vangl2 in the undifferentiated SHF ( Isl1-Cre expressing ) population of the distal outflow tract that is critical for outflow tract development , as its deletion in the Mlc2v-Cre-expressing outflow myocardium and Tie2-Cre-expressing endothelium led to normal outflow tract development . However , as well as finding outflow tract shortening as early as E9 . 5 , abnormalities in movement of muscle cells from the outflow tract wall into the cushions ( the process of myocardialisation ) was also found in the Vangl2flox/flox; Isl1-Cre embryos at E13 . 5 , as in Lp/Lp embryos [30] , showing that the outflow myocardium does not recover from its early abnormalities . In the distal outflow tract wall , cells transition from an undifferentiated form ( expressing nuclear Isl1 protein ) to differentiated myocardium ( with a consequent loss of nuclear Isl1 and upregulation of striated muscle markers including myosin heavy chain and cardiac troponin I ) . Intriguingly , Vangl2 is lost from the cell membrane and is localised to the cytosol at the proximal boundary of the transition zone , where the cells differentiate to myocardium . Thus , this switch from membrane to cytoplasmic Vangl2 , together with the loss of nuclear Isl1 , defines the proximal boundary of the transition zone . There is good evidence to suggest that Vangl2 is active when it is membrane-associated , as mutations that block membrane-association block function [33] . Thus , we suggest that it is the membrane-localisation of Vangl2 in the transition zone that imparts planar polarity on the cells , maintaining their epithelial phenotype . When this membrane-association is lost , the cells lose their typical epithelial appearance and differentiate . Although Vangl1 , a close homologue of Vangl2 is expressed in the early embryo , its expression domain is more restricted than that of Vangl2 [37] , [38] . Vangl1 mutants do not have an outflow tract phenotype [37] . Thus , it seems likely that Vangl2 is the principal Vangl gene acting in the early heart . We did not see an outflow tract phenotype when we inter-crossed the Vangl2flox mice with Nkx2 . 5-Cre line . However , analysis of the Nkx2 . 5-Cre expression domain showed that although it was expressed early enough to delete Vangl2 in cardiomyocyte progenitors [31] , its expression was patchy in the distal outflow in our hands , and Vangl2 expression was maintained in these cells . Thus , these data are consistent with the idea that Vangl2 is required in undifferentiated cells , rather than differentiated derivatives of the SHF . During outflow tract elongation in chickens , cells move from a proliferative pool within the dorsal pericardial wall into the distal outflow not as individual cells , but as a cohesive sheet [6] . This is similar to the movements of epithelial sheets in other organ systems [39] , [40] . Although this mechanism of addition of cells to the outflow has not been confirmed in mammals , it seems likely that an analogous process takes place . Once they reach the region of the distal outflow , our data suggests the SHF-derived cells then acquire a more epithelial phenotype ( i . e . robust polarised expression of E-cadherin ) than they exhibited while they were in the mesothelium of the dorsal pericardial wall . Within this newly epithelialized tissue , Vangl2 signalling regulates the apical-basal polarised expression of a range of markers of stratified epithelial tissues , including Scrib and PKCζ [41] , [42] . We see markedly abnormal distribution of these markers in the distal outflow tract wall of Vangl2flox/flox; Isl1-Cre embryos , with a reduced apical domain and expanded basolateral domain . Moreover , analysis of the position of MTOCs in the cells of the distal outflow tract suggests that planar polarisation of cells is also disturbed within this tissue . Thus , both apical-basal and planar cell polarity appear to be regulated by Vangl2 within the early outflow tract ( Fig . 8 K , L ) . We propose that the polarisation of the cells within the distal outflow tract and the consequent acquisition of epithelial phenotype is linked to the elongation of the outflow vessel . Specifically , we propose that the organisation of the cells into a pseudo-stratified epithelium ensures that the outflow tract wall lengthens rather than thickens . Epithelia are characterised by cells with strong lateral attachments ( including E-cadherin-containing adherens junctions ) that keep the cells in a sheet . Our data suggests that Vangl2 plays a key role in regulating planar polarity in the distal outflow wall , such that the cells are similarly oriented within the plane of the epithelium . When Vangl2 is lost from these cells in the distal outflow wall , cells are rotated relative to their neighbours , resulting in mis-positioning of the junctional complexes and disruption of the characteristic epithelial phenotype of the tissue ( see Fig . 8 K-M for simplified cartoon ) . Our observation that Vangl2 also appears to be regulating the apical-basal positioning of markers , may add to this phenotype by further disrupting the positioning of cell junctions . Thus , Vangl2 appears to be essential for forming/maintaining the sheet-like structure of the cells that is necessary for epithelial tube formation . The thickened , shortened outflow tract seen in the absence of Vangl2 in some ways resembles the consequences of disrupted PCP signalling at gastrulation in vertebrate embryos , when convergence and extension movements narrow and lengthen the developing embryo ( reviewed in [18] ) . Both processes likely involve junctional remodelling [40] , however , the similarities in final-phenotype may be misleading as the SHF cells are added to the distal end of the lengthening outflow tract , whereas convergence and extension gastrulation movements occur intrinsically within a fixed pool of cells . Indeed , the process we describe does not seen to be directly analogous to any other mammalian organ system so far described . However , there are similarities with the described roles for PCP genes in tracheal tube lengthening in Drosophila [43] and Vang1 ( the only worm vang/strabismus gene ) specifically in C . elegans intestinal tube formation [44] . In both these cases , disruption of PCP signalling results in disorganisation of polarised markers and cell-cell relationships , and a shortened epithelial tube . During the early development of the outflow tract , undifferentiated SHF cells , expressing Isl1 , move from the mesothelium of the dorsal pericardial wall into the outflow tract before differentiating to cardiomyocytes . This process happens prematurely in the Vangl2flox/flox; Is1-Cre mutants . Thus , the acquisition of the organised epithelial phenotype may be important both physically to create length within the tubular structure of the distal outflow , but also to prevent premature differentiation of the cells . Whether the latter contributes directly to the phenotype remains unclear . Interestingly , Wnt5a , an activator of PCP signalling , has been shown to be regulated by Tbx1 [45] , one of the key transcription factors involved in maintaining SHF cells in a proliferative , undifferentiated state [10] . Thus , Vangl2 may be acting with these other factors in a network that regulates the addition of SHF cells to the lengthening outflow tract . The link between a shortened outflow tract and double outlet right ventricle is well recognised [46] , [47] . The on-going , regulated addition of cells from the SHF is crucial for the elongation of the vessel , a process that is required to position the proximal regions of the vessels so that they can align appropriately with the ventricular chambers . In the absence of Vangl2 , the outflow tract thickens rather than lengthens . This shortened , thicker outflow tract is unable to align correctly with the ventricular chambers , resulting in double outlet right ventricle and ventricular septal defects ( Fig . 8M ) . Whether this mechanism could explain cardiac alignment defects such as double outlet right ventricle in humans is the subject of on-going studies . Disruption of Vangl2 , either throughout the body or just in the SHF lineage , results in double outlet right ventricle and ventricular septal defects . Notably , we saw no abnormalities when Vangl2 was deleted in NCC , despite PCP signalling having been shown to play crucial roles in NCC migration in frogs and zebrafish [17] . Indeed , a recent study has shown that Vangl signalling is dispensable for NCC migration in mice [38] , suggesting that NCC migration is regulated differently in mammals than in frogs , fish and birds . Similar outflow malformations to those we observe when Vangl2 is deleted specifically in the SHF lineage are observed in mice carrying mutations in other PCP genes including Dvl 1-3 , Wnt5a , Wnt11 and Fz1/Fz2 [48] , [49] , [50] , [51] , [52] . Whilst there are likely to be multiple causes of double outlet right ventricle , the strong relationship between this abnormality and mutations in PCP pathway genes suggests that PCP signalling may be fundamental to the normal septation and alignment of the great arteries with the ventricular chambers . As well as ultimately developing double outlet right ventricle , Wnt11-/- mutants display abnormalities at earlier stages of heart development . These include a reduction in outflow tract length and perturbation in the cytoarchitecture of outflow tract cardiomyocytes [51] . TGFβ2 signalling was shown to be acting downstream of Wnt11 in the outflow myocardium , and Wnt11 null and Tgfβ2 null embryos showed abnormalities in apical-basal markers in the outflow wall at E11 . 5 [51] . Although earlier stages were not analysed , and thus a direct comparison with our study cannot be made , it is possible that TGFβ2 , acting downstream of Wnt11 and Vangl2 , might be involved in maintaining organisation of the distal outflow tract wall and thus regulate outflow tract lengthening . More recently , Sinha et al , [53] studied the abnormalities in outflow tract morphogenesis in Dvl2 mutants . They concluded that the defects resulted from abnormalities in the incorporation of SHF progenitors from the splanchnic mesoderm into the dorsal pericardial wall , prior to movement into the outflow vessel . Vangl2 is expressed in the dorsal pericardial wall , however despite close examination , we saw no abnormalities in this area . Sinha et al ( 2012; [53] ) did not describe the phenotype of the cells within the distal outflow tract in the Dvl2 mutants , or examine markers of polarised cells in their embryos . Thus it is unclear whether the mechanism we describe could also be a component of the Dvl2 mutant phenotype . However , Wnt11 is expression is restricted to the outflow tract myocardium [51] suggesting that this might be a key factor in activating PCP and thereby Vangl2 signalling in the distal outflow tract . Thus , it seems likely that PCP signalling , via Vangl2 and Wnt11 ( and/or Wnt5a ) , is playing an essential role in elongating the distal outflow tract , facilitating on-going cardiac morphogenesis .
All animals were maintained and killed according to the requirements of the Animals ( Scientific Procedures ) Act 1986 of the UK Government . This work was approved by the Newcastle University Ethical Review Committee and conformed to Directive 2010/63/EU of the European Parliament . Loop-tail mice from the LPT/Le inbred strain were originally obtained from Professor Andrew Copp ( UCL , London ) . Vangl2floxneo mice were created in partnership with Ozgene ( Australia ) . The mice were subsequently crossed with FlpE [54] mice to generate Vangl2flox mice and then inter-crossed with ROSA-Stop-eYFP [55] mice to allow Cre-based lineage tracing . Cre driver lines , including Sox2-Cre [56] , PGK-Cre [57] , Isl1-Cre [58] , Wnt1-Cre [59] , Mlc2v-Cre [60] , and Nkx2 . 5-Cre [30] were all intercrossed with the Vangl2flox line and ROSA-Stop-eYFP mice . All mice were maintained on the C57Bl/6 background ( Charles River ) and were backcrossed for a minimum of three generations . For all experiments , transgenic mice were compared with their wild type and heterozygote littermates . Mice were bred and embryos collected according to standard protocols [32] . Vangl2flox mice were genotyped using genomic DNA isolated from ear clips or limb buds using primers: forward: CCGCTGGCTTTCCTGCTGCTG; reverse: TCCTCGCCATCCCACCCTCG . Embryos were dissected and fixed in 4% paraformaldehyde ( PFA ) in DEPC-PBS ( phosphate buffered saline ) overnight . The following day , the embryos were washed in PBS , dehydrated sequentially in 25% , 50% and 75% methanol in PBT ( 0 . 1% Tween 20 in PBS ) on ice , and then stored at −20°C in 100% methanol until use . When required , embryos were rehydrated through the reverse methanol series as above and then equilibrated in PBT . The embryos were bleached in 6% hydrogen peroxide in PBT for 1 hour to inactivate endogenous peroxidase in the embryos , and washed three times in PBT . To improve the penetration of the probe into the embryo , they were treated with proteinase K ( PK , 5 µg/ml ) at RT for 7 minutes ( in case of E10 . 5 embryos ) . Glycine ( 2 mg/ml ) was added to stop the PK activity and the embryos were gently rocked for 5 minutes . After two washes in PBT , the embryos were refixed in 0 . 2% glutaraldehyde in 4% PFA , and then rocked for 20 minutes . 1 ml of prehybridization solution was added to the embryos in a 2 ml tube and they were incubated at 70°C for 2 hours . After discarding the prehybridization solution , 500 µl of hybridisation solution including the DIG-labeled RNA probe ( 1 µg/ml ) was replaced and then incubated at 70°C overnight . The next day , the embryos were washed twice in salt solution I and II ( Solution I , 50% formamide , 5x SSC , pH 4 . 5 , 1% SDS; Solution II , 50% formamide , 2x SSC , pH 4 . 5 ) at 70°C for 30 minutes , respectively . Embryos were washed three times for 5 minutes at RT temperature in freshly made MABT and non-specific antigens blocked by incubating in a 10% blocking solution ( Roche ) in MABT for 1 hour . Anti-DIG antibody ( Roche ) was added to 1% blocking solution/MABT at a concentration of 1∶5000 and was left to pre-absorb at 4°C for 1 hour . 500 µl of the antibody solution was added to each embryo and they were left at 4°C for two nights with gentle rocking to allow complete penetration of the antibody . After this incubation , embryos were washed three times for 5 minutes in TBST then 5 times of 1 hour washes in TBST at RT . Embryos were then given 3 times of 10 minutes washes in NTMT to prepare them for development . Embryos were transferred to glass bottles and 3ml of NBT/BCIP ( 1 , 4-nitro blue tetrazolium chloride/5-bromo-4-chloro-3-indoyl-phosphate ) added to each at a concentration of 18 µl/ml of NTMT . Embryos were left to develop in the dark until the desired level of staining was achieved . Once the reaction was completed , the embryos were washed in PBT twice for 5 minutes , and stored in the dark in PBT containing 0 . 48 µg/ml of thymerisol to prevent fungal growth . Total RNA extraction from embryonic tissue was carried out using 1 ml Trizol-Reagent ( Ambion ) per sample , according to the manufacturer's instructions . Samples were quantified via spectrophotometry , and cDNA generated , using 1 µg of total RNA as a template , with superscript III reverse transcriptase ( Life technologies ) . PCR was carried out using the following primers: TGAGGGCCTCTTCATCTCC , ACCAATAACTCCACGGG . Following dissection at the appropriate stage of development , embryos were washed in PBS , and then fixed by immersion in 4% paraformaldehyde in PBS at 4°C for 1 to 3 nights dependent on their age . Embryos were subsequently dehydrated in ethanol and processed for wax sectioning . Sections were cut at 8 µm using a rotary microtome ( Leica ) . For haemotoxylin and eosin staining , slides were de-waxed with two 10 minute washes of Histoclear and were hydrated to water through an ethanol/H20 gradient ( 100% , 90% 70% and 50% ) . Slides were placed in Ehrlich haematoxylin ( RA Lamb ) for 10 minutes then were transferred into a trough of running tap water . The slides were left until the sections changed colour from purple to blue . The sections were differentiated by dipping in acid alcohol ( 1% HCl in 70% ethanol ) for 10–30 seconds , and then were placed back into tap water until the blue colour was restored . When an acceptable intensity of haemotoxylin stain was achieved , the slides were transferred into 1% aqueous eosin for 5 minutes , rinsed in tap water , then dehydrated through the same ethanol gradient , before washing twice in Histoclear and mounting in Histomount ( National Diagnostics ) . The Vangl2 antibody , produced by C . Dean , was raised in rabbit against the following Vangl2 specific peptide: CLAKKVSGFKVYSLGEENST by 21st Century Biochemicals , MA , USA and validated by western blot on lysate from HEK293 cells transfected with a Vangl2-GFP construct . A band representing the GFP-tagged construct was detected just above the 75KDa marker using either the Vangl2 antibody or an anti-GFP antibody . Slides were de-waxed with Histoclear and rehydrated through a series of ethanol washes . Following washes in PBS , antigen retrieval was performed by boiling slides in citrate buffer ( 0 . 01 mol/L ) for 10 minutes . Samples were blocked in 10% FCS and then incubated either overnight at 4°C , or at room temperature for 2 hours with the following antibodies: E-cadherin , β-catenin , N-Cadherin ( BD Transduction Laboratories ) , fibronectin , Scrib , PKCζ ( Santa Cruz ) , Isl1 , MF20 ( Developmental studies Hybridoma Bank , University of Iowa ) , GFP , alpha smooth muscle actin ( Abcam ) , gamma tubulin , laminin ( Sigma ) , cardiac troponin I ( HyTest ) , desmin ( Millipore ) . For immunofluorescence , samples were incubated at room temperature for two hours , with secondary antibodies conjugated to either Alexa 488 or Alexa 594 ( Life Technologies ) . Fluorescent slides were washed then mounted with Vectashield Mounting medium with DAPI ( Vector Labs ) . For non fluorescent staining , samples were incubated with biotinylated secondary antibodies for 1 hour , then with AB complex ( Vector labs ) for a further hour . Slides stained with DAB were washed then counter-stained with 5% methyl green . After dehydration in 100% butanol and Histoclear , slides were mounted using Histomount . Cells were stained with the γ–tubulin antibody to identify the microtubule organising centre ( MTOC ) . The orientation of the cell was defined by the angle between the most apical extent of the cell , the centre of the cell and the MTOC , and was measured using the angle tool in ImageJ . MTOCs lying proximally relative to the apex were considered to have an obtuse angle , and were transformed as such . Angles were converted to radians and plotted using the rose plot function in MATLAB . To analyse the distribution of MTOCs in control and mutant outflow tracts , eight sectors of possible MTOC cell position were defined ( 0–44° , 45–89°… 315–359° ) and the distribution of MTOCs within these sectors compared by Chi-Square ( IBM , SPSS statistics , version 21 ) . Cells with an active lacZ gene in embryos carrying both the Wnt1-Cre and ROSA26R constructs stain blue when treated with X-Gal . Embryos were washed twice in PBS and fixed in a solution containing 0 . 1 M phosphate buffer , 2% PFA , 5 mM EGTA ( pH 8 . 0 ) , 0 . 2% glutaraldehyde and 2 mM MgCl2 . They were washed twice in wash buffer ( 0 . 1 M phosphate buffer , 0 . 01% Na-deoxycholate , 0 . 02% Nonidet-P40 , 2 mM MgCl2 ) and X-Gal stained at 37°C wrapped in aluminium foil overnight . X-Gal solution contains 10 mM K-ferrocyanide , 10 mM K-ferricyanide and 1 mg/ml X-Gal in wash buffer . Stock X-Gal powder is dissolved in dimethylformamide before adding to the staining solution . The next day , after rinsing in PBS , the embryos were fixed in 4% PFA and embedded in wax as described above . Tissue samples from embryos were lysed with 500 µl 1X laemmli buffer ( 2% SDS , 5% betamercaptoethanol ) , 10% glycerol , 0 . 05% w/v bromophenol blue , 0 . 0625M Tris-HCl pH 6 . 8 ) and run on pre-cast 10% poly-acrylamide gels ( Biorad ) . Samples were transferred to PVDF ( ImmobilonP Millipore ) in ice cold transfer buffer ( 48 Mm Tris-HCl pH 8 , 39 mM glycine , 0 . 04% SDS , 20% methanol ) for 1 hour at 4°C . Membranes were washed in TBST ( 2 . 4% w/v Trizma hydrochloride , 8% w/v sodium chloride at pH 7 . 6; 0 . 1% Tween-20 ) then blocked in 5% milk/TBST at room temperature for 1 hour . The membrane was incubated with primary antibody ( Vangl2 1∶2500/GAPDH 1∶25000 in 5% milk/TBST ) overnight at 4°C , washed then incubated with secondary antibody ( 1∶2500 , Dako ) for 1 hour at room temperature . Membranes were washed then developed with ECL substrate ( SuperSignal West Dura , Thermo Scientific ) on Amersham Hyperfilm ECL ( GE healthcare ) . Images were manipulated in Photoshop CS3 ( Adobe ) . Diagrams were created using CorelDRAW X5 ( Corel ) .
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Congenital heart defects are common , affecting almost 1% of all live births . Many of these affect the outflow region , where the aorta and pulmonary trunk connect with the main ventricular chambers . Congenital heart defects arise from disruption of normal developmental processes and can be modelled in mice . Thus , studying normal development , together with mouse mutants that develop heart malformations , should shed light on why these common anomalies arise . We have studied cardiac development in a mouse mutant for the Vangl2 gene , a key component of the planar cell polarity ( PCP ) pathway . This pathway controls the orientations of cells in epithelia and during directional cell migration . Here , we show that PCP signalling is required by cells derived from the second heart field , which forms the outflow tract walls . We show that in the absence of Vangl2 , the cells within the distal outflow tract walls are non-polarised and disorganised . As a consequence the outflow tract is shortened and does not align properly with the ventricles . Thus , we show why disruption of a key PCP gene leads to outflow tract malformations . This is important for understanding heart development , but also more generally for understanding how PCP signalling regulates growth of tubular structures .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"biology",
"and",
"life",
"sciences",
"developmental",
"biology"
] |
2014
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Vangl2-Regulated Polarisation of Second Heart Field-Derived Cells Is Required for Outflow Tract Lengthening during Cardiac Development
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To ensure genomic integrity , the genome must be duplicated exactly once per cell cycle . Disruption of replication licensing mechanisms may lead to re-replication and genomic instability . Cdt1 , also known as Double-parked ( Dup ) in Drosophila , is a key regulator of the assembly of the pre-replicative complex ( pre-RC ) and its activity is strictly limited to G1 by multiple mechanisms including Cul4-Ddb1 mediated proteolysis and inhibition by geminin . We assayed the genomic consequences of disregulating the replication licensing mechanisms by RNAi depletion of geminin . We found that not all origins of replication were sensitive to geminin depletion and that heterochromatic sequences were preferentially re-replicated in the absence of licensing mechanisms . The preferential re-activation of heterochromatic origins of replication was unexpected because these are typically the last sequences to be duplicated in a normal cell cycle . We found that the re-replication of heterochromatin was regulated not at the level of pre-RC activation , but rather by the formation of the pre-RC . Unlike the global assembly of the pre-RC that occurs throughout the genome in G1 , in the absence of geminin , limited pre-RC assembly was restricted to the heterochromatin by elevated cyclin A-CDK activity . These results suggest that there are chromatin and cell cycle specific controls that regulate the re-assembly of the pre-RC outside of G1 .
The precise duplication of the genome is a fundamental requirement to maintain genomic integrity . Eukaryotic cells employ a tightly regulated origin licensing system to ensure that each origin of DNA replication is activated once and only once during S phase [1] . Failure to properly license replication origins will result in either re-initation of DNA replication or underreplication , and may contribute to genomic instability . A critical feature of this licensing system is that the assembly of the pre-replicative complex ( pre-RC ) at origins of replication is separated from replication initiation by strictly limiting each process to distinct phases of the cell cycle [1] , [2] . In G1 , when CDK levels are low , Cdc6 and Cdt1 function to load the replicative helicase complex ( MCMs ) at ORC binding sites to form the pre-RC . The pre-RC is subsequently activated in S phase by cyclin and Dbf4 dependent kinase ( CDK and DDK ) activities which results in the loading of DNA polymerases and the initiation of bi-directional DNA replication . Re-initiation of DNA replication within the same cell cycle is prevented by multiple redundant mechanisms that prevent the re-assembly of the pre-RC outside of G1 . For example , in S . cerevisiae and S . pombe , pre-RC re-assembly is prevented by CDK dependent activities which alter the phosphorylation and localization of Cdc6 and the MCM proteins [3]–[5] . In metazoans , the primary mechanism by which re-replication is prevented is the down regulation of Cdt1 activity by either the proteosome mediated degradation of Cdt1 or by the inhibitory binding of geminin to Cdt1 [6] . Geminin is present only in higher eukaryotes and its expression is limited to the S , G2 and M phases in proliferating metazoan cells [7] , [8] . Geminin binds and sequesters Cdt1 in an inactive complex that cannot recruit the MCMs thereby suppressing origin licensing [9] , [10] . The degradation of geminin in late M phase releases Cdt1 from geminin and allows the reformation of the pre-RC in the subsequent G1 . Disruption of licensing mechanisms leads to re-replication in a variety of model organisms . The overexpression of Cdt1 by itself or in combination with Cdc6 results in re-replication in p53 deficient human cancer cell lines [11] . Similarly , Cdt1 overexpression or geminin depletion has also been demonstrated to result in re-replication in C . elegans [12] and Drosophila [13] , [14] . In contrast , Cdt1 overexpression is insufficient to induce re-replication in yeast; instead , the simultaneous mutation of multiple pre-RC components is required to override the licensing control mechanisms [15] , [16] . Regardless of the exact mechanism ( s ) that prevent re-replication , a common feature among eukaryotes is that not all sequences are equally susceptible to re-initiation of DNA replication . In human cancer cell lines , re-replication induced by Cdt1 overexpression or Cdt1/Cdc6 co-overexpression occurs in early replicating regions of the euchromatin [11] , whereas genome-wide studies in both S . cerevisiae and S . pombe have shown that re-initiating origins of replication are distributed throughout the genome with increased re-replication at subtelomeric sequences [17]–[19] . These studies also suggest the intrinsic ability of an origin to re-initiate DNA replication is regulated both at the level of pre-RC assembly and activation [18] . The deregulation of replication licensing leads to DNA damage , checkpoint activation , aneuploidy and genomic instability [11] , [20]–[22] . We sought to better understand how origins of replication are selected and activated in the absence of replication licensing controls . Specifically , we have depleted geminin from Drosophila Kc167 cells and assessed the consequences of re-replication using genome-wide approaches . We found that the pericentromeric heterochromatin was preferentially re-replicated in the absence of geminin . Re-replication of the heterochromatin was due to the dynamic re-assembly of a limited number of pre-RCs whose assembly was restricted to the heterochromatin by cyclin A-CDK activity . These results provide insights into how cell cycle controls and chromatin environment facilitate the selection and regulation of origins of replication .
The increase in DNA content observed in the absence of geminin in both Drosophila [13] and human cell lines [20] does not follow precise genome-unit integer steps , but rather represents a broad continuum of DNA content greater than 4N . The non-integer increase of DNA copy number observed in geminin depleted cells suggests that certain sequences may have a differential capacity to re-initiate DNA replication . We sought to characterize the susceptibility of unique sequences in the Drosophila genome to re-initiate DNA replication in the absence of geminin . RNA interference ( RNAi ) was used to reduce the expression of geminin in Drosophila Kc167 cells . Asynchronous cells were treated with dsRNA targeting either geminin or a non-specific control sequence derived from the plasmid pUC119 ( pUC ) . Geminin protein levels were depleted in a time dependent manner in the cells treated with geminin dsRNA ( Figure S1 ) . In contrast , geminin levels were not perturbed by treatment with pUC dsRNA . As previously reported [13] , [23] , we observed a decrease in the stability of Dup , the Drosophila Cdt1 homolog , in the absence of geminin . Consistent with published data [13] , flow cytometry of DNA content revealed that geminin deficiency resulted in the inappropriate re-replication of the genome . After only 24 hours of treatment with geminin dsRNA , a large population of cells ( >50% ) contained more than 4N DNA content and by 48 hours of treatment , many of the cells had a DNA content greater than 8N ( Figure 1A ) . To identify sequences prone to re-replication in geminin depleted Kc167 cells , we utilized comparative genomic hybridization ( CGH ) to directly measure the relative change in DNA copy number generated as a result of re-replication . In this assay , DNA from independent biological replicates was harvested from either geminin or pUC dsRNA treated cells , differentially labeled with Cy5 and Cy3 conjugated dUTP , and hybridized to custom genomic tiling microarrays containing unique probes spanning the sequenced Drosophila euchromatin . The log ratio of Cy5 to Cy3 signal served as a proxy for the relative copy number difference between geminin depleted and control cells . Geminin depletion resulted in strikingly elevated levels of the fourth chromosome ( Figure 1B ) . The Drosophila fourth chromosome is unique because of its small size , high transposon density and heterochromatic nature [24] . Given the distinct chromatin environment of the fourth chromosome , we sought to more closely examine copy number throughout the remaining Drosophila chromosomes . We analyzed the relative copy number of DNA from geminin depleted cells versus control DNA across each of the chromosomes by plotting the moving average of DNA copy number as a function of chromosome position . The relative copy number for the large majority of euchromatic sequences along the chromosome arms of geminin depleted cells was constant and equal to one . However , as the sequences approach the pericentromeric heterochromatin on chromosome 2 , 3 and X , the copy number increased sharply , indicating that DNA sequences in these regions have re-replicated ( Figure 1C and Figure S2 , black ) . In Drosophila , repressive chromatin is marked by the trimethylation of histone H3 on lysine residue 9 ( H3K9me3 ) in the constitutive pericentromeric heterochromatin and by trimethylation of histone H3 on lysine 27 ( H3K27me3 ) at polycomb repressed sequences [25] , [26] . The elevated copy number we observe for chromosome 4 and the sequences adjacent to the heterochromatin for each of the remaining chromosomes suggests that the constitutive heterochromatin is being preferentially re-replicated in the absence of geminin . These sequences also exhibit an increased density of transposable elements ( Figure 1C and Figure S2 , red ) . We examined the extent of re-replication for sequences marked by either H3K9me3 or H3K27me3 using data generated by the modENCODE consortium [27] . These genomic datasets describe the genome-wide location of lysine trimethylation on histone H3 using nearly identical growth conditions ( including serum ) as our own experiments . We found that re-replication in the absence of geminin was specific for the constitutive heterochromatin marked by H3K9me3 ( Figure S3 ) . Because the heterochromatic sequences and those near heterochromatin have a reduced sequence complexity , we wanted to ensure that the increase in DNA copy number was not an artifact of the array hybridization . As a control , we hybridized independent samples of control DNA versus itself . The relative copy number of the control DNA was constant across each of the chromosome arms ( Figure 1C and Figure S2 , gray ) . To account for any potential variation in the copy number estimates due to the asynchronous cell cycle distribution of the control cells , we also compared by CGH analysis DNA from geminin depleted cells to cells arrested in G2/M by colcemid treatment . This analysis revealed a copy number increase for heterochromatic sequences that was indistinguishable from our prior results ( Figure S4 ) . Finally , to further validate our copy number estimation from the genomic microarrays , we tested seven different loci throughout the euchromatin and heterochromatin of chromosome 3R by quantitative PCR ( qPCR ) . The qPCR results confirmed the heterochromatin specific 2–2 . 5 fold increase in DNA content number we observed by the genomic tiling arrays ( Figure S5 ) . Our genomic analysis indicated that there was increased copy number of sequences adjacent to the pericentromeric heterochromatin; however , our initial genomic tiling arrays lacked representative probes from this region of the genome . To further identify which regions of the genome were being re-replicated in the absence of geminin , we assessed the cytological location of active replication by immunofluorescence . Actively replicating sequences were labeled by the incorporation of 5-bromo-2-deoxyuridine ( BrdU ) . BrdU was added to the medium for a 4 hour window at 18 hours post RNAi treatment , at which time re-replication is clearly detectable by FACS in geminin depleted cells ( data not shown ) . The cells treated with pUC dsRNA exhibited multiple distinct patterns of BrdU incorporation , including: no BrdU incorporation , BrdU incorporation all over the nucleus , and BrdU incorporation localized to a small region at the nuclear periphery ( Figure 2A ) . These distinct patterns of BrdU incorporation were classified as either ‘none’ , ‘global’ , or ‘local’ , respectively ( Figure 2B ) . These patterns likely represent cells in different stages of the cell cycle . For example , cells with no BrdU staining ( none ) were likely in G2-M-G1 of the cell cycle , cells with limited BrdU in a confined region at the nuclear periphery ( local ) were in late S phase , and finally those cells with BrdU incorporation throughout the nucleus ( global ) represent cells in early to mid S phase [28] . In contrast , almost 90% of BrdU staining geminin depleted cells exhibited the ‘local’ BrdU incorporation pattern in a small region of the nucleus ( Figure 2A and 2B ) . These results were consistent with re-replication being confined to a specific region of the genome . The ‘local’ pattern of BrdU incorporation in the nucleus , the detection of increased copy number near the pericentromeric heterochromatin and the increased copy number for sequences marked by H3K9me3 in geminin depleted cells suggested that the pericentromeric heterochromatin was being preferentially re-replicated in the absence of geminin . In Drosophila , the pericentromeric heterochromatin is enriched for HP1 , a heterochromatic protein that interacts with H3K9me3 [26] . To confirm that re-replication was localized to the heterochromatin , we used immunofluorescence to simultaneously detect newly synthesized DNA and HP1 ( Figure 2C ) . Control and geminin depleted cells were pulse labeled with 5-ethynyl-2-deoxyuridine ( EdU ) , for 30 minutes at either 24 or 48 hours post RNAi . EdU was used for these experiments because unlike BrdU , the denaturation of DNA was not required for detection . In control cells we observe that EdU is either specifically co-localized with HP1 ( actively replicating heterochromatin ) or that it is excluded from the HP1 staining regions of the genome ( actively replicating euchromatin ) ( Figure 2C and 2D ) . In contrast , geminin depleted cells undergoing re-replication almost exclusively exhibit co-localization of EdU and HP1 , consistent with the preferential re-replication of the heterochromatin . Together with the array data , these results show that the majority of DNA synthesis occurring in geminin depleted cells is restricted to heterochromatic regions of the Drosophila genome . Origins of replication are activated in S phase with a characteristic timing and efficiency . Chromatin structure is thought to play a role in establishing the temporal order of origin activation [29] . For example , active transcription and histone acetylation have been positively correlated with early replication in a variety of model organisms [30]–[34] . Similarly , poorly transcribed regions of the genome , often marked by repressive chromatin modifications , are typically the last sequences to be duplicated in S phase [31] , [35] . Our genomic and cytological results are paradoxical because they suggest that in the absence of geminin , the initiation of DNA replication is most efficient in the pericentromeric heterochromatin marked by H3K9 methylation and HP1 . These findings imply that either late replicating sequences are more prone to re-replication or that the observed re-replication is a function of chromatin environment . To determine if susceptibility to re-replication was a function of late replicating sequences or a heterochromatic chromatin enviroment , we examined the extent of re-replication and the relative time of replication evident in heterochromatic sequences using a custom array design [36] that , in addition to the euchromatic sequences , contained all of the unique Drosophila heterochromatic sequences [37] . DNA was isolated from control and geminin depleted cells and hybridized to the new genomic arrays . Statistical analysis of the relative probe levels between control and geminin depleted cells revealed a clear increase in copy number for those probes located in the annotated heterochromatin ( Figure 3A; p<1×10−16 ) . Analysis of the data relative to chromosome position revelead a constant one and a half to two-fold increase in ploidy in the heterochromatin ( red ) relative to the euchromatin ( black ) , with a shallow transition from heterochromatin to euchromatin , as shown for a representative region of the right arm of chromosome 2 ( Figure 3B ) . Importantly , as our previous array data indicated , re-replication and the increase in DNA copy number was limited to the heterochromatin and adjacent sequences . The normal mitotic replication timing of the heterochromatin was examined by differentially labeling early and late replicating sequences from synchronized cells [35] , [36] . Analysis of global replication timing values from either euchromatic or heterochromatic sequences revealed that the heterochromatin replicated significantly later than the euchromatin ( Figure 3C; p<1×10−16 ) . We did not detect the presence of efficient clusters of mitotic replication origins in the heterochromatin which would be evident as a continuum of enriched probes culminating in a clear and defined peak , as shown for the right arm of chromosome 2 ( Figure 3D ) . Importantly , late replication does not appear to be a determinant of re-replication as there are many late replicating regions in the euchromatin that do not appear to re-replicate as shown for a representative region of the genome ( Figure 3B and 3D ) . These data argue that susceptibility to re-initiation of DNA replication is distinct from the mechanisms that regulate replication timing . Prior to initiation of DNA replication , the pre-RC must be assembled at origins of replication . Typically , low CDK levels and the absence of geminin allow for the global assembly of pre-RCs in G1 [2] . We hypothesized that re-replication in the absence of geminin may be limited to the heterochromatin by restricting either pre-RC assembly or activation to these sequences . For example , in the absence of geminin the re-assembly of the pre-RC may occur almost exclusively in the heterochromatin . Alternatively , there may be a global re-assembly of the pre-RC throughout the Drosophila genome , but initiation of DNA replication is strictly limited to the heterochromatin . To differentiate between these two models , we assessed the re-loading of MCMs onto the DNA by chromatin fractionation and immunofluorescence . We first examined the global levels of chromatin-associated MCMs by chromatin fractionation . Chromatin was prepared from pUC dsRNA treated cells ( pUC ) , cells arrested with hydroxyurea ( HU ) , and cells treated with geminin dsRNA ( Gem ) for 24 hours . ORC remained associated with the chromatin fraction in all samples . In contrast , MCM association with chromatin was only observed in pUC RNAi cells and cells arrested at the G1/S border by treatment with HU . A marked reduction in MCM levels was observed on the chromatin prepared from geminin depleted cells while total MCM levels were not affected by geminin depletion ( Figure 4A ) . Despite the reduction in chromatin-associated MCM levels in the geminin depleted cells , a significant number of cells ( 60%–85% ) exhibited active DNA synthesis as measured by a 30 minute EdU pulse ( Figure 4B ) . Thus , cells undergoing re-replication are able to synthesize DNA with a reduced complement of MCM proteins . To gain further insights into the mechanism of MCM reloading , we examined MCM loading and DNA synthesis at the single cell level . The association of MCMs with chromatin was assessed by immunofluorescence of cells treated with a mild detergent and salt prior to fixation . The mild salt extraction ensures that only the loaded and active MCMs remain bound to the chromatin [38] , [39] . We found that the fraction of MCM positive staining cells gradually decreased during geminin RNAi treatment . At 24 hours following geminin depletion , less than 20% of the cells had detectable MCM staining ( Figure 4C ) ; however , more than 80% of the cells exhibited active DNA synthesis . Even after 48 hours of geminin depletion , nearly 70% of the cells exhibited DNA synthesis following a 30 minute EdU pulse ( Figure 4B ) . We consistently observed that the number of geminin depleted cells that were actively synthesizing DNA was much higher than the number of cells exhibiting detectable staining for chromatin-associated MCMs . Together , these data suggest that in the absence of geminin , pre-RC assembly is inefficient and that minimal levels of MCM re-assembly are sufficient for un-regulated DNA replication . The low levels of MCMs which are sufficient for un-licensed re-initiation of DNA replication in the absence of geminin made it difficult to assess whether the re-assembly of the pre-RC was limited to the heterochromatin or occurred throughout the Drosophila genome . We hypothesized that during re-replication the re-assembly of the pre-RC might be labile with the MCMs exhibiting a short half life on the DNA . In order to extend the half-life of the MCMs on chromatin , we used aphidicolin to inhibit DNA polymerase and arrest any active replication forks . DNA synthesis was inhibited in both control and geminin depleted cells at 24 hours post RNAi treatment by the addition of aphidicolin ( Figure 5A ) . The majority of control cells arrest with an early S-phase FACS profile upon exposure to aphidicolin . Similarily , aphidicolin inhibited additional re-replication in the absence of geminin and the cells arrest with a DNA content profile indistinguishable from that of cells depleted of geminin for 24 hours . In normally dividing cells , the chromatin association of MCM proteins changes thoughout the cell cycle [40]–[43] . During G1 , the MCMs are loaded onto the chromatin to form the pre-RC and to license replication origins for entry into S phase . As DNA replication progresses through S phase , the MCMs are displaced from the DNA by the passage of the replication fork [40] , [41] . Thus , during G1 and early to mid S phase the MCMs are localized throughout the entire nucleus , and by late S phase they only remain associated with late replicating sequences . We classified the untreated control cells into three categories based on their MCM localization patterns ( Figure 5B ) . In 37% of the control cells , the MCMs localized throughout the nucleus with increased staining at the heterochromatic region marked by HP1 ( type I ) . We interpret these cells to be in G1 and early to mid S phase . In contrast , 18% of cells appeared to be in late S phase with MCM staining limited to heterochromatin ( type II ) while the remaining 46% of cells were devoid of nuclear MCM staining ( type III ) and likely in G2 or M phase . The distributions of cells in each phase of the cell cycle were consistent with the FACS profiles ( Figure 5A ) . Treatment with aphidicolin altered the cell cycle distribution of control cells with the majority of cells accumulating at the G1/S transition and exhibiting the type I staining pattern ( Figure 5B ) . Treatment of geminin depleted cells with aphidicolin inhibited further re-replication and resulted in a clear increase in chromatin-associated MCM levels as evidenced by the increase in the number of cells with positive MCM staining ( type I and II , from 19% to 63% ) . Unlike control cells where the majority of MCMs were localized throughout the nucleus upon exposure to aphidicolin ( Figure 5C arrow ) , the predominant localization pattern in the geminin depleted cells was at the heterochromatin , co-incident with HP1 staining ( Figure 5B and 5C ) . In contrast to the strong uniform heterochromatic staining we observe in control cells and a few untreated geminin depleted cells , aphidicolin treatment of geminin depleted cells often resulted in a weak and heterogeneous staining of the MCMs throughout the heterochromatin . For example , there was light staining localized with a subfraction of HP1 ( Figure 5C , open arrow ) as well as faint and diffuse staining which completely colocalized with HP1 ( Figure 5C , open arrowhead ) . This weak and relatively heterogenous staining was only detected in geminin depleted cells treated with aphidicolin and may represent a random selection of heterochromatic origins of replication on a cell by cell basis . Prior studies in human cells indicated that cyclin A-CDK activity stimulates re-replication in the presence of excess Cdt1 or Cdc6 [11] . Knockdown experiments in Drosophila cells showed cyclin A silencing suppressed the partial re-replication induced by geminin depletion [13] . We sought to investigate whether cyclin A-CDK activity plays a role in restricting pre-RC assembly to the Drosophila heterochromatin . As reported previously [13] , we also observed that depletion of cyclin A by RNAi arrests cells at G2/M , and after a delay of approximately 24 hours , the cells initiate a complete endoreduplication of their genome ( Figure 6A , compare panel 2 and 4 ) . Importantly , in the absence of both geminin and cyclin A , there is only limited re-replication at 24 hours ( panel 3 ) and , similiar to the cyclin A depleted cells , we observe a complete reduplication of the genome by 48 hours ( panel 5 ) . The suppression of partial re-replication by cyclin A depletion is not due to inefficient depletion of geminin in the cyclin A co-knockdown experiment ( Figure 6D ) . Presumably , the decreased CDK activity associated with the cyclin A depletion allows for the genome-wide re-assembly of the pre-RC which would facilitate the complete reduplication of the genome . We examined the distribution of chromatin-associated MCMs in cells depleted for cyclin A , geminin , or cyclin A and geminin . In the absence of cyclin A , the majority of cells ( ∼80% ) exhibited MCM loading on the chromatin 24 hours prior to the endoreduplication ( Figure 6B and 6C ) . An equal number of cells with type I ( throughout the nucleus ) and type II ( restricted to the heterochromatin ) MCM localization patterns were observed . Similar results were obtained for cells depleted of both geminin and cyclin A ( Figure 6B and 6C ) . This is in sharp contrast to the almost complete absence of chromatin associated MCMs observed in cells depleted only for geminin ( Figure 5B and Figure 6C ) . Together , these results suggest that pre-RC re-assembly may not be specifically targeted to the heterochromatin , but rather the re-assembly of the pre-RC in euchromatin is inhibited by persistent cyclin A-CDK activity .
Maintenance of constant genome ploidy is critical for eukaryotic organisms . If unchecked , disruption of the mechanisms that tightly couple DNA replication with the cell cycle may result in re-replication , aneuploidy and genomic instability . We have found that perturbation of Dup activity by geminin depletion results in the preferential re-replication of heterochromatic sequences in the Drosophila genome . Re-replication was limited to pericentromeric heterochromatic sequences which are marked by HP1 and H3K9 methylation . Euchromatin , including gene-poor late replicating sequences and polycomb repressed sequences , was resistant to re-replication . In the absence of geminin , a minimal complement of MCMs assembled on the chromatin was sufficent for re-replication . These findings suggest that the 2–3 fold increase in ploidy we observed was regulated by the specific re-assembly and activation of the heterochromatic pre-RCs . The preferential re-replication of heterochromatic sequences in the absence of licensing controls was particularly striking given the established view that repressive chromatin environments are inhibitory to efficient origin activation [44] . Classic experiments have clearly demonstrated that the heterochromatin in Drosophila and other ogranisms is the last region of the genome to be duplicated during S phase [45] . The complex nature of the heterochromatic sequences has hampered detailed analysis of the replication program in this part of the genome and it remains possible that the heterochromatin may be populated by a very limited number of ultra-efficient origins of replication . These may be required to ensure that the heterochromatin is duplicated in a timely manner at the end of S phase and that in the absence of licensing controls these origins are preferentially activated . Our genomic data did not identify any robust origins of replication in the heterochromatin that were consistently used across the cell population . Similarly , we found that the bulk of heterochromatin was re-replicated to similar ploidy levels suggesting that origin re-activation in the absence of geminin is a stochastic process . Analysis of total DNA ploidy by FACS revealed that the majority of cells exhibited a DNA content greater than 8N following geminin depletion , suggesting geminin depleted cells had increased their total DNA content by at least 2-fold over that of G2 cells . The heterochromatin constitutes a minimum of 30% of the Drosophila genome [46] . Assuming that re-replication is specific to the heterochromatin , a 4 . 5 fold increase in heterochromatic DNA content would be sufficient to account for the increase in DNA ploidy we observe . However , we consistently observed , by multiple methods , only a 2–2 . 5 fold increase in heterochromatic DNA content ( Figure 1 , Figure S2 and S4 ) . We speculated that the highly repetitive non-unique heterochromatic sequences might be preferentially re-replicated to higher ploidy levels . We tested this hypothesis by examining the genomic abundance of the 1 . 688 satellite DNA which accounts for 4% of the Drosophila genome [47] . Again , we only observed a 2–2 . 5 fold increase in the bulk levels of the 1 . 688 satellite DNA ( Figure S6 ) . It is clear that the heterochromatin is preferentially re-replicated in the absence of geminin; however , we are unable to rule out the possibility that a limited amount of stochastic re-replication is also occurring in the euchromatin . In higher eukaryotes , there are many more MCM complexes loaded onto the chromatin in G1 than are required to complete an unperturbed S phase [48]–[51] . Although these excess MCMs are not required for completion of a normal S phase , they are critical for protecting the cell from genomic instability during replication stress [52] . However , when we deplete geminin , we only observe a minimal complement of MCMs being re-loaded onto heterochromatic DNA . These results suggest that there is not a global re-assembly of the pre-RC throughout the genome as occurs in G1 and that limiting amounts of MCMs are sufficient for the greater than two-fold increase in ploidy we observe . The MCMs appear to be transiently associated with the heterochromatin as inhibition of DNA re-replication with aphidicolin results in an increase in detectable MCMs . We propose that in the absence of geminin , the MCMs are loaded onto heterochromatic sequences and that these pre-RCs are immediately activated for initiation of DNA replication . In Drosophila , Dup activity is downregulated after origin firing through multiple mechanisms including Cul4-Ddb1 mediated proteolysis in S phase and inhibition by geminin during S , G2 and mitosis . Furthermore , we ( Figure S1 ) and others [13] , [23] have reported that Dup/Cdt1 is degraded in the absence of geminin . Thus , geminin is only one factor that negatively regulates Dup/Cdt1 and its depletion may be insufficient to induce genome-wide re-replication . Therefore , the deregulation of geminin and Dup/Cdt1 may have distinct effects on replication control . This may , in part , explain the differences in sequences and chromatin environment which are preferentially targeted for re-replication in human and Drosophila cell lines . In human cell lines , Cdt1 overexpression led to the preferential re-replication of early replicating sequences [11] , while in Drosophila cell lines , geminin depletion leads to the preferential activation of heterochromatic origins of replication . Future experiments will test whether Dup levels are critical for maintaining ploidy and selecting which origins are activated . The observation that the re-replication at pericentromeric heterochromatin was not coupled with late replication timing suggests that origin selection during re-replication and the temporal control of DNA replication in S phase are regulated by distinct mechanisms . These data suggest that a key determinant of which sequences will re-initiate DNA replication is the local chromatin environment . Drosophila pericentromeric heterochromatin is marked by H3K9 methylation which is maintained by Su ( var ) 3-9 and HP1[53] , [54] . ORC has been shown to interact with HP1 and localizes to heterochromatin by immunofluorescence in both interphase and mitotic nuclei [55] , [56] . It is therefore possible that the increased density of ORC in the heterochromatin may stimulate the preferential re-assembly of the pre-RC at those sequences . However , recent studies using GFP tagged ORC and live imaging did not observe an increased density of ORC at the heterochromatic regions of the genome [57] . We found that cyclin A-CDK activity regulates the re-activitation of replication origins at two levels . First , cyclin A-CDK activity is required for the large increase in ploidy observed , consistent with the known role of CDK activity in activating the pre-RC for initiation [1] . Second , cyclin A-CDK activity appears to differentially inhibit pre-RC re-assembly in the euchromatin and heterochromatin . The simultaneous depletion of both cyclin A and geminin results in the global re-assembly of the pre-RC in both euchromatin and heterochromatin ( Figure 6 ) . In contrast , depletion of only geminin results in pre-RC re-assembly specific to the heterochromatin , suggesting that cyclin A-CDK activity specifically inhibits pre-RC assembly in the euchromatin . In humans , the N-terminal domain of ORC1 contains consensus CDK phosphorylation sites which can be phosphorylated in vitro by cyclin A-CDK activity and may regulate the SCF/Skp2 mediated turnover of ORC1 during S phase [58] . In Drosophila , the ORC1 N-terminus also contains potential CDK phosphorylation sites , an additional O-box for APC mediated destruction [59] , and is essential for the binding of ORC1 with HP1 [56] . We propose that the interaction between ORC1 and HP1 may protect ORC1 from inhibitory cyclin A-CDK signals or destruction by the APC , thereby differentially sensitizing heterochromatic and euchromatic origins of replication to un-licensed pre-RC assembly .
Drosophila Kc167 cells were cultured at 25°C in Schneider's Insect Cell Medium ( Invitrogen ) supplemented with 10% fetal bovine serum and 1% penicillin/streptomycin/glutamine ( Invitrogen ) . DNA synthesis was inhibited by treatment with 1 mM hydroxyurea ( Sigma ) or 7 µM aphidicolin ( Sigma ) . All double-stranded RNAs were generated using gene-specific PCR products ( ∼700bp ) flanked by T7 polymerase-binding sites as templates for in vitro transcription reactions with the T7 RiboMax express large-scale RNA production system ( Promega ) . Primers are available upon request . For each RNAi experiment , cells were washed with and diluted in serum-free medium to a final concentration of 2×106 cells/ml . 15µg dsRNA was added per 1×106cells , gently mixed , and incubated for one hour . After the incubation , 2× medium was added resulting in a final concentration of 1×106 cells/ml . The cells were incubated for 1–2 days before harvesting . DNA was isolated as described [60] . Isolated DNA was sheared and labeled with either fluorescent Cy5- or Cy3-conjugated dUTP ( GE Healthcare Bio-Sci Corp . ) using Sequenase ( US Biochemicals ) and a random nonamer oligo ( IDT ) . The labeled DNA was purified using Microcon YM-30 filters ( Millipore ) and hybridized to custom Agilent tiling microarrays overnight at 65°C . The slides were then washed and scanned as per Agilent recommendations . The Agilent generated tif files from each genomic microarray were processed and analyzed in R [61] , a free software environment for statistical computing and graphics . The LIMMA package [62] from the BioConductor project [63] was used to normalize the ratio of the Cy5 and Cy3 channels across each genomic tiling array by loess normalization . Quantile normalization was used to normalize between replicate slides . The relative copy number between control and geminin depleted cells was determined by the log ratio of Cy5 and Cy3 . To assess the significance of the copy number enrichment for different genome features ( chromosome , heterochromatin , euchromatin , etc ) a Student's t-test was utilized and p-values are indicated in the figure legends . All genomic coordinates are based on the 5 . 0 assembly of the Drosophila genome . The analysis of the replication timing data is described in MacAlpine et al . , 2010 . All genomic data with accompanying metadata ( protocols and analysis parameters ) are publicly available at the NCBI GEO data repository . The accession numbers are: GSE17279 Kc167 replication timing , GSE20781 S2 H3K27me3 , GSE20794 S2 H3K9me3 , and GSE20932 Kc167 re-replication CGH data . Antibodies used in western blotting include: rat anti-geminin antibody ( a gift from Helena Richardson ) , anti-Dup guinea pig antibody ( a gift from Terry L . Orr-Weaver ) , anti-acetylated H4 antibody ( Upstate ) , and mouse anti-cyclin A antibody ( Developmental Studies Hybridoma Bank ) . All antibodies were used at a 1∶3000 dilution . Chromatin fractionation was carried out as described [64] . Samples were analyzed by PAGE and western blotting . Primary antibodies used include anti-Orc2 at 1∶3000 and anti-MCMs ( AS1 . 1 ) at 1∶100 . Secondary antibodies used include Alexa Fluor 680 goat anti-rabbit IgG ( Invitrogen ) , IRDye 800 conjugated anti-mouse IgG ( Rockland Immunochemicals ) , both at a 1∶10000 dilution . Immunoblots were scanned using a LICOR imaging system . For the BrdU incorporation experiments , cells were fixed with methanol: acetic acid 3∶1 ( v/v ) after a 4-hour incubation with 5µg/ml BrdU ( Roche ) . A rat anti-BrdU antibody ( Abcam Inc . ) was used at 1∶200 and Alexa Fluor 594 goat anti-rat secondary antibody ( Invitrogen ) was used at 1∶500 . For the double labeling of EdU and HP1 , cells were incubated in medium with 10 µM EdU for 30 minutes , treated with 0 . 5% Triton X-100 in PBS for one minute , fixed with 4% paraformaldehyde in PBS , then run through the EdU Click-iT reaction cocktail ( Invitrogen ) . Cells were then stained with rabbit anti-HP1 antibody ( #191 , 1∶1000 , a gift from Sarah Elgin ) and Alexa Fluor 568 goat anti-rabbit secondary antibody ( 1∶500 , Invitrogen ) . For double labeling of MCMs and HP1 , cells were treated with Triton/PBS and fixed as for EdU and HP1 staining , stained with monoclonal MCM antibody ( AS1 . 1 , 1∶100 ) and anti-HP1 antibody ( #191 , 1∶1000 ) , followed by secondary detection with Alexa Fluor 488 goat anti-mouse and Alexa Fluor 568 goat anti-rabbit antibodies ( 1∶500 ) . Chi-square tests were performed for quantifications of immunofluorescence localization patterns and the p-values are shown in the corresponding figure legends . The relative copy number for seven loci spanning both euchromatin and heterochromatin on chromosome 3R was examined . Specifically , three loci were located in the pericentric heterochromatin , two in the euchromatin adjacent to pericentric heterochromatin , and the remaining two in the euchromatin distant from pericentric heterochromatin . DNA isolated from control pUC dsRNA treated cells was used to generate the qPCR standard curve . Primers are available upon request . Quantitative PCR was performed using iQ SYBR Green Supermix ( Bio-Rad ) on Bio-Rad iQ5 Real-Time PCR Detection System .
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Catastrophic consequences may occur if the cell fails to either completely copy the genome or if it duplicates some regions of the genome more than once in a cell cycle . The cell must coordinate thousands of DNA replication start sites ( origins ) to ensure that the entire genome is copied and that no replication origin is activated more than once in a cell cycle . The cell accomplishes this coordination by confining the selection and activation of replication origins to discrete phases of the cell cycle . Start sites can only be selected or ‘licensed’ for DNA replication in G1 and similarly , they can only be activated for the initiation of DNA replication in S phase . Disruption of the mechanisms that regulate this ‘licensing’ process have been shown to result in extensive re-replication , genomic instability and tumorigenesis in a variety of eukaryotic systems . Here we use genomic approaches in Drosophila to identify which origins of replication are susceptible to re-initiation of DNA replication in the absence of replication licensing controls . Unexpectedly , we find that sequences in the heterochromatin , which were thought to contain only inefficient origins of replication , are preferentially re-replicated . These results provide insights into how origins of replication are selected and regulated in distinct chromatin environments to maintain genomic stability .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"genetics",
"and",
"genomics/epigenetics",
"molecular",
"biology/chromosome",
"structure",
"molecular",
"biology/dna",
"replication",
"genetics",
"and",
"genomics/chromosome",
"biology"
] |
2010
|
Preferential Re-Replication of Drosophila Heterochromatin in the Absence of Geminin
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Understanding the genetic basis of reproductive isolation promises insight into speciation and the origins of biological diversity . While progress has been made in identifying genes underlying barriers to reproduction that function after fertilization ( post-zygotic isolation ) , we know much less about earlier acting pre-zygotic barriers . Of particular interest are barriers involved in mating and fertilization that can evolve extremely rapidly under sexual selection , suggesting they may play a prominent role in the initial stages of reproductive isolation . A significant challenge to the field of speciation genetics is developing new approaches for identification of candidate genes underlying these barriers , particularly among non-traditional model systems . We employ powerful proteomic and genomic strategies to study the genetic basis of conspecific pollen precedence , an important component of pre-zygotic reproductive isolation among yellow monkeyflowers ( Mimulus spp . ) resulting from male pollen competition . We use isotopic labeling in combination with shotgun proteomics to identify more than 2 , 000 male function ( pollen tube ) proteins within maternal reproductive structures ( styles ) of M . guttatus flowers where pollen competition occurs . We then sequence array-captured pollen tube exomes from a large outcrossing population of M . guttatus , and identify those genes with evidence of selective sweeps or balancing selection consistent with their role in pollen competition . We also test for evidence of positive selection on these genes more broadly across yellow monkeyflowers , because a signal of adaptive divergence is a common feature of genes causing reproductive isolation . Together the molecular evolution studies identify 159 pollen tube proteins that are candidate genes for conspecific pollen precedence . Our work demonstrates how powerful proteomic and genomic tools can be readily adapted to non-traditional model systems , allowing for genome-wide screens towards the goal of identifying the molecular basis of genetically complex traits .
By identifying the genes underlying barriers to reproduction , we gain broad insight into the processes driving speciation and the origins of organismal diversity . Reproductive barriers have long been known to include multiple pre-zygotic mechanisms that function before fertilization , after which post-zygotic barriers including hybrid sterility and inviability mark an irreversible stage of reproductive isolation [1] . Significant progress has been made in understanding the mechanisms that underlie post-zygotic isolating barriers , and in several cases the causal genes have been identified [2]–[4] . This work has had tremendous impact on our understanding of the genetic basis and evolutionary forces underlying hybrid sterility and inviability , in particular highlighting the important role of genetic conflicts and epistatic genic incompatibilities known as Bateson-Dobzhansky-Muller ( BDM ) incompatibilities [2] . In contrast , significantly less is known regarding the genes underlying earlier acting pre-zygotic barriers . Of particular interest are barriers that result from divergence in traits and molecules that mediate mating or fertilization , and are thus likely targets of sexual selection . As with classical examples of sexually selected traits [5] , sexual selection even at a molecular level can be a strong force leading to the rapid evolution of reproductive barriers [6] . Behavioral and morphological traits that function prior to mating , for example in courtship displays , have been of interest as targets of rapid divergence via sexual selection since Darwin [7] . Only more recently have the potential impacts of male competition , female preference , and sexual antagonism been widely recognized as extending to the host of morphological , physiological , and biochemical characters that impact reproductive success after mating but prior to fertilization [8] . Identifying the genetic basis of these post-mating , pre-zygotic reproductive barriers has proven challenging . Traditional genetic mapping approaches are complicated by the genetically complex nature of these isolating barriers , which typically reflect the small cumulative effects of many loci across the genome [9] . Biochemical characterization of their molecular basis is also technically difficult because they often involve competitive interactions among gametes which are manifest only within particular structures of the female reproductive system of animals or plants [10] . Instead , efforts have largely focused on indirect screens to distinguish reproductive genes without addressing their role in any specific post-mating barrier , for example by large scale sequencing of testis- or ovary-expressed genes [11] , [12] . Then , because positive selection and rapid divergence are likely features of the genes underlying these reproductive barriers [13] , comparative sequence data from closely related taxa or population polymorphism data can be analyzed using statistical approaches to distinguish candidates among reproductive genes [14] . More recently a number of biochemical and proteomic techniques have been developed to focus this work more discretely on particular reproductive structures or stages of reproduction during which reproductive barriers are known to arise ( e . g . , proteins of the egg coat [15] or accessory gland transferred to females during mating [16] ) . Such two-tiered approaches , incorporating characterization of constituent proteins in tandem with molecular evolutionary analyses to identify candidate genes , constitute an important step enabling targeted genetic mapping studies and ultimately functional characterization towards the ultimate goal of identifying the genetic basis of post-mating , pre-zygotic barriers . The yellow monkeyflowers are an emerging model system for studies of the genetic basis of reproductive isolation and speciation in plants [17] . This group of taxa ( Mimulus guttatus species complex [17] , [18] ) are ecologically and morphologically diverse but broadly interfertile [19] , consistent with phylogenetic evidence of recent diversification [18] . Several large-flowered primarily outcrossing species are distributed across Western North America [20] including the widespread M . guttatus from which multiple self-pollinating taxa appear to be independently derived [21] , [22] . Despite their recent divergence , populations and taxa are reproductively isolated by multiple pre-mating barriers ( most notably flowering phenology [23] , [24] ) as well as conspecific pollen precedence ( CPP [25] ) . This common post-mating but pre-zygotic barrier [25]–[34] results from conspecific male pollen out competing pollen from interfertile heterospecific taxa on the female pistil [10] . Among yellow monkeyflowers , CPP has been studied most extensively between M . guttatus and the closely-related selfer M . nasutus . These species have broadly overlapping ranges and hybrid individuals are frequent in some areas [19] , [22] , [24] with molecular evidence of introgression when they occur in sympatry [21] . Thus despite earlier acting premating barriers [23] , [24] , postmating barriers are important filters of genetic exchange between these taxa . In interspecific crosses between M . guttatus and M . nasutus , CPP arises after unbiased germination on the stigma and is a unidirectional phenomenon whereby outcrossing M . guttatus pollen out competes pollen from the selfer within the outcrossing specie's styles , but is equivalent in siring success on the selfing maternal background [25] . Unidirectional CPP is consistent with theory [35] , [36] showing sexual selection is manifest differently in taxa with different mating systems ( differing degrees of outcrossing vs . selfing ) , demonstrating it's probable role in the evolution of this post-mating barrier between M . guttatus and M . nasutus . Significantly , analogous post-mating barriers among marine invertebrates ( conspecific sperm precedence , CSP ) present rare textbook examples of known speciation genes [37] . Conspecific pollen precedence between M . guttatus and M . nasutus is a genetically complex trait . Initial genetic mapping studies showed evidence of segregation distortion among many regions of an M . guttatus×M . nasutus F2 linkage map [38] , for which the M . guttatus allele at mapped markers is predominantly in excess of Hardy-Weinerg expectations consistent with the observed pattern of unidirectional CPP . Reciprocal backcrosses of the F1 as both pollen and pistil parent to M . guttatus and M . nasutus [39] confirmed that: ( i ) segregation distortion favoring M . guttatus alleles at linked markers occurs only via the F1 pollen parent for at least 8 transmission ratio distortion loci ( TRDL ) distributed across the linkage map , consistent with the role of pollen competition causing the distortion; ( ii ) for the majority of these pollen-specific TRDL ( 7 of 8 ) , distortion occurs only in the M . guttatus pistil background , consistent with observed unidirectional CPP in this cross; ( iii ) the effect size of any one pollen-specific TRDL is relatively small ( M . guttatus allele favored 1 . 3 to 1 . 9-fold ) , but together they are sufficient to explain the near complete barrier CPP represents to M . nasutus pollen . Taken together , this is strong evidence that pollen-specific TRDL are regions that may contain candidate genes underlying CPP between M . guttatus and M . nasutus . These genes could also have a broader role in reproductive isolation and diversification of yellow monkeyflowers , as similar patterns of transmission distortion are known from crossing studies of M . guttatus with other members of the complex [40] . The goal of our work here is to identify candidate genes for CPP between M . guttatus and closely related taxa of yellow monkeyflowers , including M . nasutus . We focus specifically on identifying proteins of the male gametophyte ( pollen tube ) with evidence of positive selection and/or rapid adaptive divergence as providing an opportunity to link molecular screens and CPP via the expectation that pollen competition reflects one or more components of sexual selection — an expectation which specifically predicts sweeps and/or balancing selection for outcrossing populations of Mimulus , but which may also be reflected in a signal of adaptive divergence over longer time scales among taxa of yellow monkeyflowers . Earlier studies of pollen coat proteins in other species have suggested such positive selection could be a common feature of pollen proteins [41] , though to our knowledge positive selection on genes with pollen tube function has been largely unexplored outside of self-incompatibility loci ( though see [42] ) . Towards this goal , we first adapt a shotgun proteomic approach that utilizes isotopic labeling [16] in order to characterize genes with male function ( the pollen tube proteome ) within the style of pollinated flowers where pollen competition occurs . We then employ array capture to sequence complete pollen tube exomes from a large outcrossing population of M . guttatus to determine which genes are possible targets of directional or stabilizing selection . Finally , we test for evidence of long-term positive selection on these genes across yellow monkeyflowers . Our results provide a set of candidate genes that may underlie CPP for further investigation and represent one of the first broad perspectives of reproductive protein evolution from plants .
Cultivation of M . guttatus ( IM62 ) plants in the hydroponic growth medium containing 15N KNO3 resulted in nearly complete labeling of style proteins . From high resolution mass spectra of pooled negative controls , the median level of 15N incorporation among peptides is 95% as estimated via Hardklör [43] . Moreover , protein identification from negative controls indicates essentially all style proteins are sufficiently 15N labeled to mask their detection using our shotgun proteomic techniques . Summing over reverse phase and MuDPIT analyses of pooled negative control styles from all three 15N labeled IM62 plants , only a single mass spectra was matched to each of 4 proteins , all of which were excluded from further analyses . Because we are able to match mass spectra for peptides corresponding to many thousands of proteins from pollinated styles using a similar level of MS/MS detection effort ( see below ) , this is strong evidence supporting successful 15N labeling of maternal style tissues and allows us to confidently identify pollen tube proteins ( PTPs ) within the style . The pollen tube proteome of M . guttatus consists of more than 2 , 000 proteins . Mass spectra from 15N labeled styles pollinated with unlabeled ( 14N ) pollen ( Fig . 1A ) were matched to 2 , 554 protein coding transcripts from the M . guttatus genome ( File S1 ) . More than 81% of these ( 2 , 073 ) are distinguished by unique peptides , resulting in unambiguous protein identification . The remainder ( 481 ) are identified from peptides common to two or more proteins , and are thus designated as members of ( arbitrarily numbered ) multi-protein groups that may include one or more member proteins ( File S1 ) . Overlap in protein identification among replicate ( independently 15N labeled ) IM62 plants is substantial but incomplete , with ∼60% of PTPs shared between any two and identification of 36% common among all three replicates ( Fig . S1 ) . The majority of PTPs ( 97% ) were identified from MuDPIT analyses with about half of PTPs identified in our reverse phase analyses despite similar total MS/MS scan time for both methods ( 72 versus 60 hours , respectively ) . Significantly , because 15N labeling effectively masks maternal stylar peptides and we used conservative filters for matching of peptides from 14N pollen ( ≥1 unique peptide per protein , q-value≤0 . 002 ) , PTP identifications are made with high confidence . The MS/MS spectra and sequest search results for all analyses are publicly available ( https://sites . google . com/a/uw . edu/maccoss/home/supplementary_data/ ) . Functional annotation of PTPs shows the pollen tube proteome of M . guttatus is enriched for several classes of proteins . Among the 2447 PTPs successfully annotated ( 96% of the proteome ) , gene ontology ( GO ) terms associated with rapid growth and metabolism are the most frequent functional classifications ( Fig . 1C; File S2 ) , and are significantly enriched among PTPs relative to the genome as a whole ( Fig . 1D ) . Proteins sharing these classifications are also among the most abundant PTPs within pollinated styles , their relative abundance ( NSAF ) more than 3 orders of magnitude higher than the median value ( Fig . 1E; File S1 ) . Interestingly , despite extensive divergence since the last common ancestor between Mimulus and Arabidopsis ( ∼120 million years ago ) [44] , gross protein sequence similarity ( e-value≥e−20 ) with A . thaliana pollen proteins [45] and pollen tube transcripts [46] includes 85% on average of the proteins we identified from M . guttatus pollen tubes ( Fig . 1F ) . Mass spectra from unpollinated , unlabeled ( 14N ) styles were matched to 2 , 608 protein coding transcripts from the M . guttatus genome ( File S1 ) . More than 3/4 of these proteins ( 1 , 988 ) are distinguished by unique peptides , resulting in unambiguous protein identification , while the remainder ( 620 ) are identified from peptides common to two or more proteins ( multi-protein groups ) . Of the 2 , 554 pollen tube proteins we identified , 51% are also found in unpollinated styles of M . guttatus ( 1 , 310; Fig . S1C , File S1 ) . Thus ∼1/2 of all PTPs are not unique to the male gametophyte , and can found at high relative abundance ( File S1 ) in female structures of the flower . Our array enrichment and sequencing of exomes from 28 M . guttatus plants collected at Cone Peak yielded population polymorphism data for ∼95% of the PTPs we identified in our MS/MS studies ( 2 , 419; File S3 ) . Of 3 . 4×106 bases called after filtering raw reads , ∼1 . 3×106 variants were identified , with at least one SNP called for 97% of PTPs captured that identify 0 . 47% of a PTP's coding sequence polymorphic on average ( Fig . 2A ) . The raw Illumina reads from array-captured pollen tube exomes of Cone Peak M . guttatus are publicly available ( NCBI sequence read archive accession SRP029938 ) . The distribution of Tajima's D values calculated from PTPs harboring polymorphism ( 2 , 352 ) shows no strong evidence of confounding demographic forces at Cone peak ( Fig . 2B ) , though the distribution is somewhat long-tailed towards positive values , resulting in a mean slightly greater than zero ( mean D = 0 . 0269 ) . The 2 . 5% tails of the distribution correspond to Tajima's D values less than −1 . 83 and greater than 1 . 99 ( File S3 ) , beyond which we interpret PTP's Tajima's D statistic as showing evidence of sweeps or balancing selection , respectively . As confirmation that these thresholds of the empirical distribution constitute a 95% confidence interval under the neutral expectation , we simulated data under a neutral coalescent model for each of the 116 PTPs in the tails — in all cases p-values from simulations were ≤2 . 5% ( File S3 ) , consistent with the empirical distribution and in support of either sweeps or balancing selection acting on these genes . For the 58 PTPs with evidence of sweeps based on Tajima's D , we were able to identify orthologs from M . tilingii or M . cupriphilus for 56 , from which Fay and Wu's H was calculated . Calculations using M . tilingii as an outgroup identify 36 of these as targets of sweeps based on statistical support from coalescent simulations ( p≤0 . 05 ) , similar to results using M . cupriphilus as an outgroup ( 39 PTPs; File S3 ) . Based on a significant test statistic using one or more of these taxa as outgroups , Fay and Wu's H validates the inference of sweeps for 44 of the 58 PTPs identified via Tajima's D ( 76% ) , consistent with the view that population demographic history at Cone Peak does not confound inferences of selection via statistical tests based on site frequency spectra . Our assembly and mapping of publicly available Illumina reads for the outgroup M . tilingii yielded pairwise estimates of synonymous ( dS ) and non-synonymous ( dN ) substitution rates from alignment with IM62 protein coding sequences ( CDSs ) for 25 , 550 CDSs genome wide , including 2 , 262 PTPs ( Fig . 3A ) . While the range of dS values among PTPs is comparable to other CDSs ( 0 . 067 vs . 0 . 070 , respectively; p = 0 . 69 ) , non-synonymous substitution rates ( dN ) are markedly lower ( 0 . 005 vs . 0 . 011 , respectively; p<0 . 001 ) . This pattern of strong evolutionary constraint among PTPs is further supported by our mapping and assembly of publicly available Illumina reads for the 9 additional accessions of the M . guttatus species complex , which yielded dN/dS estimates from the one-ratio model M0 for 21 , 106 CDSs genome wide , including 2 , 133 PTPs ( Fig . 3B ) . Mean dN/dS for PTPs was less than half the genome-wide average for other CDSs ( 0 . 10 vs . 0 . 23 for PTPs and all other CDSs ( -PTPs ) , respectively; p<0 . 001] even after removing those also found in our MS/MS studies of style proteins [mean dN/dS = 0 . 11 for PTPs after removing SPs; p<0 . 001 for comparison with other CDSs ( -SPs ) ] . While strong evolutionary constraints characterize divergence of PTPs as a whole , sites models of codon evolution identify a small subset ( ∼2% ) with evidence of positive selection among yellow monkeyflowers ( File S3 ) . Manual validation of computational alignments for the 45 PTPs for which likelihood ratio tests pass the multiple-comparison corrected significance threshold ( q≤0 . 10 ) confirmed statistical support for all but two ( File S3 ) . Only one of these genes also shows evidence of adaptive diversification in the M . guttatus population at Cone Peak ( mgv1a018842m; File S3 ) , which is not a significant enrichment relative to PTPs as a whole ( one-tailed p-value = 0 . 327 from Fisher's exact test ) . Functional classifications of the 43 PTPs with evidence of adaptive divergence in combination with the 116 with evidence of sweeps or balancing selection at the Cone Peak population of M . guttatus are similar to PTPs as a whole ( Fig . 4A ) . Fisher's exact test identifies no GO categories as significantly enriched relative to other PTPs . In addition , their relative abundances ( NSAF ) span the range of values calculated for PTPs as a whole ( Fig . 4B ) , consistent with selection acting on PTPs independent of protein abundance , and nearly half these genes ( 48% ) were also identified in our MS/MS studies of style proteins , suggesting they are not enriched for pollen-specific functions .
The M . guttatus pollen tube proteome is a significant contribution to plant reproductive biology that: ( i ) demonstrates the broad applicability of 15N labeling for distinguishing male and female reproductive proteins in situ; ( ii ) catalogs male reproductive genes relative to model plant species such as Arabidopsis; and ( iii ) represents a significant increase over previous proteomic work , but is nonetheless likely to be a conservative picture of the proteins present in growing M . guttatus pollen tubes . To identify pollen tube proteins ( PTPs ) , we adapted a novel isotopic labeling scheme in order to characterize the pollen tube proteome within the particular female structures of the flower ( styles ) where pollen competition occurs . We grew plants used as the female pistil parent on hydroponic media containing 15N KNO3 as the sole source of nitrogen [52] , which rendered them effectively “invisible” in our MS/MS-based proteomic analyses . By using unlabeled ( 14N ) plants as pollen donors in controlled pollinations , we are able to identify and characterize proteins within 15N styles that were unambiguously derived from pollen tubes ( Fig . 1A ) . The general approach was recently developed in Drosophila to identify male seminal fluid proteins within the female reproductive tract of flies [16] , but had not been applied to plants previously . Our success with 15N labeling in a plant system demonstrates the approaches potential broad utility for distinguishing among male and female reproductive proteins in situ . Our catalog of the molecular components of pollen tube growth and guidance within the pistil ( File S1 ) complements expression data [46] , [53] , [54] and smaller proteomic studies [45] , [55]–[57] from other plant taxa . Shotgun proteomic analyses unambiguously identified 2 , 073 PTPs from unique peptides found within the style of pollinated 15N-labeled pistils of M . guttatus . An additional 481 correspond to multi-protein groups consisting of one or more member proteins with shared peptides . The majority of these 2 , 554 proteins ( File S1 ) are characterized by GO annotation terms ( Fig . 1C ) consistent with their role as components of the molecular machinery for rapid semi-autonomous growth and guidance of pollen tubes through the maternal style [47]–[50] and that are enriched in the pollen tube proteome ( Fig . 1D , File S2 ) . For example , among the most abundant are several methionine synthases ( Fig . 1E ) that have previously been identified as pollen tube components in other plant taxa [55] , [58] , likely facilitating targeted pollen tube tip growth through their role in diverse metabolic pathways [59] , [60] . Many of these genes are likely to also be expressed in other tissues ( see below ) , including structures of the female pistil: our MS/MS studies demonstrate that ∼1/2 of PTPs can also be found within unpollinated styles ( Fig . S1C , File S1 ) . While the large number of PTPs identified in Mimulus precludes detailed discussion , many of these genes appear to have homologs in model plant taxa as evidenced by the large degree of overlap with the A . thaliana whole pollen proteome [45] and pollen tube transcriptome [46] ( Fig . 1F ) . This overlap emphasizes the many rich opportunities for more detailed comparative studies of plant reproduction between established model systems and emerging model systems like Mimulus that represent distinct evolutionary lineages , and for which genomic and proteomic tools are rapidly developing . The number of PTPs we identified in situ from M . guttatus ( >2 , 000 ) is remarkable when compared with similar studies of animal reproductive proteins . For example , we identified ∼10-fold more proteins than previous work employing the same 15N-labeling approach with similar MS/MS-based protein detection methodologies in Drosophila [16] . This increase undoubtedly reflects the greater biological complexity of the male gametophyte of flowering plants , though variation in MS/MS detection effort , degree of maternal 15N labeling , or genome annotation may also contribute . Like a recent study of Arabidopsis whole pollen employing comparable methodologies [45] , our work represents a >3-fold increase over previous ( principally 2-D gel based ) proteomic studies of pollen and pollen tubes [55]–[57] . However , the PTPs we have identified in M . guttatus are still likely to be a conservative representation of the pollen tube proteome for several reasons . First , we reduced power to detect proteins [61] by using conservative protein identification filters aimed at minimizing the false discovery rate . Second , peptides from PTPs represent a small fraction ( ∼8% ) of the analyzed mass spectra from pollinated styles . Thus , many additional PTPs could be masked by the disproportionate abundance of 15N-labeled peptides from stylar proteins , as suggested by the relatively low overlap in proteins identified among biological replicates ( ∼60% between any two; Fig . S1 ) . PTPs could also be masked if transcripts were translated in vivo from amino acid residues derived from the maternal ( 15N ) stylar environment . Third , Arabidopsis pollen expresses >7 , 000 transcripts , a large proportion of which are unique to the pollen tube [46] . Although the typically low correlation between transcript and protein abundance argues against a one-to-one correspondence of the underlying loci [62] , this again suggests that additional Mimulus PTPs await characterization . Our studies of the molecular evolution of M . guttatus PTPs: ( i ) identify the loci that may be targets of selective sweeps or balancing selection in a large outcrossing population of M . guttatus where sexual selection can be a potent evolutionary force on reproductive genes; ( ii ) test for the action of positive selection more broadly in adaptive divergence of PTPs among closely related members of the M . guttatus species complex; and ( iii ) address questions regarding rates of evolution among male plant reproductive genes in a broader genome-wide context . Our analyses of array-captured M . guttatus PTP exomes from Cone Peak identify the subset of these genes that are likely targets of selective sweeps or balancing selection . Coding regions of the 2 , 419 PTPs we were able to sequence via array capture harbor relatively high levels of polymorphism ( Fig . 2A ) , comparable to silent site diversity for a much smaller set of loci within natural populations of Arabidopsis lyrata [63] , one of the few outcrossing taxa with data available for comparison with our results . However , unlike previously studied populations of A . lyrata , the distribution of Tajima's D statistics calculated from M . guttatus PTPs harboring polymorphism at Cone Peak ( Fig . 2B ) is very close to that expected under theory for an ideal population in the absence of demographic forces such as bottlenecks [64] . Because we've assayed SNPS at ∼10% of IM62 CDSs genome-wide , this suggests demographic factors have not been a strong evolutionary force at Cone Peak and that the empirical distribution of Tajima's D can serve to identify PTPs that deviate from the expectation under neutrality . The significance threshold ( α = 0 . 05 ) set from the 2 . 5% tails of the empirical distribution and validated by coalescent simulations ( see File S3 ) identify 116 genes in total for which neutrality is rejected , corresponding to 58 PTPs with evidence of selective sweeps and 58 with evidence of balancing selection ( negative and positive D , respectively; File S3 ) . For the subset of PTPs inferred to be targets of selective sweeps , Fay and Wu's H provides further support for positive selection acting on 44 of these PTPs at Cone Peak . Significantly , the high degree of overlap ( 76% ) between these two complementary tests of selection utilizing different portions of the site frequency spectra that are more ( Tajima's D ) or less ( Fay and Wu's H ) subject to the influence of population growth also supports the view that our results are largely free from the influence of demographic history . Sweeps or balancing selection on these 116 PTPs are consistent with the action of sexual selection manifest among outcrossing M . guttatus plants at Cone Peak . Sexual selection could result most simply from a strictly male-male competition scenario , in which the haploid genotype of the pollen tube is the sole determinant of male performance . A straightforward prediction of this scenario is selective sweeps at the underlying loci in outcrossing M . guttatus populations such as Cone Peak . Alternately , pollen tube growth via maternal guidance mechanisms and molecules produced within the style and translocated to the pollen tube [47]–[50] provides the opportunity for direct female mediation of male-male competition ( female choice ) and for antagonistic coevolution between male and female ( sexual conflict ) [36] . Both forms of sexual selection are likely to be relaxed in selfers such as M . nasutus [35] , but should exert strong pressure in outcrossing M . guttatus populations such as Cone Peak . Under some female-choice scenarios , determinants of pollen function may be under balancing selection in outcrossers , whereas other scenarios predict frequent selective sweeps [35] , [36] . And because sexual conflicts may resolve differently , the underlying genes can be under either directional or balancing selection among different populations , a complex pattern known for fertilization proteins important in conspecific sperm precedence ( CSP ) [65] . Genes under sexual selection within outcrossing populations such as Cone Peak could give rise to reproductive barriers among divergent members of the M . guttatus species complex including M . nasutus via several mechanisms . First , if alleles are under sexual selection due simply to male-male pollen competition , faster growing pollen tubes carrying these alleles should outcompete heterospecific pollen from taxa lacking a history of strong pollen competition and resulting in a higher frequency of conspecific fertilizations . In the absence of more complex genetic interactions ( see below ) , this constitutes a strong barrier only between outcrossers such as M . guttatus and primarily self-pollinating taxa such as M . nasutus [25] and predicts that alleles from outcrossers can rapidly invade populations of self-pollinating taxa . There is good evidence that at least some of the transmission ratio distortion loci ( TRDL ) which contribute to unidirectional CPP between M . guttatus and M . nasutus harbor such simple pollen performance genes based on reciprocal backcrosses demonstrating distortion acts independent of the maternal style's genotype for these loci [39] . Because other members of the complex are also likely to experience lower rates of outcrossing than M . guttatus [66] , they could be a potent unidirectional barrier limiting gene flow . Second , if allelic variation for female pistil genes does influence the outcome of pollen competition , reproductive incompatibilities can arise between populations or taxa harboring divergent alleles at interacting reproductive genes as a form of Bateson-Dobzhansky-Muller ( BDM ) incompatibility . Sperm and egg fertilization proteins from marine invertebrates are well documented examples of this form of reproductive barrier [13] which constitute rare textbook examples of speciation genes [37] . Importantly , because cognate fertilization genes must co-evolve to maintain compatibility among interbreeding individuals within a population , their divergence driven either as a result of female preference or sexual conflicts can result in reproductive barriers among divergent populations or taxa which function without respect to mating system . Consistent with the idea that they may function in reproductive isolation between M . guttatus and M . nasutus , reciprocal pollinations show that M . nasutus styles do not support rapid growth of M . guttatus pollen tubes [25] and distortion for a subset of the TRDL underlying CPP between M . nasutus and M . guttatus is style specific [39] suggesting they harbor female modifiers of male function alleles expressed by pollen tubes . We also tested for positive selection on PTPs more broadly among representative yellow monkeyflowers for which raw genomic sequence reads were publicly available . These include a dune ecotype of M . guttatus along with 8 additional taxa of the M . guttatus species complex ( M . cupriphilus , M . dentilobus , M . glaucescens , M . micranthus , M . nasutus , M . nudatus , M . pardalis , and M . platycalyx ) and a closely related outgroup , M . tilingii . Of the 2 , 133 PTPs meeting our criteria for assembly , mapping , and codeml analyses ( see Materials and Methods ) , ∼2% show evidence of adaptive divergence under positive selection . There is almost no overlap between these 43 genes and the 116 with evidence of sweeps or balancing selection at Cone Peak – only one ( mgv1a018842m; File S3 ) is identified as under balancing selection as well as evolving under positive selection among yellow monkeyflowers , which is not a significant enrichment relative to PTPs as a whole ( one-tailed p-value = 0 . 327 from Fisher's exact test ) . The different sets of genes identified by these analyses are most likely due to the very different time scales for which the tests we employ have power to detect signatures of selection [67] . However , we cannot exclude the possibility that evolutionary forces acting among yellow monkeyflowers are largely distinct from those that function within populations , e . g . , reflecting selection pressures of pathogens as opposed to recurrent sexual selection among populations . Regardless , because a signature of positive selection from phylogeny-based tests of dN/dS ratios is a well established feature of the genes underlying barriers to reproduction [13] , we consider PTPs with evidence of adaptive divergence as additional candidates that may contribute to reproductive isolation between M . guttatus and M . nasutus , and perhaps more broadly among yellow monkeyflowers . We find that constituent proteins of the male gametophyte of yellow monkeyflowers evolve under markedly stronger evolutionary constraint than most other protein coding sequences in the genome ( Fig . 3A , B ) . Non-synonymous substitutions ( dN ) for 2 , 262 PTP orthologs average only about half that calculated for the remaining 23 , 288 protein coding sequences ( CDSs ) calculated from pairwise differences between M . guttatus and M . tilingii ( 0 . 005 -vs- 0 . 011 , respectively; p<0 . 001 ) despite comparable levels of synonymous substitution ( dS = 0 . 067 and 0 . 070 , respectively; p = 0 . 069 ) . Similarly , dN/dS ratios averaged across sites ( model M0 ) of 2 , 133 PTPs for which we were able to map and assemble orthologs from multiple members of the M . guttatus species complex are less than half that of the remaining 18 , 973 CDSs ( dN/dS = 0 . 10 and 0 . 23 , respectively; p<0 . 001 ) . This pattern persists when we remove from the comparison those CDSs that are shared between both pollen tube and style proteomes ( Fig . 3B ) . Our finding that genes with male function in plants evolve under strong constraint is in contrast with longstanding evidence across animal groups that male function genes evolve more rapidly than others [68] . For example , genes expressed specifically in male reproductive tissues or with male-biased expression are among the fastest evolving protein coding sequences between the Drosophila melanogaster and D . simulans lineages [69] . Previous smaller scale studies of pollen genes finding evidence of rapid divergence under positive selection ( e . g . , [70] ) had hinted that similar trends might be found for genes with male function in plants . While we too find rapid adaptive divergence for a small subset of PTPs , at least in Mimulus genes with male function clearly do not fit the pattern predicted from animal studies . In part , this may simply reflect the greater biological complexity of pollen tubes . The male gametophyte represents a distinct life-history stage of plants that is semi-autonomous with respect to many aspects of cellular growth and respiration [49] , [59] , [60] , suggesting a higher proportion of PTPs could be constrained due to essential house-keeping or developmental regulatory function . Importantly , the evolutionary dynamics of haploidy versus diploidy [71] could contribute strongly to this effect . Unmasking of recessive alleles during pollen tube growth in competitive pollinations should result in more rapid purging of slightly deleterious non-synonymous substitutions for genes expressed in haploid male gametophytes , consistent with lower levels of dN and smaller dN/dS ratios for PTPs relative to genome-wide averages ( Fig . 3A , B ) . Under this scenario , slower rates of evolution for plant male function genes reflect the efficiency of selection acting during the male gametophytic stage as opposed to greater essentiality or lower potential for positive selection per se relative to male function genes in animals . We identify a total of 159 PTPs that are candidate CPP genes based on theory and empirical evidence identifying sexual selection within outcrossing M . guttatus as the likely driving force of pollen competition in crosses with M . nasutus , and divergence under positive selection as a common feature of genes underlying reproduction among closely related species such as members of the M . guttatus species complex ( File S3 ) . As with PTPs generally ( Fig . 1C , D ) , many of these candidates appear to function in basic aspects of cellular growth and metabolism , with the majority yielding GO terms associated with binding or catalytic activity ( Fig . 4A ) . Interestingly , because there is no evidence of GO term enrichment among candidates and they span the range of relative protein abundances ( NSAF ) observed among PTPs generally , their distinguishing features appear to be limited to evidence of sweeps , balancing selection , and adaptive divergence . To our knowledge , pollen tube phenotypes have been identified in other plant model systems for homologs of just three of these 159 PTPs . These include an Arabidopsis thaliana homolog ( AT1G08660 ) of mgv1a005647m , a glycosyl transferase for which mutants are known to show reduced pollen tube germination and growth rates [72] , along with an A . thaliana homolog ( AT1G14420 ) of mgv1a006379 , a pectate lyase-like gene first characterized in tomato ( LAT59 ) and notable for studies of it's pollen-specific promoter element [73] . In addition , they include an M . guttatus homolog ( mgv1a000017m ) of A . thaliana KINGY POLLEN ( KIP; AT5G649680 ) [74] . KIP is a large secreted protein , and mutants exhibit periodic growth arrest followed by axis reorientation , thought to be due to disruption of secretory trafficking at the Golgi membrane based on studies of homologs in corn [75] , where it is known to influence pollen tube growth rates and pollen competition . Because the pollen-specific TRDLs underlying CPP between M . guttatus and M . nasutus manifest as quantitative effects on genome transmission in F1 hybrids and backcross populations , and often exhibit broad peaks [39] , their characterization by positional cloning would be extraordinarily laborious . Instead , we can now ask a relatively straightforward question in the context of higher resolution segregation studies focusing on our 159 candidate genes: does a candidate gene exhibit more extreme transmission distortion in F2 and backcross populations of M . guttatus×M . nasutus than increasingly distant flanking genes , and is this distortion pollen-specific ? If a given candidate is at the core of a region of local pollen-specific distortion , reverse genetic confirmation of its effects on transmission can rapidly follow as part of future studies . Thus our screen for candidate genes promises to accelerate the molecular characterization of the many loci underlying a complex , common , and poorly understood species barrier in flowering plants . The increasing availability of genomic data from non-model taxa such as yellow monkeyflowers presents opportunities to unravel the complex genetic basis of pre-zygotic reproductive barriers . However , bringing this data to bear on questions of reproductive isolation can be daunting given the complexities of linking variation in genomic-scale data with particular reproductive barriers that have a complex genetic basis . We demonstrate one approach to this challenge by first identifying the constituent proteins of the M . guttatus pollen tube within the maternal style using 15N labeling and shotgun proteomics – these male function genes include the subset of loci responsible for pollen competition underlying conspecific pollen precedence ( CPP ) between M . guttatus and the sister taxon M . nasutus . Then , because sexual selection within outcrossing M . guttatus populations and adaptive divergence among closely related members of yellow monkeyflowers are expected features of the genes contributing to CPP , we test for these signals proteome-wide . Using this approach allows us to identify 159 candidate genes for CPP , a common reproductive barrier between closely related plant taxa including species of Mimulus . Though several of these genes have known functions or exhibit mutant phenotypes in model species that may be of relevance to pollen competition , the challenge now is to explicitly test their role in reproductive isolation via fine-mapping of pollen-specific distortion around candidates followed by functional and genetic confirmation .
An inbred strain of M . guttatus ( IM62 ) used previously for construction of genetic linkage maps [38] , [40] and genome sequencing [17] was isotopically labeled using a modified hydroponic cultivation method . IM62 seeds were germinated on potting soil , and after two weeks transferred to polystyrene plugs placed in 0 . 5 L aerated containers filled with hydroponic media which contains 15N-potassium nitrate ( KNO3; Cambridge Isotope Laboratories , Andover , MA ) as the sole source of nitrogen , as in [52] . Plants were grown on 15N media until they flowered ( approx . 12–16 weeks post germination ) and used as pistil parents in crosses to unlabeled ( 14N ) IM62 plants . For crosses , unlabeled pollen was collected from 40 flowers 2–3 days post anthesis and used to hand pollinate 30 virgin flowers on each of 3 separately 15N labeled IM62 plants ( full replicates ) . Pollinated 15N labeled pistils were dissected 3 hours after pollination ( Fig . 1A ) , removing both stigma and ovaries , and styles pooled within replicates . Preliminary experiments showed pollen tubes had grown approximately half the length of styles after 3 hours ( Fig . 1B ) . From each 15N labeled maternal plant , an equal number of unpollinated flowers were simultaneously collected , dissected , and pooled as negative controls for pollen tube protein identification and to estimate percent 15N incorporation ( see below ) . Proteins from both pollinated and unpollinated negative control styles were identified using a shotgun proteomic approach utilizing tandem mass spectrometry ( MS/MS ) . Total protein was extracted from pooled pollinated and unpollinated styles for each of the 3 replicate 15N labeled plants as in [57] , followed by tryptic digestion of the resulting approximately 10 µg of protein as in [15] . Digested proteins ( 5 µg each ) were then analyzed via multi-dimensional protein identification technology ( MudPIT ) as in [15] using a 13 step ( 0 to 5 M ammonium acetate ) salt elution . In addition , proteins were analyzed by reversed-phase HPLC ( 5 technical replicates each ) . For reversed-phase analyses , digested proteins ( 1 µg per technical replicate ) were injected into a 75 µm internal diameter capillary column packed with 30 cm of Jupiter C12 reversed-phase resin , peptides eluted in a 4 hour water∶acetonitrile gradient and mass spectra acquisition handled exactly as for MudPIT analyses . The acquired tandem mass spectra were searched against a database containing all IM62 protein coding transcripts ( JGI release v1 . 1; http://www . phytozome . org/ ) , proteins of common contaminants ( e . g . , trypsin , keratin ) , and a shuffled decoy database using a parallelized implementation of Sequest [76] . Search databases and Sequest search parameters are available for public download ( https://sites . google . com/a/uw . edu/maccoss/home/supplementary_data/ ) . The program IDPicker [77] , implemented as part of a data analysis pipeline , was used to filter the peptide identifications and assemble peptides and proteins . Protein identification filters ( ≥1 unique peptide per protein , per peptide q-value≤0 . 002 ) were selected to produce protein identifications with an approximate false discovery rate ( FDR ) ≤2% [61] . Pollen tube proteins ( PTPs ) were then defined as those found uniquely within samples from pollinated , 15N labeled styles and absent from all negative controls ( unpollinated , 15N styles ) . Tandem mass spectra from negative controls were also used to measure the extent of 15N labeling of IM62 plants . Equal amounts of digested protein from the 3 replicate negative controls was pooled and analyzed as above for reversed-phase MS/MS , but employing a 3 hour water∶acetonitrile gradient and high resolution mass spectrometry in order to estimate the percent 15N incorporation among peptides using the Hardklör algorithm [43] . The constituent proteins of the M . guttatus pollen tube proteome were characterized using several quantitative and descriptive measures . The relative abundance of each PTP was inferred using the spectral counting method of [[78]; normalized spectral abundance factor , NSAF] and averaged across MudPIT and reversed-phase analyses from which the protein was identified . Putative functions of PTPs were inferred by automated annotation of all IM62 protein coding transcripts using the Blast2GO annotation tools with default parameters [79] . Fisher's exact test ( p≤0 . 05 ) was then used for statistical comparison of functional term enrichment [79] between all PTPs and all IM62 coding transcripts , or a subset of PTPs identified in our molecular evolution studies ( see below ) . Similarity among constituent genes of the M . guttatus pollen tube proteome , the Arabidopsis thaliana pollen proteome [45] , and the A . thaliana pollen tube transcriptome [46] were inferred from blastp scores [80] from which putative orthologs were identified based on a minimum expectation value of e−20 . To test for evidence that PTPs may also function in female reproductive structures , we also carried out MS/MS of style proteins . Style proteins were identified and characterized as for PTPs , but using pooled unpollinated styles from a single unlabeled ( 14N ) maternal plant . Styles were harvested from virgin flowers within 24 hours of receptivity ( gauged by petal opening ) . Though flowers were not emasculated in the bud , protogyny significantly reduces opportunities for pollen contamination . Style protein digestion , peptide separation , and mass spectra acquisition were carried out as above for MudPIT analyses of PTPs , and acquired tandem mass spectra were searched and assemble into peptides and proteins in an identical fashion as previously ( ≥1 unique peptide per protein , per peptide q-value≤0 . 002 , approximate protein FDR≤2% ) . The relative abundance of each style protein was estimated ( NSAF ) . Because sexual selection underlying CPP functions as a strong evolutionary force principally at the population level where pollen competition may be high [35] , we sequenced PTPs from individuals within a natural population of M . guttatus at Cone Peak , Oregon , U . S . A . This population consists of several thousand synchronously flowering and primarily outcrossing annual plants that have been studied previously [e . g . , [21] , [81] , [82]] , and is adjacent to the Iron Mountain population from which the IM62 strain used in genome sequencing and genetic mapping studies was derived . A total of 50 individual plants were collected at random on July 11–12 , 2011 , from locations spanning the population ( ≥4 m between plants; centroid N 44°24 . 472′W 122°08 . 111′ , elevation 1 , 580 m ) , transported in 50 ml Falcon tubes containing moist potting soil to the University of Washington greenhouses ( Seattle , WA ) , and transplanted into 10 cm pots where they were grown 3 weeks under long day conditions in order to obtain sufficient vegetative tissue for genomic DNA extractions . Individual plant's vegetative tissue for all surviving plants ( 96% survivorship ) were harvested and stored at −80°C . Coding sequences of PTPs from 28 randomly selected Cone Peak individuals were obtained by array capture and high throughput sequencing . Genomic DNAs were extracted from frozen tissue using the Plant DNeasy Maxi Kit ( Qiagen , Valencia , CA ) and concentrated using 30K nucleic acid concentration columns ( Millipore , Billerica , MA ) per manufacturers guidelines . Prior to capture , sequencing libraries were constructed as in [83] , except that a unique bar-coded reverse primer was used for each individual in place of the SLXA_Pair_Rev_Amp primer ( 5′_CAAGCAGAAGACGGCATACGAGATNNNNNNNNCGGTCTCGGCATTCCTGCTGAACCG_3′ ) . This allowed us to pool 14 barcoded individuals for capture and sequencing . A custom Agilent array ( 244K format; Agilent , Santa Clara , CA ) was designed with probes tiling all exons of the 2 , 554 identified PTPs ( Table S1 ) . The probe sequences were pulled from the IM62 reference genome and designed according to [84] . A total of 20 ng of pooled barcoded DNA ( 1 . 4 ng per individual ) was then captured using the protocol described in [83] . Array-bound DNAs were eluted with two sequential additions of H2O ( 1 ml , 95°C each ) . Each elution was ethanol precipitated and re-suspended in 20 µl H2O , and then PCR amplified as in [83] with forward ( 5′_AAT GATACGGCGACCACCGAGATCT _3′ ) and reverse ( 5′_CAAGCAGAAGACGGCATACGAGAT_3′ ) PCR primers . We generated one lane of 76-bp paired-end reads for each of the two pooled sequencing libraries ( from the first elution ) on an Illumina Genome Analyzer IIx according to the manufacturer's instructions . Sequencing reads from different individuals were divided by their barcode sequence and individually aligned to the IM62 reference genome using BWA v0 . 5 . 9 [85] with parameters that were optimized for mapping highly diverged sequences . These modifications included raising the maximum number of differences allowed in the alignment of both the seed region ( raised from 2 to 4; -k 4 ) and the entire read ( raised from 4 to 10; -n 10 ) . The alignments were sorted and filtered for duplicates using Picard 1 . 15 ( http://picard . sourceforge . net ) . GATK [86] was then used to perform local indel realignment and SNP calling across all 28 individuals simultaneously with minimum base and mapping qualities of 20 [87] . SNP calls for each individual were then required to have a minimum genotype quality of 30 and a minimum read depth of 10 . For each gene , we calculated standard measures of nucleotide diversity per site ( expected heterozygosity ) and skews in the site frequency spectra [Tajima's D; [64]] after excluding sites with missing data and those that violate the infinite sites model [88] . In order to infer the subset of PTPs that may have been targeted by selective sweeps or balancing selection at Cone Peak , we first utilized the empirical distribution of Tajima's D calculated from exome-captured PTPs to establish 95% confidence intervals defining a neutral expectation . Because population demographic forces affect all loci genome-wide , Tajima's D values for PTPs in the 2 . 5% upper and lower tails are likely to reflect locus-specific selective forces assuming loci are unlinked ( balancing selection and sweeps , respectively ) . Such use of an empirical distribution drawn from population genomic data provides a straightforward and robust means of identifying potential targets of positive selection [89] . Next , in order to provide statistical support for these cutoffs we generated 10 , 000 replicate datasets for each PTP in the upper and lower 2 . 5% tails of the distribution under a neutral model in the MS program [90] implemented in the DNAsam computer package with default simulation parameters [91] . P-values were calculated for each of these loci as the proportion of 10 , 000 coalescent simulations with a Tajima's D value more extreme than the observed value . Finally , to validate the subset of PTPs identified as potential targets of selective sweeps , we calculated Fay and Wu's H [92] . These two statistics use different portions of the frequency spectrum to infer sweeps , with Fay and Wu's H relying on high frequency alleles as determined via comparison with an outgroup taxon . While population growth can potentially confound inference of sweeps via Tajima's D , Fay and Wu's H is generally robust to this aspect of population demographic history . We calculated Fay and Wu's H and associated p-values for each PTP in the tails of the empirical distribution as above for Tajima's D , utilizing each of two outgroup taxa ( M . tilingii or M . cupriphilus; see below ) to infer the ancestral state at variable sites . We next tested for evidence of positive selection acting more broadly on PTPs by examining their divergence between Mimulus species . We downloaded publicly available paired-end Illumina sequences from NCBI's Sequence Read Archive ( SRA ) for 10 accessions of yellow monkeyflowers . These include a dune ecotype of M . guttatus along with 8 additional taxa of the M . guttatus species complex ( M . cupriphilus , M . dentilobus , M . glaucescens , M . micranthus , M . nasutus , M . nudatus , M . pardalis , and M . platycalyx ) , and a closely related outgroup , M . tilingii [SRX030540-1 , SRX030973-4 , SRX116529 , and SRX142372-6; [17] , [18]] . Read mapping and variant calling were preformed in the same way as described for the Cone Peak individuals . Coding sequences were extracted from the genotype calls and required to contain a minimum of 75% unambiguous bases per gene . We examined the evolutionary forces acting among PTPs and other CDSs genome wide by comparing the rate of non-synonymous ( dN ) with synonymous ( dS ) nucleotide substitutions . The ratio ( dN/dS , or ω ) constitutes an index of selection where ω<1 is consistent with purifying selection , ω = 1 indicates neutral evolution , and ω>1 is consistent with positive selection ( i . e . , adaptive diversification ) . We first calculated pairwise estimates of dN and dS for each CDS between IM62 and the outgroup M . tilingii to examine their respective distributions across the species complex using codeml in the PAML computer package [93] . We then estimated dN/dS directly from multiple sequence alignments containing a minimum of 3 taxa with the one-ratio sites model in codeml [M0; [94]] . Finally , we explicitly tested for positive selection acting on individual PTP CDSs using nested sites models that either allow for positive selection at a subset of codons ( M8 ) or constrain ω≤1 ( M8a ) . Neighbor joining ( NJ ) trees were constructed for each CDS to avoid potential confounding effects from introgression among species . We assessed statistical significance by performing likelihood ratio tests with a χ2 approximation to calculate P-values .
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Barriers to reproduction are necessary for generating new species . Little is known about the genes underlying reproductive barriers , particularly those that function prior to fertilization , but their identity is of great interest as they offer insight into the genetic mechanisms and evolutionary forces generating biological diversity . In this work , we use an emerging plant model system for speciation studies ( yellow monkeyflowers , species of Mimulus ) to identify genes that might influence the relative competitive abilities of male pollen from the same versus different species within the maternal flower's style . This is a common reproductive barrier among plant taxa known as conspecific pollen precedence ( CPP ) , and is analogous to sperm competition during animal fertilization . We first identify the pollen proteins that are found within the style where pollen competition occurs , and then screen these for evidence that may indicate which genes have been targets of pollen competition ( a form of sexual selection among individuals of a population ) or adaptive diversification among species of yellow monkeyflowers ( a common feature of genes underlying reproductive barriers ) . Our evolutionary analyses identify 159 candidates that may function in reproductive isolation of yellow monkeyflowers , and provide some of the first broad perspectives on evolution of plant reproductive genes .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2013
|
Selection on Plant Male Function Genes Identifies Candidates for Reproductive Isolation of Yellow Monkeyflowers
|
Choosing the right nutrients to consume is essential to health and wellbeing across species . However , the factors that influence these decisions are poorly understood . This is particularly true for dietary proteins , which are important determinants of lifespan and reproduction . We show that in Drosophila melanogaster , essential amino acids ( eAAs ) and the concerted action of the commensal bacteria Acetobacter pomorum and Lactobacilli are critical modulators of food choice . Using a chemically defined diet , we show that the absence of any single eAA from the diet is sufficient to elicit specific appetites for amino acid ( AA ) -rich food . Furthermore , commensal bacteria buffer the animal from the lack of dietary eAAs: both increased yeast appetite and decreased reproduction induced by eAA deprivation are rescued by the presence of commensals . Surprisingly , these effects do not seem to be due to changes in AA titers , suggesting that gut bacteria act through a different mechanism to change behavior and reproduction . Thus , eAAs and commensal bacteria are potent modulators of feeding decisions and reproductive output . This demonstrates how the interaction of specific nutrients with the microbiome can shape behavioral decisions and life history traits .
The appropriate intake of nutrients has a major beneficial impact on health and lifespan [1–3] . The level of dietary protein intake has emerged as a key determinant of overall mortality , fecundity , and lifespan in species ranging from humans [4] to mice [5] and Drosophila [6–9] . Accordingly , animals , including humans , are able to direct food choice in order to tightly control protein intake [2 , 3 , 10–13] . Despite the striking physiological and behavioral impact of nutritional proteins , how animals direct feeding decisions to ensure protein homeostasis is not understood . A major obstacle in identifying the rules governing food choice is the nutritional complexity of natural foods , which hinders the discovery of the nutritional variables controlling feeding decisions . In Drosophila melanogaster , yeast is thought to cover the protein as well as most other noncaloric nutritional requirements [7] . In adult females , yeast appetite is driven by two main internal states: mating and lack of yeast [12–15] . The molecular and circuit mechanisms leading to an increase in yeast appetite upon mating have been extensively characterized . During copulation , the male-derived Sex Peptide is transferred to the female and acts on the neuronal Sex Peptide Receptor , leading to the silencing of a postmating neuronal circuit , consisting of SPSN/SAG/octopamine components , which projects to the central brain to change feeding preference from sugar to yeast [12 , 14 , 16] . Besides mating , the other known determinant of protein intake is removal of yeast from the diet , which leads to a strong compensatory appetite for yeast [12] . The mechanisms underlying this homeostatic change in appetite are less well understood . This is partially due to the fact that yeast is a complex food containing different nutrients , including amino acids ( AAs ) , carbohydrates , vitamins , and sterols [17 , 18] . However , it is still unknown which nutrient ( s ) , when absent , triggers flies to ingest yeast . Identifying the mechanisms controlling protein homeostasis in Drosophila requires untangling this nutritional complexity . The interaction of microbiota with ingested nutrients has emerged as a major determinant of health and disease , including obesity [19–24] . Commensal bacteria have also been proposed to affect a wide array of brain functions [25–29] ranging from bulk food intake [30] to anxiety [31–33] , neurodevelopmental disorders [34] , and social behavior [35] . Despite being an intense field of research , the importance of microbe–nutrient interactions in influencing behavior remains poorly understood . In vertebrates , this task is especially challenging given the complexity of their microbiota and the large set of nutritional parameters that could influence their function . Furthermore , in the context of nutrition , research on microbiota has mainly focused on their role in carbohydrate homeostasis [21 , 36] . More recently , however , the importance of commensal bacteria in controlling growth [37–39] and in protecting children from malnutrition symptoms [40] indicate that the microbiome could also play a pivotal role in protein homeostasis . However , the importance of commensals in protein homeostasis and in directing food choices has not been directly addressed . In this study we show that yeast and AA preferences are driven by dietary deprivation from essential AAs ( eAAs ) . While the absence of a single eAA is sufficient to induce a potent yeast appetite , removal of other important nutrients from the diet does not lead to an increase in yeast preference . The fly , however , is not specialized in detecting the identity of the missing AA . Flies rendered auxotrophic for a nonessential AA ( neAA ) display a strong yeast appetite upon deprivation of this artificially engineered eAA . Furthermore , we show that the presence of commensal bacteria abolishes the yeast appetite and the strong decrease in egg laying induced by the removal of eAAs . Commensal bacteria also have a strong phagostimulatory effect that is likely to aid the replenishment of gut bacteria . Using gnotobiotic animals , we show that the effect of commensals on yeast appetite is due to the concerted action of Acetobacter pomorum with Lactobacilli . Finally , we test the hypothesis that commensal bacteria alter feeding decisions by providing eAAs to the host . We find , however , no evidence that the decrease in eAA levels induced by dietary deprivation is ameliorated by the presence of commensal bacteria , suggesting that they may use a different mechanism to alter food choice . Our study identifies two key components driving food choice in Drosophila: eAAs and the gut bacteria species Acetobacter pomorum and Lactobacilli . Furthermore , we provide initial insights into their action on the host , highlighting the power of Drosophila for identifying key determinants underlying complex nutritional–microbial–behavioral interactions .
Yeast deprivation leads to a strong compensatory appetite for yeast [12] ( Fig 1B ) . Given the complexity of this resource , it has not been possible to identify the nutrients that , when absent , trigger flies to ingest yeast . To answer this question , we decided to manipulate each nutrient present in yeast independently using a chemically defined ( holidic ) diet [7] ( Fig 1A ) and study their effects on feeding decisions using a two-color food choice assay [12 , 41] . The holidic medium is able to suppress yeast appetite to the same extent as a yeast-based medium ( Fig 1B and 1C ) , supporting the idea that it provides the necessary nutrients to support adult behavior [7] . Removal of AAs from the holidic medium induced a potent yeast appetite , indistinguishable from that observed upon yeast deprivation ( Fig 1B and 1C ) . Removing folic acid , metals , nucleic acids , lipids , sterols , or vitamins , however , did not lead to a significant increase in yeast appetite ( Fig 1C ) . This effect stands in strong contrast to the clear effects on lifespan and egg production of removing these nutrients [7] . To identify the nutrients that flies select when deprived of AAs , we used the holidic diet in the choice paradigm . We gave the flies a choice between a holidic base diet containing sucrose without AAs and one containing AAs but no carbohydrates . AA deprivation shifted flies' preference from the sucrose-containing option towards the AA-containing option ( Fig 1D ) . This shift in preference was specific to AAs since it was abolished by removal of AAs from the choice medium , whereas removing any other class of nutrients left the shift intact ( Fig 1D and S1A Fig ) . These results suggest that similarly to yeast deprivation [42] , upon AA deprivation , flies specifically select a diet containing AAs . In our paradigm , the decision to switch from eating sucrose to yeast or AAs is therefore guided by the absence of AAs , while the absence of other physiologically important dietary nutrients does not lead to an increase in yeast or AA appetite . AAs can be broadly classified as either essential or nonessential . neAAs can be synthesized by the animal , allowing animals to be largely independent from dietary uptake of these important building blocks [43] . It is currently unclear whether animals sense these two types of AAs differently and if they have different effects on nutrient choice [44 , 45] . We tested this by manipulating AAs of each type independently . Removal of all eAAs from the diet induced a yeast ( Fig 1E ) and AA appetite ( Fig 1F ) that were indistinguishable from that observed upon removal of all AAs . The complete removal of neAAs , however , had no effect on nutrient choice ( Fig 1E and 1F ) . Given that we adjust the total level of AAs to maintain a constant amount of nitrogen in the diet , these results also show that it is the identity of the AAs and not the nitrogen level in the diet that leads to changes in food choice . Intriguingly , AA deprivation induced a preference for both eAAs and neAAs , suggesting that the phagostimulatory power of AAs is not correlated with their nutritional importance , as indicated by previous studies [42] ( S1B Fig ) . Taken together , these data strongly indicate that eAAs are specific mediators of protein and AA appetite and highlight the ability of animals to efficiently buffer the absence of neAAs . Behavioral [45] , physiological [9] , and molecular studies [46] have suggested that different single AAs can vary widely in their potency to suppress protein appetite and to activate nutrient-sensitive pathways . We therefore took advantage of the unique possibility to manipulate single dietary AAs afforded by the holidic diet to remove every eAA individually from the diet and test the effect on food choice . Strikingly , removal of any eAA was sufficient to induce a clear increase in yeast choice ( Fig 2A ) . The extent to which they did so did not differ , suggesting that each eAA has a similar impact on food choice . Furthermore , we quantified the effect of removing specific AAs from the diet on the intake of sucrose and yeast extract using a method to quantify food intake [47] ( the capillary feeder [CAFE] assay; Fig 2B ) . Consistent with our results using the two-color assay , removal of either all AAs or single eAAs ( arginine or valine ) led to a specific increase in yeast extract intake without affecting carbohydrate intake ( Fig 2B ) . In agreement with previous reports [16] , these data indicate that the changes in food choice induced by AA deprivation in the two-color choice assay are due to an increase in yeast appetite and not to a decrease in sucrose intake . They further indicate that single eAAs are potent and specific nutritional modifiers of protein intake , highlighting their unique importance in controlling food choice . Animals can synthesize neAA in order to compensate for their absence from the diet . For example , tyrosine ( Tyr ) can be synthesized from phenylalanine ( Phe ) through the action of phenylalanine hydroxylase , which in Drosophila is encoded by the Henna gene ( Fig 3A ) [48] . In humans , mutations in phenylalanine hydroxylase cause phenylketonuria , the most common metabolic disease [49 , 50] . Patients with phenylketonuria suffer from elevated Phe and low Tyr titers , leading to severe complications including neurological and behavioral symptoms [51] . Strict adherence to a diet low in Phe and high in Tyr allows patients to lead an asymptomatic life , highlighting the impact of dietary AAs on human health [52] . We mimicked the genetic lesion leading to phenylketonuria by knocking down the Henna gene ubiquitously ( S2A Fig ) , thus transforming Tyr from a neAA to an eAA . This allowed us to test if the capacity to homeostatically trigger changes in food choice is related to the specific identities of the ten eAAs or if it can be driven by low levels of any AA . While removal of dietary neAAs in control animals did not lead to the induction of a yeast appetite , the same dietary manipulation in Henna knockdown animals led to a strong yeast appetite ( Fig 3B and S2B Fig ) . This increased yeast appetite was indistinguishable from that observed upon removal of all AAs . Supplementing the diet lacking neAAs with Tyr suppressed the preference of flies for yeast in a dose-dependent manner , indicating that the phenotype was specifically due to an acute lack of Tyr and not to other detrimental effects of our genetic manipulation ( Fig 3B ) . Importantly , the addition of proline , a neAA which is not synthesized by phenylalanine hydroxylase , did not suppress the Henna phenotype , further emphasizing the specificity of the metabolic manipulation ( S2B Fig ) . These results strongly suggest that flies can detect the absence of any limiting AA independent of their specific identity ( eAA versus neAA ) . In mammals , neAAs are mainly synthesized in the liver [53 , 54] , and it is thought that in insects , the fat body fulfills a similar role [55–57] . We tested the importance of the fat body in guiding nutrient choice by interfering with the ability of this organ to synthesize Tyr . Knockdown of Henna using a fat body driver Cg-Gal4 rendered the animal sensitive to the absence of dietary neAAs , with induction of a strong yeast appetite ( Fig 3C ) . Henna knockdown in neurons or trachea , in contrast , did not change the behavioral sensitivity of flies to removal of all neAAs ( S2C Fig ) , indicating that the effect observed with the fat body manipulation is tissue specific . However , Cg-Gal4 has also been shown to drive expression in hemocytes [58] . It is thus possible that this cell type also contributes to Tyr synthesis and the observed behavioral phenotype . Taken together , these data further demonstrate that AAs , be they dietary or endogenously synthesized , are able to control yeast appetite . Furthermore , our data indicate that biosynthetically active organs are important regulators of food choice , suggesting that genetic metabolic conditions such as phenylketonuria could have effects on aspects of behavior such as nutrient-specific appetites . Mounting evidence indicate that commensal bacteria are important determinants of how nutrients are utilized [59 , 60] . As such , they modulate a large set of nutrient-sensitive traits . However , whether commensals influence the selection of specific dietary nutrients is currently unknown . We therefore set out to test the effect of commensals on nutrient choice in Drosophila . Importantly , the flies used in our experiments had a very low baseline gut microbe load ( S3 Fig ) . This is likely due to the use of sterile media and the fact that upon serial passage to new food , adult flies lose a large part of their microbiota [61] . To test the effect of the microbiota on behavioral protein homeostasis , we removed one eAA ( histidine [His] ) from the holidic diet to increase the flies' preference for yeast and examined if they would show alterations in food choice when treated with a controlled microbiota ( Fig 4A ) ( pure culture of five Drosophila gut bacteria strains: Lactobacillus plantarumWJL [62] , Lactobacillus brevisEW [62] , Acetobacter pomorum [62] , Commensalibacter intestiniA911T [62] , and Enterococcus faecalis [63] ) . Strikingly , in contrast to control flies , bacteria-treated flies did not show an increased yeast appetite upon His removal ( Fig 4B ) . The effect of commensals was not limited to His but suppressed yeast appetite upon removal of any of the ten tested eAAs ( S4 Fig ) . The effect of commensals on food choice was so strong that the flies with a reconstituted microbiome were even buffered against the removal of all eAAs from the diet ( Fig 4C ) . To test if the bacteria were merely acting as food or if they needed to be metabolically active , we tested the effect of inactivated bacteria on food choice . Inactivation abolished the ability of the bacteria to alter feeding decisions , suggesting that their activity is essential to drive the change in behavior ( Fig 4B ) . The behavioral effect was specific to commensal bacteria , as it was not observed when a non-commensal bacterium ( Escherichia coli ) was used in the experiments ( S5A Fig ) . That bacteria do not simply act as food is further supported by the fact that even when diluted , they are able to lower the preference for yeast ( Fig 4D ) . Furthermore , to show that the effect of the bacteria is not confined to flies fed on the synthetic diet , we pretreated flies with decreasing amounts of yeast to induce a yeast appetite . As expected , flies fed with decreasing amounts of yeast showed an increase in yeast preference ( S5B Fig ) . Similarly to the effect on AA-deprived flies , bacteria pretreatment reduced the yeast appetite of yeast-deprived flies when compared to non-pretreated controls ( S5B Fig ) . This indicates that the effect of commensal bacteria on food choice is generalizable to ecologically relevant AA sources . Commensals are therefore strong modifiers of food choice behavior by buffering the animal from the effect of dietary lack of eAAs . Given the strong effect of commensals on food choice , we set out to test if they would also be able to suppress the food choice phenotype in flies which are impaired in Tyr synthesis . Surprisingly , addition of the five bacteria to the diet was not able to suppress the yeast appetite induced by neAA deprivation in Henna knockdown flies , while Tyr supplementation was able to suppress this appetite ( Fig 4E ) . The bacteria mix was still able to reduce the yeast appetite induced by His deprivation in Henna knockdown flies , indicating that the bacteria are effective in this genetic background ( S6 Fig ) . This indicates that the microbiota exerts its effect specifically in the context of eAA but not neAA depletion . This experiment also strongly suggests that the commensal pretreatment does not alter food choice in an indirect way ( e . g . , by suppressing the ability of flies to choose yeast or only increasing their preference for sucrose ) and that the effect of the bacteria is not due to them serving as food , as these effects should also lead to a decreased yeast preference index in these flies . Commensal bacteria are therefore strong modifiers of food choice specifically in the context of eAA deficiencies . Stem cell proliferation and differentiation is limited by the availability of eAAs [64–66] . In Drosophila this is most evident in the context of egg production , in which depletion of eAAs strongly reduces egg laying [7 , 9] . We therefore tested if commensals would also be able to affect egg laying . As shown above , depriving animals of three different eAAs ( His , Ile , or Val ) led to the induction of yeast appetite , which was strongly suppressed in animals pretreated with commensals ( S4 Fig ) . Removal of any of these three eAAs significantly decreased egg laying when compared to the complete holidic diet ( Fig 4F ) . Flies treated with the five bacteria , however , laid a significantly higher number of eggs in the context of a diet without single eAAs when compared to the flies without bacterial pretreatment ( Fig 4F ) . The microbiota is therefore not only able to buffer the effect of removing eAAs in the context of food choice but also in terms of physiological traits such as the reduction of egg laying triggered by removing one eAA . Given the importance of fecundity for the fitness of the animal , this effect therefore suggests that in the adult fly the host–bacteria interaction is mutualistic . To separately analyze the effects of the microbiome on yeast and sugar feeding , we chose to use the flyPAD assay [67] . Furthermore , to fully control the microbial conditions of the experiments , we performed the flyPAD experiments starting with flies kept in an axenic state , which ensured that our microbiome reconstitutions resulted in gnotobiotic flies . Flies kept on a full holidic diet and pretreated with the five bacteria did not show an increase in yeast feeding when compared to the germ-free controls ( Fig 5A ) , but they showed an increase in sucrose feeding ( Fig 5B ) . The sucrose effect is reminiscent of previous reports that the gut microbiota can increase food intake in flies [60] . In agreement with the data generated using the CAFE assay , both His and Ile deprivation led to a specific increase in yeast feeding in the axenic flies ( Fig 5A ) , while sucrose feeding was unaltered ( Fig 5B ) . Compared with germ-free flies , the gnotobiotic flies pretreated with the five gut bacteria showed a highly significant decrease in yeast feeding , corroborating the hypothesis that the microbiota suppresses yeast appetite ( Fig 5A ) . In contrast to the effect on yeast appetite , the increase in sugar feeding observed in the flies treated with bacteria was more variable . While a significant effect was observed in the His-deprived flies , Ile-deprived flies did not show an increase in sucrose feeding ( Fig 5B ) . These data support the idea that commensal bacteria specifically change food choice by decreasing yeast appetite in eAA-deprived flies . It has been indicated that the microbiota competes with their host for the availability of sugars in the diet [36] . To test this hypothesis and rule out that the effect on yeast choice is due to the observed increase in sugar intake , we increased the amount of sugar in the holidic diet . Adding increasing amounts of sucrose to the diet decreased the sugar appetite , suggesting that the bacteria were indeed reducing the sugar available to the fly from the diet ( Fig 5D ) . Importantly , neither the levels of sucrose in the diet nor the level of sugar feeding affected the yeast appetite , showing that these macronutrient appetites are independently regulated ( Fig 5C and 5D ) . Therefore , while a decrease in the sugar content of the food could account for the previously reported increase in food intake caused by gut bacteria [60] , this effect is not related to the changes in food choice we describe . Gut bacteria therefore use an independent mechanism to specifically reduce the yeast appetite of the host . Flies rely on the continuous replenishment of their microbiome through feeding [61] . If commensal bacteria provide protection against eAA depletion , one might expect flies to prefer ingesting food containing commensals . We therefore set out to compare the appetite of flies towards food with or without commensal bacteria using the flyPAD ( Fig 5E ) . In agreement with our hypothesis , flies ate more vigorously from a food source containing the commensal bacteria when compared to the same food without commensals ( Fig 5E ) . Flies are therefore able to increase feeding behavior when bacteria are present in the food . This suggests that flies are able to actively modulate their feeding behavior to replenish or modify their microbiota in order to profit from the physiological benefits of the commensals . Our data suggest that specific bacteria directly act on host physiology and behavior and provide evidence contrary to a generalized effect of bacterial material . We therefore decided to use the gnotobiotic model to identify which bacteria in the mix were producing the change in feeding behavior in eAA-deprived animals . To do so , we first removed each species separately from the mix and tested if the reduced sets could suppress the yeast appetite of His-deprived flies . While removal of Acetobacter pomorum ( Ap ) abolished the capacity of the mix to suppress yeast appetite , removal of any of the other four species had no effect ( Fig 6A ) . Ap alone , however , is not sufficient to change yeast appetite , indicating that it acts in concert with other bacteria in the mix . Given that Lactobacilli act together with Ap to alter metabolite composition in flies [68] , we decided to test if Ap together with Lactobacillus plantarum ( Lp ) or Lactobacillus brevis ( Lb ) are sufficient to alter yeast appetite . Indeed , the combination of Ap with either Lp or Lb is sufficient to suppress the yeast appetite induced by deprivation from either His or Ile ( Fig 6A and S7 Fig ) . This result also explains why removing either Lp or Lb from the five-bacteria mix had no effect , as these species seem to act redundantly . Furthermore , neither Lp , Lb , nor the combination of both change feeding behavior , highlighting the specificity of the combined Ap–Lactobacilli effect on yeast appetite ( Fig 6A ) . The same approach allowed us to conclude that Ap and Lp act together to increase sugar appetite ( Fig 6B ) . In contrast to the effect on yeast appetite , the Ap–Lb combination has no effect on carbohydrate consumption ( Fig 6B ) . This reinforces the previous data showing that yeast appetite is independent of sugar appetite . Taken together , these data show that Acetobacter pomorum can act together with either Lactobacillus plantarum or to a certain extent with Lactobacillus brevis to change food selection . The ability of the commensal bacteria to compensate for the effect of eAA deprivation on yeast appetite and egg laying suggests that the bacteria could supply the host with eAAs , thus buffering the animal from the absence of these important nutrients in the diet . Such an effect would be reminiscent of the role of the Buchnera endosymbiont in aphids , which allows this insect to thrive while feeding on sap , which contains very low amounts of AAs [69] . We tested this hypothesis by depriving flies from three different eAAs ( His , Ile , and Val ) and comparing the levels of free AAs in the heads of flies that had been either pretreated or not with the five-bacteria mix . We decided to focus on the AA levels in heads to avoid effects due to changes in the number of eggs carried by the fly and because of evidence that nutrient sensing could act at the level of the brain of the fly to change food preference [12] . His , Ile , or Val deprivation lead to a drastic decrease in the levels of these three AAs in head extracts ( Fig 7A ) , which is likely to cause the previously observed increases in yeast appetite ( Fig 2 ) . This effect was specific to the manipulated AAs , as the levels of nonmanipulated AAs neither increased nor decreased ( Fig 7A ) . AA-satiated flies treated with the bacterial mix did not show an increase in His , Ile , or Val . Surprisingly , deprived flies continued having very low titers of the measured eAA independent of the bacterial pretreatment ( Fig 7A ) . This stands in contrast to the clear effect of the bacterial pretreatment on yeast preference and egg laying ( Fig 4 and S4 Fig ) . Our failure to observe changes in eAA levels induced by bacterial pretreatment opens the intriguing possibility that the commensal bacteria modify food choice and egg laying through an AA-independent mechanism ( Fig 7B ) .
Multiple nutrients , including AAs , metals , vitamins , and sterols have been shown to be nutritional modulators of life history traits [7] . Given that in an ecological setting , yeast is likely to be an important nutritional source of these nutrients , it is therefore surprising that the animal only develops a yeast appetite upon the restriction of AAs . One reason might be that animals have not evolved strategies to regulate the intake of all nutrients separately but just specific ones . This could be explained by the fact that in an ecological setting , animals do not need to react to the lack of each nutrient independently as these distinct nutrients are found together in nature in the form of food . If yeast is the ecologically relevant source of most nutrients required for the fly , then the animal could use the lack of internal AAs as a proxy for the concomitant lack of other nutrients , such as minerals , metals , and vitamins . The increase in yeast appetite triggered by AA deprivation would thus be sufficient to compensate for the lack of other nutrients . This would highlight that while synthetic diets are invaluable tools for studying the impact of nutrients on physiology and behavior , the results obtained from such studies always need to be interpreted in the context of ecologically relevant food sources such as yeast . It is also possible , however , that foods other than yeast can serve as sources for specific nutrients . Further focused analyses of the effect of specific nutrient classes on behavior will be required to identify the full capacity of Drosophila to maintain nutrient homeostasis . How do flies sense the internal deficiency of AAs and what are the circuit mechanisms allowing them to increase yeast or AA intake upon this nutritional restriction ? The brain should be able to detect changes in AA concentrations given that the concentration of free eAAs in the head drops dramatically upon their removal from the diet . Changes in behavior could therefore either be informed by direct sensing of internal AA titers by the nervous system and/or by detecting a signal released by peripheral tissues as response to lack of AAs ( Fig 7B ) . Two molecular mechanisms have been proposed as mediating neuronal AA sensing: the TOR and GCN2 pathways . In Drosophila , neuronal TOR signaling has been proposed to influence food selection [12] , and in Drosophila and vertebrates , GCN2 has been proposed to direct behavioral nutrient homeostasis by mediating post-ingestive neuronal sensing of AAs [70–72] . The involvement of neuronal GCN2 in nutrient selection in vertebrates has , however , been recently challenged [73] . We therefore have only very rudimentary clues as to what could be the mechanisms mediating internal sensing of AA availability and subsequent changes in behavior . Our data that flies can behaviorally react to the absence of a genetically engineered neAA deficiency suggest that whatever the sensing mechanism , it has to be able to sense the absence of any AA . Intriguingly , at the molecular level , nutrient-sensing pathways such as the TOR pathway have been proposed to mainly react to specific AAs [46] . Our data suggest that nutrient-sensing pathways could have a much broader spectrum of action . Alternatively , the sensing of AA deficiencies could rely on different molecular mechanisms , which could for example detect the decrease in translation induced by the lack of AAs . Such a decrease could either be sensed per se or could lead to a decrease ( or increase ) of specific translation products , which could serve as signals to alter behavior . Pinpointing the site and cellular substrate of AA sensing as well as the underlying molecular mechanisms remains a key challenge in the field of nutrient homeostasis . At the circuit level , the lack of AAs is likely to lead to a change in chemosensory processing that would lead to a change in nutrient preference . For proteins , such changes have been proposed in locusts [74] . In Drosophila , chemosensory neurons have been shown to be directly modulated by the internal energy state of the animal [13] . Furthermore , mating has been shown to modulate salt taste processing using the same circuit as yeast intake [14] . How internal AA states affect yeast and AA chemosensory processing , however , still remains to be elucidated . The main obstacle is the lack of information on the identity of the chemosensory neurons mediating yeast and AA feeding in Drosophila . Identifying these neurons and analyzing how yeast perception is modulated by internal state should allow us to better understand how the internal AA state directs feeding decisions . The extent to which the microbiota affects specific nutrient appetites has not been previously explored . We show that when flies are AA challenged , commensal bacteria reduce their compensatory yeast appetite . The increase in sugar appetite observed in flies harboring commensals further decreases the ratio of protein to carbohydrate intake , an important determinant of life history traits in animals , including vertebrates [2] . Given that a reduction in yeast and AA intake leads to an increase in lifespan , our observation that commensal bacteria reduce the intake ratio of proteins to carbohydrates could account for the shorter lifespan of axenic flies [75] . It is interesting to note that the flies harboring commensals are able to increase their reproductive output ( our study and [76] ) despite their lower protein intake . Commensal bacteria could therefore have a highly beneficial impact on the fly , enabling it to simultaneously maximize lifespan and reproductive output . The increase in sugar appetite observed in gnotobiotic flies can be simply explained by the bacteria utilizing the sugar in the food and therefore inducing a carbohydrate deficit in flies . The decrease in yeast appetite , however , is more difficult to explain . One simple possibility could have been that these bacteria act as nutrients , which has been proposed for yeast and other fungi [77] . Our findings that inactivated bacteria do not induce a change in feeding behavior , that bacteria are not able to suppress the yeast appetite induced by neAAs when we perturb the function of Henna , and that only specific commensal bacteria change yeast appetite , all indicate that the microbiota acts in a very specific way to alter food choice . Furthermore , previous data that microbes can improve the uptake of AAs [77 , 78] are not sufficient to explain the suppression of yeast appetite in flies pretreated with commensals , as we use holidic media completely devoid of eAAs . A key question is thus: what could be the mechanisms by which gut bacteria change yeast appetite and increase egg laying ? Intracellular symbiotic bacteria are known to provide eAAs in other insects [69] , and gut bacteria have been shown to provide significant amounts of eAAs in vertebrates , including humans [79] . A straightforward hypothesis would therefore be that these two bacteria are able to provide the fly with eAAs . However , we were not able to detect changes in the levels of free eAAs in flies pretreated with the commensal mix . In spite of this , we are reluctant to completely rule out that the microbiota acts on yeast appetite and egg laying by providing eAAs . It is possible , for example , that in an eAA-deprived situation , bacterially produced eAAs are immediately utilized without increasing the pool of free AAs . In such a model , bacterially derived eAAs would be fully allocated to sustain reproduction as well as alleviate the process which triggers changes in yeast appetite upon eAA deprivation . In such a situation , it is conceivable that one would not be able to measure an increase in free eAAs provided by the bacteria . Our data , however , suggest that commensal bacteria do not act by providing eAAs to the host . What could be alternative mechanisms by which they influence behavior and egg production ? They could secrete metabolites that help the host to increase its ability to use its remaining AAs , thereby buffering the fly from the effects of dietary eAAs . Intriguingly , both yeast appetite and reproduction are thought to be regulated by the nutrient-sensitive TOR pathway [12 , 80–83] , and commensals have been shown to be able to modulate this pathway [37] . It is therefore possible that these bacteria act directly on nutrient sensing pathways by releasing metabolites that mimic the availability of eAAs ( Metabolite X in Fig 7B ) . Distinguishing between these hypotheses will require comprehensive metabolome analyses of flies in different bacterial and nutrient states as well as careful genetic and behavioral studies , both at the level of the host and the bacteria . The metabolic repertoire of an organism is evolutionarily fixed in its genome . As such , it represents a static set which can mainly be modulated by transcriptional control . The observation that flies ingest more food containing commensal bacteria suggests that they might be able to direct their feeding behavior to replenish or maintain a specific microbiome composition . It is therefore attractive to speculate that the dynamic nature of the microbiome in flies paired with the ability to modulate the replenishment of gut microbes through feeding could allow them to extend and adapt their metabolic repertoire by exploiting that of the microbiome [84] . This ability could partially explain the success of Drosophila in adapting to a wide range of habitats . Our understanding of how the microbiota influences behavior remains extremely rudimentary . In vertebrates , this task is made especially daunting by the complexity of their microbiota . Drosophila , on the other hand , has proven to be an especially powerful model for understanding microbe–host interaction because of the ability to isolate a single bacterial species promoting physiological effects such as improved growth [85 , 86] . Especially in vertebrates , many effects of the microbiome on the host , however , are likely to rely on interactions among different microbial species . Our finding that Ap acts together with Lactobacilli to influence food choice provides a powerful system for not only understanding how microbes act on the host to influence brain function but also how microbes cooperate to shape complex host traits . Microbes could act together by exchanging metabolites to act on the host . Alternatively , one bacterium could support the growth and survival of the other in nutritionally challenging situations , allowing it to exert its behavioral effect . The identification of these two bacterial species as mediators of food choice paired with the powerful genetic toolkit available in Drosophila provides a unique opportunity to identify the mechanisms by which microbes interact to shape the behavior of the host . Our findings highlight a new function of the microbiota in modulating nutrient-specific appetites . Given that in Drosophila , AA state not only controls food intake but also more complex behavioral features , such as risk taking [16] , the microbiota could influence behavior beyond feeding . Furthermore , because AAs and nutrient sensing play a pivotal role in controlling physiology , neurodevelopmental disorders [87 , 88] , and behavior across metazoans , such mechanisms could be conserved across phyla . Nutrition could therefore provide a framework for understanding how the microbiome influences behavior , disease , and physiology across phyla . Our findings highlight the power of the Drosophila model for dissecting complex nutritional–microbial–behavioral interactions and suggest the intriguing possibility that commensal bacteria influence behavior and brain function in invertebrates and vertebrates by tapping into the nutrient-sensing abilities of the nervous system .
Unless stated otherwise , all experiments were performed with mated w1118 female flies . Ubiquitous ( tubulin-Gal4 [89] ) , pan-neuronal ( elav-Gal4 [90] ) , tracheal ( btl-Gal4 [91] ) , or fat body ( Cg-Gal4 [92] , BL #7011 ) expression of RNAi delivering transgenes against Henna ( CG7399 ) was achieved by crossing Gal4-carrying female flies with three independent UAS-Henna-RNAi stocks ( HennaIR1: VDRC #35240; HennaIR2: NIG-RNAi #7399R-3; HennaIR3: BL #29540 ) . The full genotypes of experimental flies are listed in S1 Table . Flies were reared on yeast-based medium ( YBM ) ( per liter of water: 8 g agar [NZYTech , PT] , 80 g barley malt syrup [Próvida , PT] , 22 g sugar beet syrup [Grafschafter , DE] , 80 g corn flour [Próvida , PT] , 10 g soya flour [A . Centazi , PT] , 18 g instant yeast [Saf-instant , Lesaffre] , 8 ml propionic acid [Argos] , and 12 ml nipagin [Tegospet , Dutscher , UK] [15% in 96% ethanol] supplemented with instant yeast granules on the surface [Saf-instant , Lesaffre] ) . To ensure a homogenous density of offspring among experiments , fly cultures were always set with 5 females and 4 males per vial and left to lay eggs for 7 d . Flies were reared in YBM until adulthood . Holidic media ( HM ) were prepared as described previously [7] using the HUNTaa formulation without food preservatives , with the exception of the HM used for pretreating axenic and gnotobiotic flies , for which we used an HM with an improved AA composition [93] . The different HM used in this study are described in S2 Table and S3 Table . In all experiments where we refer to all neAAs removal , L-glutamate was still present in the diet in order to prevent any possible adverse effects in neuronal function . Sucrose medium consisted of Kleenex tissue soaked with 5 ml of a 100 mM sucrose ( Sigma-Aldrich , #84097 ) solution . For all experiments using the HM , the following dietary treatment protocol was used in order to ensure a well-fed state and minimize the microbial load in the flies [61] ( S8 Fig ) : groups of 1–5-d-old flies ( 16 females and 5 males ) were collected into fresh YBM-filled vials and transferred to fresh YBM after 48 h . Following a period of 24 h , flies were transferred to different HM for 72 h and immediately tested in the indicated assay . Flies treated using this protocol had a low titer of commensal bacteria ( S3 Fig ) . For yeast dilution experiments presented in S5 Fig , flies were kept for 72 h prior to the behavioral assay on media containing 200 mM sucrose ( Sigma-Aldrich , #84097 ) , 2% agar ( Difco , # 214530 ) , and variable instant yeast concentrations: 5% , 2 . 5% , 1% , and 0% ( Saf-instant , Lesaffre ) . After preparation , all yeast-based media were autoclaved before pouring into culture vials . Fly rearing , maintenance , and behavioral testing were performed at 25°C in climate-controlled chambers at 70% relative humidity in a 12-h light–dark cycle ( Aralab , FitoClima 60000EH ) . Polypropylene fly vials ( VWR , #734–2261 ) were used . These protocols are available in the following collection in protocols . io dx . doi . org/10 . 17504/protocols . io . hdtb26n . The protocol to generate axenic w1118 fly cultures by sterilizing embryos was adapted from [94]: embryos were put for 2 min in 2 . 5% active chlorine ( 50% bleach ) followed by 2 min in 70% ethanol and 2 min in autoclaved distilled water . The embryos were then transferred onto sterile food ( autoclaved before pouring into culture vials ) containing antibiotics ( final concentrations: 416 . 7 μg/ml tetracycline [high dose] , 41 . 67 μg/ml chloramphenicol , 41 . 67 μg/ml ampicillin , and 8 . 333 μg/ml erythromycin ) . In order to compensate for the developmental delay observed in axenic larvae [37] , the yeast content of the medium was increased to 41 . 67 g per liter . Axenic w1118 flies were regularly transferred into vials containing freshly prepared , antibiotic-supplemented , high-yeast food ( S8 Fig ) . The absence of bacteria was assessed by grinding flies in sterile 1x PBS and spreading the suspension on LB , MRS , or Mannitol plates . LB and MRS plates were incubated at 37°C and Mannitol plates at 30°C , respectively , before assessing the presence of bacterial colonies . The antibiotic treatment did not lead to any apparent malaise in the treated flies . Furthermore , to ensure that the antibiotics exposure would not directly affect the experimental animals , these were raised in sterile food without antibiotics ( S8 Fig ) . Importantly , the results obtained using the gnotobiotic flies fully recapitulate the results obtained with the conventionally reared “low bacteria titer” flies . All experiments in Figs 5A–5D , 6 and S7 were performed using axenic or gnotobiotic flies . These protocols are available in the following collection in protocols . io dx . doi . org/10 . 17504/protocols . io . hdtb26n . The following bacterial species and strains ( kindly provided by François Leulier , IGFL , France , and Won-Jae Lee , SNU , South Korea ) were used in this study: Lactobacillus plantarumWJL [62] , Lactobacillus brevisEW [62] , Acetobacter pomorum [62] , Commensalibacter intestiniA911T [62] , and Enterococcus faecalis [63] . Lactobacilli were cultured in 10 ml of liquid MRS medium ( Sigma-Aldrich , #69966 ) in 14 ml culture tubes ( Thermo Fisher Scientific , #150268 ) at 37°C for 24 h without agitation . C . intestiniA911T and A . pomorum were cultured in a liquid mannitol medium ( 3 g/l Bacto peptone [Difco , #0118–17] , 5 g/l yeast extract [Difco , #212750] , 25 g/l D-mannitol [Sigma-Aldrich , #M1902] ) at 30°C for 48 h under 170 rpm agitation . C . intestiniA911T was cultured in 20 ml of medium in 50-ml tubes ( Falcon ) , and A . pomorum was cultured in 200 ml of medium in 500-ml flasks . E . faecalis was cultured in 200 ml of liquid LB medium ( Sigma-Aldrich , #L3022 ) in 500-ml flasks at 37°C for 24 h under 220 rpm agitation . Liquid cultures were set with colonies grown in fresh solid media ( 15 g/l agar [Difco , # 214530] ) . These protocols are available in the following collection in protocols . io dx . doi . org/10 . 17504/protocols . io . hdtb26n . Prior to transferring the flies , each HM vial was inoculated with either single or different combinations of the following bacterial species: L . plantarumWJL ( 6 . 4 x 104 CFU ) , L . brevisEW ( 5 . 31 x 103 CFU ) , C . intestiniA911T ( 9 . 04 x 104 CFU ) , A . pomorum ( 9 . 5 x 104 CFU ) , E . faecalis ( 1 . 11 x 105 CFU ) , and E . coli ( 0 . 924 x 109 CFU ) . To prepare this mixture , the necessary volume of liquid culture for each bacterial species was centrifuged three times at 3 , 000 rpm for 10 min and repeatedly resuspended in 1x PBS . To exclude an effect from residual components of bacterial media , the equivalent volume of sterile bacterial media was centrifuged in parallel and used as a control . After the final centrifugation , both the control and the bacterial pellet were resuspended in sufficient 1x PBS to achieve an inoculation volume of 50 μl per vial . For the experiments with heat-inactivated bacteria , the bacterial suspension was incubated at 100°C for 10 min before inoculation in HM vials . The final suspensions were added to the surface of the HM and allowed to dry for approximately 1 h before the addition of flies . Note that even when not pretreated to be axenic , because of rearing protocol flies used in all experiments had a very low starting titer of internal microbes prior to inoculation ( S3 Fig ) . These protocols are available in the following collection in protocols . io dx . doi . org/10 . 17504/protocols . io . hdtb26n . Flies were surface sterilized to remove any bacteria that could be found on the cuticle by washing them in 70% ethanol followed by two washes in sterile 1x PBS . Flies were grinded in 1x PBS ( 500 μl/18 flies ) and diluted 180X . The suspension was then plated on LB , MRS , or Mannitol medium . LB and MRS plates were incubated at 37°C and mannitol plates at 30°C before counting the number of bacterial colonies . These protocols are available in the following collection in protocols . io dx . doi . org/10 . 17504/protocols . io . hdtb26n . Two-color feeding preference assays were performed as previously described [12] . Groups of 16 female and 5 male flies were briefly anesthetized using light CO2 exposure and introduced into tight-fit-lid Petri dishes ( Falcon , #351006 ) . For the yeast choice assays , the flies were given the choice between nine spots of 10 μl sucrose solution mixed with red colorant ( 20 mM sucrose [Sigma-Aldrich , #84097]; 7 . 5 mg/ml agarose [Invitrogen , #16500]; 5 mg/ml Erythrosin B [Sigma-Aldrich , #198269]; 10% PBS ) and nine spots of 10 μl yeast solution mixed with blue colorant ( 10% yeast [Saf-instant , Lesaffre]; 7 . 5 mg/ml agarose; 0 . 25 mg/ml Indigo carmine [Sigma-Aldrich , #131164]; 10% PBS ) for 2 h . For the defined nutrient-choice assays , flies were given the choice between HM lacking AAs and containing 20 mM sucrose mixed with red colorant ( option 1: sucrose ) and HM lacking sucrose and containing the nutrients required for the experiment mixed with the blue colorant ( option 2 ) . In these experiments , the agar concentration in the HM was changed to 1 . 5% . After visual inspection of the abdomen under the stereo microscope ( Zeiss , Stereo Discovery . V8 ) , each female fly was scored as having eaten either sucrose ( red abdomen ) , yeast ( blue abdomen ) , or both ( red and blue or purple abdomen ) media . The yeast preference index ( YPI ) for the whole female population in the assay was calculated as follows: ( nblue yeast−nred sucrose ) / ( nred sucrose + nblue yeast + nboth ) . Initially , dye-swap ( red yeast versus blue sucrose choice ) experiments were performed in parallel , and because the change of feeding preference was observed in both conditions , we opted to exclusively perform red sucrose versus blue yeast choice experiments . In all experiments , the observer was blind for both diet and genotype . All assays were performed between ZT6 and ZT9 . CAFE assays were based on a protocol previously described [14 , 47] with some adaptations . On the assay day , flies were anesthetized with CO2 , sorted under a stereo microscope in groups of 18 females , and allowed to recover for 3 h at 25°C . The CAFE chamber consisted of a large plastic vial ( 50 x 100 mm ) ( Semadeni AG , #6128 ) with 6 5-μl glass capillaries ( Hirschmann , #9600105 ) inserted through a foam lid . Capillaries were filled with 20 mM sucrose or 10% yeast extract ( Sigma-Aldrich #1625 ) solutions and placed in an alternating circular fashion . Each group of flies was aspirated into a CAFE chamber , and during the 4 h of the assay , four experimental readings per capillary were scored ( t0 , t0+1 h , t0+3 h , and t0+4 h ) to determine consumption . In order to correct for evaporation , each set of experimental chambers was accompanied by an empty chamber ( no flies ) . Total sucrose or yeast extract consumption per time point was determined by subtracting the sum of the readings of the three capillaries of the respective solution in the empty chamber from the equivalent values in the experimental chamber . Consumption per fly was obtained by dividing sucrose or yeast extract total consumption by the number of living flies at the end of the assay . flyPAD assays were performed as described in [67] . For food choice experiments , single flies in different dietary conditions were tested in arenas that contained two kinds of food patches: 10% yeast and 20 mM sucrose , each mixed with 1% agarose . To measure the phagostimulatory power of bacteria ( Fig 5E ) , we used a flyPAD setup that had never been exposed to yeast . All tested flies were deprived from amino acids using HM–AAs . For the flyPAD assays , one feeding well per arena was filled with HM without sucrose , either intact media ( holidic AA medium ) or media supplemented with the bacterial mixture ( holidic AA medium with five bacteria ) ( in HM: L . plantarumWJL 1 . 02 x 102 CFU , L . brevisEW 8 . 49 CFU , A . pomorum 1 . 52 x 102 CFU , C . intestiniA911T 1 . 45 x 102 CFU , and E . faecalis 1 . 77 x 102 CFU ) . These media were prepared by adding agarose ( 1% ) as a gelling agent together with cholesterol after autoclaving . Media were prepared on the experimental day and maintained at 30°C in a heat block . Preparation of the control and bacterial mixture pellets were performed as described above and directly resuspended in the HM without sucrose to generate holidic AA medium and holidic AA medium with five bacteria , respectively . Each medium was loaded into a single feeding well of the arena . For all experiments , flies were individually transferred to flyPAD arenas by mouth aspiration and allowed to feed for 1 h at 25°C , 70% relative humidity . The total number of sips per animal over this hour was calculated using previously described flyPAD algorithms [67] . Noneating flies ( defined as having fewer than two activity bouts during the assay ) were excluded from the analysis . Groups of 16 female and 5 male flies were briefly anesthetized using light CO2 exposure and transferred to apple juice agar plates ( per liter , 250 ml apple juice , 19 . 5 g agar , 20 g sugar , and 10 ml nipagin [15% in ethanol] ) , where they were allowed to lay eggs for 24 h . Flies were then removed and counted and eggs were counted . Egg laying was calculated by dividing the number of eggs by the number of living females at the end of the assay . Flies used for mRNA extraction were snap frozen in dry ice and kept at –80°C until used . Behavioral assays were performed in parallel to confirm that sibling flies presented the expected feeding phenotype . mRNA was extracted from flies ( three flies per condition ) using the following procedure: flies were grinded and homogenized for 20 s ( using pestles #Z359947 , Sigma ) in 100 μl of PureZOL ( #732–6890 , Bio-Rad ) . 250 μl of PureZOL was further added and mixed by pipetting and incubated at RT for 10 min . Finally , 350 μl of 100% ethanol was added , and the samples were mixed and transferred to a Zymo column ( Direct-zol RNA MicroPrep #R2062 , Zymo research ) . The manufacturer’s instructions were followed to purify the mRNA ( including DNAse treatment ) , and samples were eluted in 15 μl of distilled RNase/DNase-free water . The concentration of the total mRNA samples was determined by performing a spectrophotometer scan in the UV region . Total RNA ( 1 μg ) was reverse transcribed ( RT ) using the iScript Reverse Transcription Supermix for RT-PCR kit ( #170–8840 Bio-Rad ) , following the manufacturer’s instructions . The expression of Henna was determined using real-time PCR . Each cDNA sample was amplified using SsoFast EvaGreen Supermix on the CFX96 Real-Time System ( Bio-Rad ) . Briefly , the reaction conditions consisted of 1 μl of 1:10 diluted cDNA , 1 μl ( 10 μM ) of each primer , 10 μl of supermix , and 7 μl of water . The cycle program consisted of enzyme activation at 95°C for 30 s , 39 cycles of denaturation at 95°C for 2 s , and annealing and extension for 5 s . The primers used in this reaction are listed in S4 Table . This experiment was performed using three experimental replicas and two technical replicas per genotype . Appropriate nontemplate controls were included in each 96-well PCR reaction , and dissociation analysis was performed at the end of each run to confirm the specificity of the reaction . Absolute levels of RNA were calculated from a standard curve and normalized to the internal controls ( Actin42A and RpL32 ) . The relative quantitation of each mRNA was performed using the comparative Ct method . Data processing was performed using Bio-rad CFX Manager 3 . 1 ( Bio-Rad ) . 500 females per condition were collected on the same day as behavioral assays and were snap frozen in dry ice . Flies were kept at –80°C until head preparation for amino acids measurements . Fly heads were separated from other body parts by vortexing the Eppendorf tubes and posteriorly passing the debris through 710-mm and 425-mm sieves ( Retsch GmbH ) . Fly heads were counted before homogenization to ensure that the same number was used for all conditions . Heads were homogenized in 200 μl of 2 . 5% TCA and centrifuged for 10 min at top speed at 4°C . The supernatant was recovered and stored at 4°C for analysis . Amino acid quantification was performed by HPLC at a clinical laboratory ( Joaquim Chaves Laboratories , PT ) . Amino acids were detected using AccQ . Tag ( Waters , #176001235 ) .
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What animals , including humans , choose to eat has a tremendous impact on health and wellbeing . Though intake of dietary proteins and amino acids is essential for animals , excessive consumption of these nutrients is known to have detrimental effects . Many animals , therefore , execute precise control over the intake of these key nutrients . However , the factors controlling protein appetite are poorly understood . Here , we show that in the vinegar fly Drosophila melanogaster , essential amino acids and gut bacteria are key modulators of protein appetite . Lack of any one essential amino acid from the diet produces a strong and specific appetite for proteinaceous or amino acid–rich food . However , flies with an appropriate microbiome do not develop this protein appetite . Specifically , two gut bacteria species , Acetobacter pomorum and Lactobacilli , work together to suppress protein appetite . Furthermore , we show that flies lacking dietary essential amino acids have reduced reproductive output , an effect which is also rescued by gut bacteria . Finally , based on metabolite measurements , we propose that the influence of bacteria on host physiology and behavior is not mediated by changing amino acid levels . Our study demonstrates how the interaction of specific nutrients with the microbiome can shape behavior and animal fitness and suggests that they do so through a novel mechanism .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"medicine",
"and",
"health",
"sciences",
"microbiome",
"chemical",
"compounds",
"disaccharides",
"appetite",
"microbiology",
"diet",
"carbohydrates",
"organic",
"compounds",
"physiological",
"processes",
"fungi",
"nutrition",
"eating",
"bacteria",
"microbial",
"genomics",
"food",
"medical",
"microbiology",
"chemistry",
"yeast",
"organic",
"chemistry",
"physiology",
"genetics",
"biology",
"and",
"life",
"sciences",
"physical",
"sciences",
"genomics",
"sucrose",
"nutrients",
"organisms"
] |
2017
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Commensal bacteria and essential amino acids control food choice behavior and reproduction
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Salivary components from disease vectors help arthropods to acquire blood and have been shown to enhance pathogen transmission in different model systems . Here we show that two salivary enzymes from Lutzomyia longipalpis have a synergist effect that facilitates a more efficient blood meal intake and diffusion of other sialome components . We have previously shown that Lundep , a highly active endonuclease , enhances parasite infection and prevent blood clotting by inhibiting the intrinsic pathway of coagulation . To investigate the physiological role of a salivary hyaluronidase in blood feeding we cloned and expressed a recombinant hyaluronidase from Lu . longipalpis . Recombinant hyaluronidase ( LuloHya ) was expressed in mammalian cells and biochemically characterized in vitro . Our study showed that expression of neutrophil CXC chemokines and colony stimulating factors were upregulated in HMVEC cells after incubation with LuloHya and Lundep . These results were confirmed by the acute hemorrhage , edema and inflammation in a dermal necrosis ( dermonecrotic ) assay involving a massive infiltration of leukocytes , especially neutrophils , in mice co-injected with hemorrhagic factor and these two salivary proteins . Moreover , flow cytometry results showed that LuloHya and Lundep promote neutrophil recruitment to the bite site that may serve as a vehicle for establishment of Leishmania infection . A vaccination experiment demonstrated that LuloHya and Lundep confer protective immunity against cutaneous leishmaniasis using the Lu . longipalpis—Leishmania major combination as a model . Animals ( C57BL/6 ) immunized with LuloHya or Lundep showed minimal skin damage while lesions in control animals remained ulcerated . This protective immunity was abrogated when B-cell-deficient mice were used indicating that antibodies against both proteins play a significant role for disease protection . Rabbit-raised anti-LuloHya antibodies completely abrogated hyaluronidase activity in vitro . Moreover , in vivo experiments demonstrated that blocking LuloHya with specific antibodies interferes with sand fly blood feeding . This work highlights the relevance of vector salivary components in blood feeding and parasite transmission and further suggests the inclusion of these salivary proteins as components for an anti-Leishmania vaccine .
Leishmaniasis , a vector-borne parasitic disease , comprises several clinical manifestations ranging from skin sores to life-threating visceral diseases . The causative agents , Leishmania parasites , are transmitted to the vertebrate host by the bite of infected female phlebotomine sand flies ( Diptera: Psychodidae ) [1] . During blood feeding , sand fly saliva is deposited into the vertebrate host skin . It consists of a mixture of pharmacologically active compounds that work in a redundant way to counteract vertebrate platelet aggregation , blood coagulation , vasoconstriction and inflammation as an insect strategy for blood feeding success [2–4] . Sand fly saliva modifies the bite site environment and is known to play a major role in parasite transmission [5–9] . Several proteins found in the saliva of Lutzomyia longipalpis , the main vector of visceral leishmaniasis in the New World , contribute to enhance Leishmania pathogenesis . The salivary peptide Maxadilan , which was described in the early 1990s as the most potent known vasodilatory substance [10] , exacerbates Leishmania infection [11] . Likewise , the salivary endonuclease Lundep acts as an anticoagulant , anti-inflammatory and destroys the neutrophil extracellular traps ( NETs ) resulting in an increased parasite survival [9] . The fact that antibodies against Lundep block the DNase activity of female salivary gland extract ( SGE ) , opens the possibility of a potential use of Lundep as an anti-Leishmania vaccine . Hyaluronidases are enzymes commonly found in snake and arthropod venoms , blood sucking hookworms or leeches [12 , 13] . They cleave hyaluronic acid ( HA ) , a major component of the extracellular matrix in vertebrates [14] . Hyaluronidases can cause loss of the extracellular matrix leading to the diffusion of other salivary and/or venom components [13 , 15] . Hyaluronidases are present in saliva of sand flies and have been postulated as Leishmania virulence factors . Volfova and others [16] showed that inoculation of Leishmania major parasites with bovine hyaluronidase resulted in larger lesions when compared with parasites injected alone in a BALB/c mouse model . Although hyaluronidase activity has been described in the saliva of a variety of hematophagous insects [15–18] the mechanisms of salivary hyaluronidase pathogenesis and its role in blood feeding are not well understood . In this work we characterized the hyaluronidase from Lu . longipalpis saliva ( LuloHya ) and determined its physiological role in reducing the time of a blood meal as an essential factor for the insect to succeed in hematophagy . Furthermore , we show that LuloHya and Lundep could act as the salivary spreading factors increasing dissemination of other salivary components at the bite site . Finally , we show that immunization against these molecules significantly reduce L . major infection in mice , highlighting them as candidates for an anti-Leishmania vaccine .
The presence of a putative salivary hyaluronidase in adult Lu . longipalpis was first described by bioinformatic survey of its sialotranscriptome [19] . Accordingly , hyaluronidase activity was found in SGE of female Lu . longipalpis but not in non-blood feeding male adult sand flies ( Fig 1A ) . This sex specificity , at the protein and mRNA level ( Fig 1B ) suggests its involvement on blood feeding . Transcript AF132515 ( LuloHya ) codes for a putative hyaluronidase containing a signal peptide indicative of secretion with a calculated molecular weight ( MW ) of 42 . 28 kDa and pI of 7 . 98 . The amount of LuloHya present in a salivary gland pair is 0 . 338 ng , which is in accordance with the previous estimated data derived from silver gel staining [20] . These authors suggested that the presence of a hyaluronidase in the salivary glands would be < 0 . 1% of the total salivary gland protein; If Lu . longipalpis SG protein content is 0 . 36 μg , the estimated hyaluronidase would be < 0 . 36 ng ) . LuloHya has 6 putative N-glycosylation sites in its primary amino acid sequence indicating that the native protein can be highly glycosylated . Alignment of LuloHya with other known hyaluronidases in the NCBI database reveals the presence of the conserved amino acids found in most hyaluronidase enzymes characterized so far [21] . The predicted 3D-structure of LuloHya ( Fig 2 ) was generated automatically by I-TASSER software [22] . LuloHya coordinates used to generate the model were based on the ligand-free honeybee venom hyaluronidase ( 1FCQ ) and in complex ( 1FCV ) with hyaluronic acid tetramer [23] . The PyMol-generated model of LuloHya shows a typical globular protein structure resembling the classic ( β/α ) 8 TIM barrel commonly found in hydrolases and present in 10% of all enzymes [24] . The predicted topology of the active site appears to fall into the cleft or groove class , which can accommodate polymeric substrates such as HA . The modeled catalytic site of LuloHya contains the conserved amino acid residues aspartic acid ( D100 ) and glutamic acid ( E102 ) which are likely involved in the catalytic activity as well as two arginine residues ( R105 and R245 ) situated at opposite walls of the catalytic cleft ( Fig 2 ) . These 2 arginine residues may guide the HA into the active site through electrostatic interactions with the substrate [23] . To further characterize the catalytic and biological activity of this salivary enzyme , recombinant LuloHya was expressed in HEK cells and purified by affinity and size exclusion chromatography and visualized as a single band by Coomassie-staining gel electrophoresis ( Fig 3A and 3B ) . Identity of the purified recombinant protein was confirmed by N-terminal sequencing . Mouse polyclonal anti-LuloHya antibodies specifically recognized LuloHya and a single band in the SGE , both running at an apparent MW of 70 kDa , higher than the calculated MW of 42 kDa ( Fig 3B ) . This shift in electrophoretic mobility properties is due to the presence of sugar residues as in vitro deglycosylation of the recombinant protein recovers its expected MW under reducing conditions ( Fig 3C ) . Moreover , glycosylation of LuloHya is critical for its enzymatic activity ( Fig 3D ) . To investigate pH and ionic strength dependency of LuloHya , a series of enzymatic digestion of HA reactions at different pH and ionic strength conditions were carried out . Recombinant LuloHya shows higher hyaluronidase activity at acidic pH , peaking at pH 6 where no traces of HA were detected ( Fig 3E ) . Therefore , LuloHya belongs to the neutral ( pH 5–8 ) active class of hyaluronidases [13] . Hyaluronidase activity is also dependent on ionic strength , being most active at lower NaCl concentration ( 25–100 mM , Fig 3F ) . Because some hyaluronidases can digest other types of substrates , the substrate specificity of LuloHya was determined using different glycosaminoglycans . Both SGE and LuloHya specifically cleave HA but failed to digest other tested components of the extracellular matrix such as chondroitin sulfate B , dextran sulfate or heparin ( Fig 4A ) , confirming that LuloHya is specific for HA processing . Like bovine hyaluronidase , LuloHya and SGE effectively break HA of high , medium and low MW down to 27 kDa . In contrast , hyaluronidase isolated from the bacteria Streptomyces hyalurolyticus performs a complete cleavage of glycosaminoglycans ( Fig 4B ) . To investigate the biological activity of LuloHya and Lundep and their possible implication as salivary spreading factors , a dermonecrotic assay was performed . Whole ears of naïve mice inoculated with 3 μg of hemorrhagic factor ( HF ) alone or in combination with SGE ( 2 pairs ) , LPS-free LuloHya ( 10 μg ) and LPS-free Lundep ( 10 μg ) were excised after 2 h and the macroscopic changes in the skin of treated animals were measured . All samples were submitted for histological processing and evaluation . The macroscopic hematoma recorded in animals treated with HF in combination with Lundep , LuloHya and SGE groups resulted in a significantly larger area ( 2 . 6–4 . 2-fold increase ) when compared to the control group ( PBS+HF , Fig 5A and 5B , P<0 . 0001 ) . Interestingly , combining both recombinant proteins ( 5 μg each ) with HF resulted in a significantly larger area of erythema when compared with that observed when both proteins were injected separately ( Fig 5A and 5B ) . Histologically , sections of ears collected 2 h post-exposure to HF alone were characterized by moderate expansion of the sub-epithelial tissues by a mixture of edema fluid and infiltrating leukocytes ( most often neutrophils ) . The blood vessels within the affected regions were often dilated and congested and , in some areas , red blood cells were present free within the interstitial tissues immediately surrounding the congested vasculature ( hemorrhage ) . Ear sections from animals injected with HF in combination with Lundep contained focal and extensive areas of acute hemorrhage and inflammation most visible within the layer of the sub-epithelial stroma containing skeletal muscle . Inflammatory cells ( neutrophils ) were also present in the superficial dermis , between many of the adnexal structures . For the HF+LuloHya group , the changes in these tissues were similar to those described in the HF+Lundep group; however , the superficial dermis was more noticeably expanded by edema fluid and contained a more prominent and predominantly neutrophilic inflammatory cell infiltrate . Sections of ears from mice injected with HF in combination with SGE exhibited milder pathological changes that were characterized by mild sub-epithelial hemorrhage ( also associated with skeletal muscle layer ) but a much less prominent inflammatory infiltrate and minimal edema . The ears from animals in the control group ( without HF ) were found to be within normal histological limits ( Fig 5C ) . Ear sections from mice injected with HF and both LuloHya and Lundep showed the most significant areas of stromal hemorrhage and edema . These ear sections were markedly expanded by hemorrhage that ranged from focally extensive to larger coalescing regions and , in some cases , completely obscured the normal dermal structures of the ear ( collagen , pilosebaceous units , and skeletal muscle fibers ) . Inflammation within the superficial dermis was predominantly neutrophilic and of moderate abundance ( Fig 5D ) . Taken together , these results demonstrate that LuloHya and Lundep can enhance the local effect of HF and can potentially act as the spreading factor in the saliva of Lu . longipalpis , helping in the dissemination of other salivary molecules . Neutrophils are the first leukocytes to be recruited to an inflammatory site such as the one caused by blood feeding arthropods and represent the first line of defense against pathogen infections [25 , 26] . To examine the role of LuloHya and Lundep in neutrophil recruitment , human dermal microvascular endothelial cells ( HMVEC ) were treated with 1 μM of LuloHya , Lundep ( LPS-free ) and SGE ( 10 pairs ) or culture media alone and the gene expression of 84 cytokines and chemokines were measured using a qPCR array . After 4 h of incubation , genes coding for CCL2 , CCL20 , CSF2 , CSF3 , CX3CL1 , CXCL1 , CXCL10 , CXCL2 , CXCL8 , LIF , LTB and TNF were upregulated in the presence of LuloHya , whereas IL-11 and PPBP were downregulated . When cells were treated with Lundep , genes coding for CSF2 , CSF3 , CXCL1 and CXCL2 were also overexpressed . Additionally , IL-1 alpha , IL-5 , SPP1 , IFN gamma and chemokines PPBP , CCL3 and CXCL13 were downregulated in the presence of Lundep ( Table 1 ) . These results ( upregulated genes with more than 10-fold change ) were validated by qPCR in a separate experiment ( Fig 6 ) . The lack of effect of SGE on cytokine or chemokine gene expression change might be a result of low individual salivary protein concentration compared to the amount of recombinant protein tested . Because CXCL8 was upregulated upon stimulation in vitro , we also investigated the ability of these molecules to induce neutrophil infiltration in vivo . For this experiment , the ears of C57BL/6 mice were intradermally injected with either LPS-free LuloHya ( 10 μg and 1 μg ) , Lundep ( 10 μg and 1 μg ) , or Lu . longipalpis SGE ( equivalent to 2 pairs ) . As a negative control ears were injected with PBS alone or with a non-related salivary protein from the mosquito Aedes aegypti which was expressed and purified in the same manner to rule out unspecific cell recruitment . After 2 h , the ears were collected and polymorphonuclear leukocyte infiltration was analyzed by flow cytometry . Myeloid live cells were gated based on the CD11b expression and then further characterized as Ly6G+Ly6Cint neutrophils or Ly6G-Ly6Chi inflammatory monocytes ( Fig 7A ) . We observed a significant increase in the total number and frequency of neutrophils in the ears of mice injected with LuloHya when compared with PBS-injected mice . Salivary gland extracts from female Lu . longipalpis also increased infiltration of neutrophils in mice while Lundep alone slightly increased cell recruitment ( Fig 7B and 7C ) . The neutrophil recruitment appears to be dose dependent as higher doses promoted greater cell recruitment at the inoculation site . This effect is specific for the tested sand fly salivary proteins , as the Ae . aegypti protein did not alter the cell recruitment . Taken together , our results demonstrated that LuloHya and SGE actively cause neutrophil infiltration to the mouse ears and may serve as a vehicle for establishment of L . major infection . The relevance of cell recruitment to the bite site , for blood feeding , remains to be elucidated . Chagas et al [9] found that antibodies against Lundep block the DNase activity of female SGE and proposed the potential use of Lundep as an anti-Leishmania vaccine . In this work , we also found that anti-LuloHya antibodies can effectively block the hyaluronidase activity of LuloHya and SGE in vitro ( Fig 8 ) . Based on these results we carried out a vaccination experiment to test whether antibodies against LuloHya and Lundep can neutralize the endonuclease and hyaluronidase activity of Lu . longipalpis saliva in vivo thus protecting against cutaneous leishmaniasis in mice . Animals ( C57BL/6 mice ) were immunized with LuloHya and Lundep and challenged via needle injection with 103 L . major metacyclic promastigotes mixed with SGE ( equivalent to 1 pair ) and the ear lesion size was measured weekly for 8 weeks . Mice immunized against LuloHya and Lundep showed a significant reduction in the lesion size ( 2-fold smaller ) caused by L . major infection than those recorded in mice treated with adjuvant alone ( P<0 . 05 and P<0 . 01 , respectively , Fig 9A and 9B ) . Furthermore , vaccinated mice with either LuloHya or Lundep also showed a markedly reduced parasite load ( 28–53 fold ) in their ears ( P<0 . 05 , Fig 9C ) than control mice . To determine whether this protection against L . major infection is due to cellular or humoral immune response to LuloHya and Lundep , we carried out a similar vaccination experiment in B-cell-deficient ( B6 . 129S2-Ighmtm1Cgn/J ) mice . Immunization , parasite challenge and lesion measurement were carried out exactly as described above . Results shown in Fig 9D and 9E , demonstrated that the protective effect of LuloHya and Lundep against L . major infection is antibody-mediated response since the protection against parasite infection was abolished in B-cell-deficient mice . Parasite load showed no significant differences among the 3 groups ( Fig 9F ) . Mouse anti-LuloHya and anti-Lundep antibodies did not recognize L . major extract when analyzed by ELISA . Ninety-six well plates were coated with either LuloHya , Lundep or L . major extract . Anti-LuloHya antibodies strongly reacted with LuloHya but not Lundep or L . major extract . Likewise , anti-Lundep antibodies only recognized Lundep . A strong positive reaction was observed between anti-tubulin ( positive control ) and L . major due to the presence of tubulin proteins in the parasite membrane , ensuring parasite protein antigenicity ( S1A Fig ) . Comparable results were obtained by immunofluorescence microscopy , where neither mouse anti-LuloHya nor anti-Lundep antibodies recognized L . major parasites ( S1B Fig ) . Because rabbit IgG blocked the hyaluronidase activity of LuloHya and SGE in vitro ( Fig 8 ) , we also investigated the role of LuloHya in Lu . longipalpis blood feeding in mice . Blood feeding success was tested on passively immunized mice with anti-LuloHya or pre-immune IgG as a control . Among the sand flies that attempted to feed on mice with circulating anti-LuloHya antibodies only 22% succeed in ingesting blood compared to 51% success in the control group ( reduction of 56 . 86% , P<0 . 0001 , X2 test , S2A Fig ) . In our experiments , a 12 min period was chosen as the appropriate time to obtain around 60% of blood feeding success in the control group ( sand flies fed on mice with circulating pre-immune IgG ) . This effect was maintained when sand flies were blood fed for a second time on mice passively immunized ( 28% of engorged sand flies fed on mice with anti-LuloHya antibodies versus 48% of engorged sand flies fed on control mice , P = 0 . 0009 , X2 test ) . When sand flies are allowed to feed for a longer period of time ( 45 min ) , the trend is maintained; there is still a lower percentage of sand flies that successfully acquired blood when feeding on mice with anti-LuloHya antibodies ( 81% ) when compared with the sand flies fed on control mice ( 90 . 2% ) , however , the difference was not statistically significant . The relevance of the LuloHya and Lundep on other physiological parameters such as blood meal size or oviposition was also assayed . We compared blood meal size of fully engorged sand flies fed on animals passively immunized against LuloHya and Lundep [9] . No significant difference in blood meal size and oviposition rate of sand flies blood fed on immunized mice compared to sand flies fed on control mice were observed ( S2B and S2C Fig , [9] ) .
Sand fly saliva contains a vast array of pharmacological potent substances , including endonucleases and hyaluronidases , that counteracts vertebrate hemostasis and enhance pathogen transmission . Commonly found in poisonous animals , hyaluronidases and non-specific endonucleases are thought to be essential for the spreading of toxins and other venom component by compromising the integrity of extracellular matrix and significantly increasing the diffusion rates of other molecules [27 , 28] . Similarly , both enzymes ( EST or enzymatic activity ) are found in salivary glands of blood feeding arthropods [9 , 15 , 17 , 20 , 29–31] . Notably , these two enzymes appear to be mostly expressed in salivary glands as a pair , where their combination may help the diffusion of other salivary components and assisting blood meal intake by lowering the local viscosity caused by the release of host DNA and other extracellular matrix ( ECM ) components , and as an anticoagulant by inhibiting the intrinsic pathway of coagulation . Sand fly mouthparts penetrate no more than 0 . 5 mm of the host skin and thus can only reach the superficial capillaries . These are most of the time closed , as skin capillaries serve a function on heat dissipation in addition to skin nutrition . The capillary flow is regulated by the pre-capillary arteriolar sphincter . The vasodilators in sand fly saliva have to travel through the skin tissues deeper into the arteriolar bed , and this task may be facilitated by salivary spreading factors [32] . Although hyaluronidase activity was known to be present in sand fly saliva for several years [15 , 16 , 19 , 21 , 33] , their recombinant salivary proteins have never been produced . In this work , we successfully expressed LuloHya , the recombinant hyaluronidase from Lu . longipalpis salivary glands to further characterize it and its relevance in blood feeding and parasite transmission . Gene expression and enzymatic activity of LuloHya was exclusively found in adult female sand flies . These two findings suggest that LuloHya is involved in blood feeding . Moreover , its sequence contains a signal peptide and this protein , along with other salivary proteins , is secreted during blood feeding , as its activity is highly reduced afterwards [19] . LuloHya is catalogued as an endo-N-acetyl-hexosaminidase , an enzyme class that includes mammalian and hymenopteran hyaluronidases [14 , 16 , 20] . LuloHya specifically cleaved HA and did not break down other components of the extracellular matrix tested in our study . Lack of hydrolysis of chondroitin sulfate B was already ascribed for LuloHya but a moderate chondroitinase activity ( due to the breakdown of chondroitin sulfate A and C ) had been observed [20] . LuloHya hyaluronidase activity is dependent on pH , showing its highest activity at acidic pH , being concordant with studies in other insect hyaluronidases [15 , 18 , 20] . There is a pH gradient through the epidermis , varying from acidic values on the skin surface ( pH 4–6 ) to physiological pH of 7 . 4 in inner skin layers [34] . Interestingly , LuloHya would remain active through this pH range . Hyaluronidases and endonucleases appear to come in a two-pack in insect saliva . Either both transcripts are transcribed resulting in functional proteins or none of them are . For instance , in the saliva of Ae . aegypti or Anopheles spp . mosquitoes neither one of these transcripts are present or active [16 , 35 , 36] whereas Culex quinquefasciatus saliva shows a potent hyaluronidase and endonuclease activity [16 , 29] . Culex mosquitoes preferentially feed on birds [37]; therefore , both activities seem essential for counteracting the high density of avian blood meal , due to the presence of nucleated blood cells . In the case of sand flies , they feed on birds as well as mammals; being the host preference dependent on the sand fly species and the host availability [38–40] . Sand flies , as hematophagous insects , require a blood meal for egg development . Their blood feeding strategies consist of lacerating tissues and feeding from skin hemorrhages . Tissue damage caused by lacerating the host’s skin can release the vertebrate cell contents at the bite site , including nucleic acids and HA among other ECM components . Local release of these components can increase the local viscosity at the bite site . Hyaluronidase and endonuclease activities have been mainly found in the saliva of insects that use pool feeding strategies as well as in poisonous snakes and bee venom where it has been proposed to potentiate the biological effect of other toxins present in the venom . Venom hyaluronidases can contribute to systemic envenomation by accelerating venom absorption and diffusion [41] . Based on these finding , anti-hyaluronidases antibodies and inhibitors have been considered as potential treatments to attenuate local and systemic effect of snake poisoning [42] . Interestingly , antibodies against LuloHya completely abrogate the enzymatic activity of recombinant LuloHya and SGE . Our results also demonstrate the role of LuloHya in blood feeding , since passively immunized mice with anti-LuloHya antibodies significantly reduced the feeding success of Lu . longipalpis . Similar results were obtained when feeding sand flies on mice with circulating anti-Lundep antibodies [9] . Accordingly , blocking the salivary hyaluronidase and endonuclease activities of Lu . longipalpis can result in a more viscous skin environment , preventing the dispersion of other salivary components involved in blood feeding as well as making it more difficult to ingest a blood meal . Sand fly blood feeding was impaired when salivary hyaluronidase was blocked in vivo , as shown in our blood feeding success assays on passively immunized mice with anti-LuloHya antibodies . In these experiments , sand flies were allowed to feed for a short period of time ( 12 min ) . Interestingly , no differences were found on the feeding success rate using naïve sand flies versus sand flies that had already fed on passively immunized animals , ruling out the possibility of selection of a resistant population after vaccination with these salivary proteins . If they are permitted to feed for a longer period ( 45 min ) the trend is maintained: there is still a lower percentage of sand flies that successfully acquired blood when feeding on mice with anti-LuloHya antibodies , however , the difference is not statistically significant . It should be noted that feeding success experiments in the laboratory were carried out on anesthetized animals . These differences in required time to successfully acquiring a blood meal may be of epidemiological relevance in nature as longer feeding time can lead to awareness of the host and trigger defensive behavior of the host preventing a completion of a blood meal or even causing the death of the insect . Although sand flies take longer to blood feed in a hyaluronidase-blocked environment , they eventually ingest a similar amount of blood and no differences in other biological parameters such as oviposition rates were noticed . Bacterial hyaluronidases cleave HA down to disaccharides , thus helping bacterial nutrition [43] . However , LuloHya breaks HA down to fragments of 27 kDa , in a similar fashion as bovine hyaluronidase digestion of HA . During a skin injury , degradation of HA into small polymer fragments can lead to the activation of an alarm signal that triggers an immune response . Immunology of HA is a very complex field as different molecular sizes of HA produce contrary effects . While high molecular weight HA causes immune suppression small HA metabolites have been incriminated in the inflammatory stage of a healing wound [44] . Increased concentration of HA fragments in injured tissues , like the one caused by sand fly bites , inhibit fibrocyte differentiation and collagen deposition to allow macrophages to move freely around the injured site to phagocyte debris and clear any possible infectious agents [45] . Interestingly , our study shows that expression of neutrophil-specific CXC chemokines and colony stimulating factors were upregulated in HMVEC cells after incubation with LuloHya , and Lundep . Neutrophil-specific CXC subfamily of chemokines has been shown to be involved in leukocyte chemotaxis and inflammatory responses [46] . Granulocyte-colony stimulating factors ( CSF2 and CSF3 ) and leukemia inhibitory factor ( LIF ) are cytokines involved in leukocyte activation and differentiation . The effect of these 2 salivary molecules on cytokine and chemokine production could be attributed to either the salivary enzymes or their metabolites . The latest hypothesis agrees with other authors findings who have shown that small HA fragments induce leukocyte chemotaxis and expression of inflammatory cytokines [44 , 47–49] . Moreover , HA cleavage by hyaluronidase opens tissue spaces that facilitate neutrophil invasion . These results were consistent with the acute hemorrhage , edema and inflammation involving a massive infiltration of leukocytes , especially neutrophils , observed in the dermonecrotic assay in mice . Moreover , neutrophil infiltration in the inoculation site was demonstrated in vivo by flow cytometry experiments in the presence of these two proteins and SGE . We hypothesize that in the Lu . longipalpis—L . major transmission model , both proteins cooperate at the bite site promoting a favorable environment for Leishmania parasite: LuloHya would enhance neutrophil recruitment whereas Lundep would protect L . major from the leishmanicidal activity of neutrophil extracellular traps [9] . Ultimately , L . major survival in neutrophils would be enhanced and used as Trojan horses for further invasion of macrophages [50] . Previous studies on Lundep and bovine hyaluronidase treated these two salivary proteins as virulence factors , since L . major lesions were enlarged when these proteins were co-administered with the parasite [9 , 16] . In our work , we showed that mice were able to control L . major infection when vaccinated against either Lundep or LuloHya . Moreover , when using B-cell-deficient mice , the protection seen in C57BL/6 mice was abrogated , indicating that it is due to a humoral-mediated immune response . Antibodies against LuloHya and Lundep did not recognize L . major parasites either by immunofluorescence or ELISA . Therefore , the possibility of a direct effect of anti-LuloHya or anti-Lundep on the parasite was ruled out . The protective effect of sand fly saliva or specific salivary proteins has been generally attributed to a cellular immune response at the bite site ( delayed-type hypersensitivity ) [51] . Although the protecting effect of neutralizing antibodies against salivary proteins has been neglected [4 , 52] , we have shown here that neutralizing antibodies against LuloHya and Lundep play a role on the Leishmania infection development , as determined by the ear lesion and parasite load measurements . Although the sand fly–parasite combination used in this study does not occur in nature , Lu . longipalpis is a permissive sand fly that supports L . major infection in laboratory conditions . Furthermore , our laboratory has a well-established murine model of infection for this pair and it allowed us to compare our results with previous publications [9 , 53 , 54] . We believe that results showed in this article could be extrapolated to the natural setting where Phlebotomus papatasi acts as the natural vector of L . major . Phlebotomus papatasi has both enzymatic activities in their salivary glands; hyaluronidase [20] and endonuclease ( [55] , S3 Fig ) . In conclusion , we provide new insights of LuloHya and Lundep , the proteins responsible for the hyaluronidase and endonuclease activities present in the salivary glands of female Lu . longipalpis . These two proteins are crucial for accelerating the blood feeding process by reducing local viscosity at the bite site and disseminating other salivary components . In our model of study , LuloHya and Lundep promote neutrophil recruitment to the bite site that may serve as a vehicle for establishment of L . major infection . Our vaccine results clearly demonstrate the protective effect of antibodies against LuloHya and Lundep indicating that these two salivary proteins are promising vaccine candidates against Leishmania infection . Furthermore , it has been suggested that a saliva-based vaccine could be theoretically be cross-protective between phylogenetically related vector species [4] . Therefore , the potential use of these Lu . longipalpis proteins to protect infection of different Leishmania species transmitted by other sand flies should be considered . These aspects need to be evaluated with the natural sand fly—Leishmania parasite combination . The work presented herein is another example of the complexity of the biology of sand fly blood feeding and the potential use of vector salivary proteins as transmission blocking vaccines . Efforts to unravel these complex mechanisms should be encouraged .
Public Health Service Animal Welfare Assurance #A4149-01 guidelines were followed according to the National Institute of Allergy and Infectious Diseases ( NIAID ) , National Institutes of Health ( NIH ) Animal Office of Animal Care and Use ( OACU ) . These studies were carried out according to the NIAID-NIH animal study protocol ( ASP ) approved by the NIH Office of Animal Care and Use Committee ( OACUC ) , with approval IDs ASP-LMVR3 and ASP-LMVR4E . Lutzomyia longipalpis ( Jacobina strain ) were maintained using standard procedures in the insectary of the Laboratory of Malaria and Vector Research , NIAID [56] . Sand flies were anesthetized with CO2 , and SG dissections were manually performed under a stereo microscope in 20 mM phosphate buffer . SGE was prepared by sonication , according to Ribeiro et al [57] . For the endonuclease assays , SGE from Phlebotomus duboscqi and P . papatasi from Saudi Arabia and Turkey was used . LuloHya coding DNA sequence ( AF132515 ) was codon-optimized for mammalian expression and synthesized by BioBasic Inc . VR2001-TOPO vector containing LuloHya sequence ( Vical Incorporated ) was transformed in One Shot TOP10 Chemically Competent E . coli ( Invitrogen ) . Plasmid DNA was prepared using the EndoFree plasmid MEGA prep kit ( Qiagen , Valencia , CA ) . Recombinant protein expression was carried out at the SAIC Advance Research Facility ( Frederick , MD ) . Briefly , human embryonic kidney cells ( HEK293E; American Type Culture Collection , Manassas , Virginia ) were transfected with 1 mg of plasmid DNA and supernatants were collected 72 h after transfection and shipped frozen to our laboratory for protein purification . Recombinant LuloHya was purified by affinity chromatography followed by size-exclusion chromatography , using Nickel-charged HiTrap Chelating HP and Superdex 200 10/300 GL columns , respectively ( GE Healthcare Life Science , Piscataway , NJ ) . All protein purification experiments were carried out using the AKTA purifier system ( GE Healthcare Life Science , Piscataway , NJ ) . Purified protein was separated in a NuPAGE Novex 4–12% Bis-Tris Protein Gels ( Life Technologies ) and visualized by Coomassie stain . Protein identity was verified by Edman degradation at the Research Technologies Branch , NIAID , NIH . Lipopolysaccharides ( LPS ) can induce an immune response and thus samples containing LPS can generate false results . To ensure the recombinant proteins used in this study were LPS-free LuloHya and Lundep were LPS-decontaminated using the Pierce High-Capacity Endotoxin Removal Resin ( Thermo Scientific ) . Briefly , recombinant protein samples were passed through a column containing 1 ml-bed volume of a resin consisted of cellulose beads covalently attached to a modified polylysine associated with polymyxin B ligands which show high affinity for LPS . Proteins were incubated with the resin for 2 h at room temperature before being eluted with endotoxin-free , sterile PBS . Endotoxin levels were measured by Endosafe nexgen-PTS ( Charles River ) . All samples were below the allowable endotoxin limits stated by the FDA for IV preparations ( 5 EU/kg ) . The hyaluronidase activity was determined by the turbidimetric method as previously described [15] with minor modifications . Briefly , 0 . 1 mg/ml HA solution was prepared in 25 mM HEPES , 100 mM NaCl , 0 . 1% BSA , pH 7 . 3 . Four micrograms of HA were incubated with the SGE or 10 nM LuloHya ( final concentration ) . Reaction was run at 37°C for 1 h and stopped by the addition of 100 μl of 10 mg/ml cetylpyridinium chloride , which reacts with HA resulting in turbidity . Plates were read at 540 nm using a VersaMax microplate reader ( Molecular Devices ) . Turbidity corresponds with intact HA . Blanks lacking samples as well as HA were included in all tests . To characterize the ionic strength dependence of LuloHya activity , HA was prepared in different concentrations of NaCl ( 25 , 50 , 100 , 250 , 500 and 1000 mM ) . In the case of the pH dependence , hyaluronidase activity was measured using buffers at pH 4 , 5 ( 25 mM acetate ) , 6 ( 25 mM MES ) , 7 , 8 ( 25 mM HEPES ) , 9 and 12 . 5 ( 25 mM CAPS ) . The effect of glycosylation of LuloHya on hyaluronidase activity was investigated . The recombinant protein was deglycosylated using the Enzymatic DeglycoMx Kit ( QABio ) , following the manufacturer’s instructions . Both native and deglycosylated recombinant LuloHya were run on a NuPAGE gel and hyaluronidase turbidimetric assay was performed . Five micrograms of HA of different sizes ( high , medium and low molecular weight , which correspond to > 950 kDa , 75–350 kDa and 15–40 kDa , respectively , RD Systems , Minneapolis , MN ) were incubated for 1 h at 37°C with SGE ( equivalent of 1 SG pair ) or LuloHya ( 10 nM ) . Other commercial hyaluronidases were included as controls: bovine hyaluronidase ( Sigma ) and hyaluronidase from Streptomyces hyalurolyticus ( Sigma ) , both at 1 Unit per reaction . Samples were run on a 1 . 2% agarose gel for 30 min at 20 V followed by 2 h 30 min at 40 V . Agarose gels were stained with 0 . 05% Stains-all ( Sigma ) . For hyaluronidase activity specificity , other glycosaminoglycan components of the extracellular matrix were also tested , including chondroitin sulfate B , dextran sulfate and heparin ( 5 μg/sample , Sigma ) . Assays for determine the nuclease activity were carried out as previosuly described [9] . 200 ng of double stranded circular plasmid DNA ( VR2001; Vical Inc . , San Diego , CA ) were incubated with SGE from different Phlebotomus species in the presence of 50 mM Tris , 150 mM NaCl , 5mM MgCl2 , pH 8 . 0 . Polyclonal antibodies against LuloHya were raised in mice and rabbits . Mice ( Balb/c; Charles River , Frederick , Maryland ) were IM immunized with 10 μg of LuloHya or PBS as the control group in combination with Magic Mouse Adjuvant ( CD Creative Diagnostics , Shirley , NY ) . Mice were boosted 3 weeks after the first immunization and blood was collected ten days later . Immunization of rabbits was carried out in Noble Life Science facility ( Sykesville , MD ) according to their standard protocol ( http://www . noblelifesci . com/preclinical-drug-development/polyclonal-antibody-production/ ) . Briefly , animals ( female New Zealand white rabbits , 3 month old ) were immunized 3-times with 200 μg of recombinant protein ( Freund’s complete adjuvant was used for first injection and Freund’s incomplete adjuvant in the last 2 injections ) every 21 days and the serum collected at day 72 . Rabbit sera ( pre-immune and immune ) were shipped to our laboratory where purification of IgG was performed by affinity chromatography using a 5-ml HiTrap protein A HP column following manufacturer’s instructions ( GE Healthcare , Piscataway , NJ ) . Purified IgG protein concentration was determined by Nanodrop ND-1000 spectrophotometer . To test its blocking activity , 5 μg of purified rabbit anti-LuloHya IgG or rabbit pre-immune IgG as a control , were incubated with either LuloHya ( 10 nM final concentration ) or SGE ( 1 pair of SG ) for 30 min at 37°C . Hyaluronidase activity was measured by the hyaluronidase turbidimetric method described above . Western blot was carried out as described elsewhere [9] . Briefly , the content of 5 Lu . longipalpis SG and 100 ng of LuloHya were separated by NuPAGE protein gels . Proteins were transferred to a nitrocellulose membrane ( iBlot , Invitrogene ) that was blocked overnight at 4°C with blocking buffer: 50 mM Tris , 150 mM NaCl containing 5% ( w/v ) powdered nonfat blotting-grade milk ( Bio-Rad ) and 0 . 05% of Tween-20 ( Sigma ) . Mouse anti-LuloHya antibodies were diluted in blocking buffer ( 1:5 , 000 ) and incubated for 90 min . Goat anti-mouse conjugated to alkaline phosphatase ( Sigma ) diluted in blocking buffer ( 1:10 , 000 ) was used as a secondary antibody . Immunogenic protein bands were visualized by Western Blue Stabilized Substrate for Alkaline Phosphatase ( Promega , Madison , WI ) and reaction was stopped with distilled water . Samples of 10 head and thorax were collected from female and male sand fly adults and kept in Trizol reagent ( Life Technologies ) at -80°C until used . Total RNA , conversion to cDNA and PCR protocols were performed as described before [59] . Specific primers to target LuloHya gene were designed ( HyaP1F: 5’-ATTGGAAGGACGCTAAATGG-3’ and HyaP1R: 5’-CTTTCATTTCTTGCATTGGC-3’ ) . As a reference gene , Lu . longipalpis S60 gene was amplified with specific primers ( LuloS60F: 5’-CCTCGTTGATGATGATGTGG-3’ and LuloS60R: 5’-CGAAGAGTCCAGGCCAGTAG-3’ ) . PCR products were visualized under UV light in a 1 . 2% agarose gel ( E-gel , Invitrogen ) . To test the blocking activity of the anti-LuloHya in vivo , a blood feeding experiment was carried out . Briefly , C57BL/6 mice were intraperitoneally injected with either 100 μg of rabbit anti-LuloHya IgG or rabbit naïve IgG ( Sigma ) as a control group . After 15 minutes , each mouse was anesthetized and placed on top of a cage containing five-day-old female sand flies that had been deprived of water and sugar the day before the experiment . Lutzomyia longipalpis female adults were allowed to feed on the animals for 12 min at 27°C in the dark . Sand flies were scored under a stereo microscope as unfed or blood fed . In order to investigate whether vaccination with the salivary proteins may lead to a modified blood feeding success rate with epidemiological implications , a set of experiments on second blood feedings were carried out . Fully engorged sand flies were separated from a first blood feeding , as previously described . The following day they were transferred to oviposition pots . Six days after the first blood feeding , sand flies were allowed to feed on a new passively immunized animal for 12 min at 27°C in the dark . Right after the second feeding , sand flies were scored as unfed or blood fed . Blood fed sand flies were individually homogenized in 100 μl of PBS and cleared in a centrifuge for 5 min at 13 , 000 x g . Drabkin’s reagent ( Sigma ) was used to determine the hemoglobin content following the manufacturer's protocol with minor modifications . Each sample ( 40 μl of supernatant from a homogenized sand fly ) was incubated for 15 min with 100 μl of Drabkin’s reagent in flat-bottom 96-well microtiter plates ( Corning , Costar ) . Samples were tested in duplicates and absorbance at 540 nm was determined using a VersaMax microplate reader ( Molecular Devices ) . Five-day-old Lu . longipalpis females were blood fed on passively immunized mice with 100 μg of rabbit anti-LuloHya , anti-Lundep and naïve IgG as described before for at least 45 min to maximize the number of fully engorged sand flies . Three to four hours after feeding , fully blood fed females were selected under the stereo microscope and individually separated into 4 cm height x 2 cm diameter plastic vials filled with 1 cm of plaster of Paris . Sand flies were kept at 27°C with sugar ad libitum and allowed to lay the eggs . After 6 days , eggs were counted under a stereo microscope . Sand flies that died before laying eggs were excluded from the counting . Two independent experiments were carried out . Leishmania major parasites ( WR 2885 strain , Walter Reed Army Institute of Research ) were cultured in Schneider’s medium supplemented with 10% heat-inactivated fetal bovine serum , 2 mM L-glutamine , 100 U/ml penicillin , and 100 μg/ml streptomycin . Metacyclic promastigotes were negatively isolated from stationary cultures with peanut agglutinin ( Vector Laboratories , Inc . , Burlingame , CT ) . Parasites were resuspended in phosphate saline buffer and counted in a Neubauer chamber . Five-week-old mice of two different strains were used . C57BL/6 mice were purchased from Charles River Laboratories ( Wilmington , MA ) while the B-cell-deficient B6 . 129S2-Ighmtm1Cgn/J mice came from Jackson Laboratory ( Bar Harbor , ME ) . Mice were IM immunized with 10 μg of LuloHya , Lundep [9] or PBS as the control group in combination with Magic Mouse Adjuvant ( CD Creative Diagnostics , Shirley , NY ) . Mice were boosted 3 weeks after the first immunization . Ten days later and coincident with the antibody production peak , mice were challenged with L . major metacyclic parasites . Both mice ears were intradermally inoculated with 10 μl containing 1 , 000 parasites and the content of one salivary gland from adult female Lu . longipalpis using a 29-gauge needle . Ear lesions were weekly monitored during 8 weeks by measuring ear thickness with a Vernier caliper ( Mitutoya America Corporation , Aurora , IL ) . Parasite load in the ears was determined by limiting dilution assay as described elsewhere [9] . Briefly , homogenized ears were four-fold serially diluted with supplemented Schneider’s medium in microtiter culture plates . After 7 days of incubation at 27°C , wells were examined for motile promastigote presence under an inverted microscope . Parasite burden was expressed as the number of parasites per ear , considering that the last positive dilution contained at least one living parasite . Cross reactivity between antibodies against LuloHya or Lundep and L . major was determined by ELISA according to Chagas et al [59] with minor modifications . Briefly , microtiter flat-bottom plates ( Maxisorp , Nunc , Roskilde , Denmark ) were coated with 100 ng of recombinant proteins ( LuloHya or Lundep ) and 1 , 2 and 5 μg of L . major extract per well in carbonate-bicarbonate buffer pH 9 . 5 ( Sigma ) at 4°C for 16 h . Leishmania major extract was obtained by disrupting the cultured promastigotes by 3 cycles of freezing and thawing with liquid nitrogen and a 60°C water bath . Plates were blocked with 5% BSA in TBS ( 25 mM Tris , 150 mM NaCl , pH 7 . 4 ) for 2 h at room temperature . After 3 washes with TBS supplemented with 0 . 05% ( v/v ) Tween ( TTBS ) primary antibodies ( mouse anti-LuloHya and mouse anti-Lundep [9] , both diluted at 1:1 , 000 in TTBS ) were added . Mouse anti-tubulin , beta , clone KMX-1 ( MAB3408 , lot 2452493 , EMD Millipore ) , 1:2 , 000 in TTBS , was used as a positive control for parasite recognition . After 1 h incubation and further washing , alkaline phosphatase-coupled anti-mouse IgG ( 1:10 , 000 in TTBS , Sigma ) was added . Following another washing cycle , plates were developed with stabilized p-nitrophenyl phosphate ( Sigma ) and absorbance was measured at 405 nm in a VersaMax microplate reader ( Molecular Devices ) after a 15-min incubation . The amount of LuloHya in the salivary glands of Lu . longipalpis was assessed by ELISA , as previously described . Plates were coated with a serial dilution of recombinant LuloHya protein ( 50–0 . 78 ng ) to generate a standard curve which was used to infer the amount of LuloHya in Lu . longipalpis SGE obtained from 10 and 5 salivary glands in two independent experiments ( linear fit , R2 = 0 . 9909 ) . Leishmania major promastigotes from a stationary culture were collected and fixed in 4% paraformaldehyde ( Sigma ) . Immediately , 200 μl of 107 parasites/ml were dispensed into each well of an 8-well chamber slides ( Lab-Tek , Nunc , Thermo Fisher Scientific ) and incubated for 24 h at 4°C . After 3 washes of 10 min with PBS , slides were blocked with 200 μl of 1% BSA , 0 . 5% Triton X-100 in PBS ( blocking buffer ) for 30 min . Mouse anti-LuloHya , anti-Lundep ( 1:1 , 000 ) and anti-tubulin , beta , clone KMX-1 ( EMD Millipore , 1:2 , 000 ) were diluted in blocking buffer and incubated at 4°C for 16 h . After 3 washes with blocking buffer , samples were incubated with 10 μg/ml rabbit anti-mouse IgG conjugated with Alexa Fluor 488 ( Life Technologies ) diluted in 0 . 05% Tween , PBS ( v/v ) for 1 h . Excess of conjugate was removed by 3 additional washes with blocking buffer and slides were mounted with ProLong Gold Antifade Mountant with DAPI ( Invitrogen ) . Differential Interference Contrast ( DIC ) and fluorescent images were acquired in a Leica EpiFluorescence Microscope , using an oil immersion 100X objective with a 1 . 6X digital magnification . A primary culture of HMVEC was obtained from Lonza . Cells were grown at 37°C in a 5% CO2 incubator with Endothelial Cell Basal Medium-2 ( EBM-2 , Clonetics , Lonza ) , supplemented with EGMTM-2 Single Quote ( Lonza ) and subcultured using the Clonetics ReagentPack ( Lonza ) . The day before an experiment , cells ( in their fourth passage ) were detached and distributed into a 12-well culture plate ( 5x10e5 cells/well ) . Complete medium was removed and replaced by incomplete medium ( EBM-2 without growth factors ) for 4 h . Starved cells were incubated with LuloHya , Lundep ( both at a final concentration of 1 μM ) or SGE of 10 pairs of Lu . longipalpis SG for another 4 h . Control wells were incubated with only incomplete medium . Cells were collected in Trizol reagent ( Invitrogen ) and total RNA isolation and cDNA conversion was prepared as described before [58] . Cytokine expression pattern was assessed using The Human Cytokines & Chemokines RT2 Profiler PCR Array PAHS-150ZD ( Qiagen , Valencia , CA ) that includes expression profiles of 84 key secreted proteins for immune response and other functions . Data analysis was carried out with RT2 Profiler PCR Array Data Analysis version 3 . 5 following the software guidelines ( SABioscience , Qiagen , https://www . qiagen . com/us/shop/genes-and-pathways/data-analysis-center-overview-page/ ) . Parameters were as follows: Ct cut-off was set at 35 cycles . All RT-PCR data were adjusted to the same threshold . After checking stable amplification of positive controls for all samples and absence of genomic DNA contamination results were normalized against the housekeeping genes HPRT1 ( Refseq No . NM_000194 ) and RPLP0 ( Refseq No . NM_001002 ) . Only genes with a fold change greater than 4 were considered . Expression levels of the cytokines CSF2 , CSF3 , CXCL1 , CXCL2 , CXCL8 and LIF were further validated by qPCR with specific primers ( Qiagen , Valencia , CA ) . HPRT1 was chosen as a reference gene . qPCR was performed as described before [58] . Isolation of the serine protease was achieved by cation exchange chromatography . Venom sample ( 5 mg ) from Crotalus oreganus helleri ( Southern Pacific rattlesnake ) were dissolved in 200 μL of 20 mM Tris-HCl pH 8 . 0 buffer and injected into a cationic exchange column ( Sulfopropyl Waters Protein Pak 7 . 5 x 75 mm-10 μm , Milford , MA ) equilibrated with 20 mM Tris-HCl , pH 8 . 0 buffer at a 1 mL/min flow rate . The eluting buffer was integrated linearly from 0 to 100% using a 20 mM Tris-HCl , pH 8 . 0 buffer containing 0 . 5 M NaCl . The proteins were eluted at a 1 mL/min flow rate over 90 min using a Waters 1525 binary HPLC system ( Milford , MI , USA ) . A Waters 2487 dual λ absorbance detector ( Milford , MI , USA ) was used to monitor absorbance at 280 nm and Waters Breeze software was used to control the HPLC system and store the data . Four μg of purified venom protein was transferred from an SDS-PAGE onto a polyvinylidene difluoride ( PVDF ) membrane ( Millipore Corporation , MA , USA ) using a Trans-Blot SD semi-dry transfer cell ( Bio-Rad , USA ) at 125 mA for 1 h . The membrane was stained with Coomassie blue R-250 for 5 min and destained with 50% methanol for 5 min . A target band was excised from the membrane and subjected to N-terminal sequence analysis using a PPSQ-33B protein sequencer ( SHIMADZU , Kyoto , Japan ) following the manufacturer’s instructions . Sequencing was performed for 14 cycles . The 14 N-terminal amino acid sequences were compared to the sequences in the GenBank database using GenBank BLASTP programs . A dermonecrotic assay was used to assess hemorrhage and edema induced by SGE and recombinant salivary proteins . SGE of two pairs of Lu . longipalpis SG , 10 μg of LuloHya or Lundep and a combination of 5 μg of each recombinant protein were injected intradermally into the ears of BALB/c mice along with 3 μg of venom serine protease ( HF ) or PBS as control . After 2 h , ears were excised and dermonecrotic lesions were recorded . Ears were formalin-fixed in our laboratory and histological preparation ( paraffin inclusion , sectioning and hematoxylin/eosin staining ) were processed by Histoserv Inc . ( Germantown , MD ) . Examination of histological samples were examined under the microscope at the Infectious Disease Pathogenesis Section ( NIAID , NIH ) . C57BL/6 mice ears were intradermally injected with LuloHya ( 10 μg and 1 μg ) , Lundep ( 10 μg and 1 μg ) , Lu . longipalpis SGE ( equivalent to 2 pairs of salivary glands ) . As negative controls , ears were injected either with PBS or with a non-related salivary protein from the mosquito Ae . aegypti which was expressed and purified in the same manner . After 2 h , mice were euthanized , and the two sheets of ear dermis were separated , deposited in PBS containing 0 . 2 mg/ml Liberase CI purified enzyme blend ( Roche Diagnostics Corp . ) , and incubated for 1 h at 37°C . Digested tissue was placed in a grinder and processed in a tissue homogenizer ( Medimachine; Becton Dickenson ) . Tissue homogenates were filtered using a 30 μm Filcon filters ( BD ) . The resulting single cell suspensions were first stained with the Fixable Yellow Dead Cell Stain Kit ( Invitrogen ) for 20 min . The suspension was then washed and incubated with anti-Fc ( CD16/32 ) antibodies to block non-specific binding . After 10 min , the cells were stained for Ly6C ( clone AL-21; FITC; BD ) , Ly6G ( clone 1A8; PE; BD ) and CD11b ( clone M1/70; PE-Cy7; BD ) for 30 min . Cells were gated based on forward scatter and side scatter parameters and further gated on live cells . Cells were acquired on a MACSQuant flow cytometer ( Miltenyi Biotec ) and data were analyzed with FlowJo Software 4 . 3 . GraphPad Prism v 7 . 01 ( GraphPad Software , Inc . , San Diego , CA ) was used to analyze data . Comparisons between study groups were determined with a 2-tailed t test with 2-way analysis of variance and 95% confidence interval . Statistical significance was set as p value <0 . 05 .
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Blood-feeding is key to sand flies reproductive success and an important link in Leishmania spp . transmission . While the sand flies attempt to modify the bite site to enhance blood feeding success , the host’s ability to react to injury becomes compromised and could facilitate pathogen invasion . In our model , several proteins found in the saliva of the New World sand fly Lutzomyia longipalpis contribute to enhance Leishmania major pathogenesis . Among these secreted salivary molecules , endonucleases ( Lundep ) and hyaluronidases ( LuloHya ) increase parasite virulence by destroying the neutrophil traps and disrupting the integrity of the extracellular matrix , respectively , leading to the diffusion of other salivary components; allowing the Leishmania parasite to evade the host immune response and to cause an infection . Immunization against these molecules significantly reduces L . major infection in mice . These vaccine candidates are intended to disrupt a very early step in the Leishmania transmission event , the internalization of parasites in neutrophils ( without being killed ) and spread from the inoculation site . The work presented here highlights the relevance of vector-based vaccine development to disrupt vector-borne diseases transmission .
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2018
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Immunity to LuloHya and Lundep, the salivary spreading factors from Lutzomyia longipalpis, protects against Leishmania major infection
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Ribonucleotide reductase ( RNR ) provides the precursors for the generation of dNTPs , which are required for DNA synthesis and repair . Here , we investigated the function of the major RNR subunits Rnr1 and Rnr3 in telomere elongation in budding yeast . We show that Rnr1 is essential for the sustained elongation of short telomeres by telomerase . In the absence of Rnr1 , cells harbor very short , but functional , telomeres , which cannot become elongated by increased telomerase activity or by tethering of telomerase to telomeres . Furthermore , we demonstrate that Rnr1 function is critical to prevent an early onset of replicative senescence and premature survivor formation in telomerase-negative cells but dispensable for telomere elongation by Homology-Directed-Repair . Our results suggest that telomerase has a "basal activity" mode that is sufficient to compensate for the “end-replication-problem” and does not require the presence of Rnr1 and a different "sustained activity" mode necessary for the elongation of short telomeres , which requires an upregulation of dNTP levels and dGTP ratios specifically through Rnr1 function . By analyzing telomere length and dNTP levels in different mutants showing changes in RNR complex composition and activity we provide evidence that the Mec1ATR checkpoint protein promotes telomere elongation by increasing both dNTP levels and dGTP ratios through Rnr1 upregulation in a mechanism that cannot be replaced by its homolog Rnr3 .
Telomeres are nucleoprotein complexes that protect the linear ends of eukaryotic chromosomes from nucleolytic degradation and unwanted DNA repair activities [1–3] . Due to the "end-replication-problem" telomeres shorten with every cell division [2] . Once telomeres reach a critical length , with progressive number of chromosome replications they can no longer maintain their protective function and induce a permanent arrest of the cell cycle which has been referred to as replicative senescence [4–7] . Most cancer cells overcome this limitation of replicative capacity by upregulating telomerase , an enzyme whose expression is usually restricted to highly proliferative cells or early developmental stages [8 , 9] . A minority of tumors makes use of an Alternative-Lengthening of Telomere ( ALT ) -mechanism , which is based on homology-directed repair ( HDR ) to re-elongate their telomeres [10 , 11] . Budding yeasts constitutively express telomerase; when telomerase subunits are deleted , a senescence-like growth arrest is induced after approximately 60–80 cell divisions [4 , 12] . Similar to ALT-positive tumors , single yeast cells called “survivors” manage to overcome this arrest by using HDR-mechanisms for telomere elongation [13–15] . Genetic screens in yeast have uncovered a large number of genes responsible for the maintenance of a constant telomere length ( Telomere Length Maintenance or TLM genes ) [16–18] . These genes affect diverse cellular pathways . Ribonucleotide reductase ( RNR ) is a universally conserved enzyme that converts ribonucleotides to 2’-deoxyribonucleotides and provides the precursors for the generation of dNTPs , which are required for both DNA synthesis and repair [19–21] . Budding yeast RNR is a tetrameric complex , which in the absence of DNA damage consists mainly of a homodimer of the large Rnr1 subunits and a heterodimer of the small Rnr2 and Rnr4 subunits . The Rnr1 subunit harbors the catalytic site and two effector sites , which regulate substrate preference ( specificity site ) and overall enzymatic activity ( activity site ) . A diferric tyrosyl radical is present in the small Rnr2 subunit and is essential for catalytic reduction of NDPs [19] . The Rnr2-paralog , Rnr4 , plays a structural role by stabilizing the tetramer complex and is crucial for the assembly and activity of the Rnr2 co-factor [22 , 23] . RNR abundance and activity is largely regulated by the Mec1ATR checkpoint , and its downstream effector kinases Rad53 and Dun1 . When cells enter S-phase and in response to DNA damage Mec1ATR upregulates RNR activity by inducing the degradation of the Rnr1 inhibitor Sml1 through its downstream kinase Dun1 , thus ensuring the constant supply of dNTPs required for replication and repair [24 , 25] . Dun1 activation also results in the degradation of Dif1 , leading to the release of the Rnr2 and Rnr4 subunits from the nucleus and thus promoting the formation of active RNR complexes in the cytoplasm [26 , 27] . Finally , Mec1ATR-Rad53 upregulates Ixr1 , a positive transcription factor of RNR1 [28] . An alternative large subunit , Rnr3 , is usually expressed at very low levels under normal growth due to transcriptional repression by the Crt1/Rfx1 protein but is highly upregulated in response to DNA damage in a Mec1ATR checkpoint dependent manner [29 , 30] . In vitro , Rnr3 is about 100 times less active than Rnr1 but increases its activity when associated with Rnr1 , via a crosstalk between the activity site of Rnr1 and the Rnr3 catalytic site [31] . Changes in dNTP ratios that result from hypomorphic mutations in the large Rnr1 subunit have been proposed to affect the processivity of telomerase [32] . The concrete function of Rnr1 in telomere elongation , however , remained difficult to analyze since a full deletion of RNR1 results in cell death or a severe growth defect . Furthermore , the investigation of a direct connection between Mec1ATR and its downstream target RNR in telomere maintenance was impaired by the fact that survival of mec1Δ strains depends on dNTP level upregulation that is usually achieved by a co-deletion of the Rnr1-inhibitor SML1 [33] . In this work we expressed an alternative Rnr3-containing RNR complex , which allowed us to study the consequences of a complete loss of Rnr1 function on telomere length regulation . Surprisingly , we found that Rnr1 activity not only promotes telomere maintenance by telomerase but is absolutely required for the sustained telomerase-dependent elongation of short telomeres . We demonstrate that a loss of Rnr1 activity specifically impairs the elongation of short telomeres by telomerase but leaves ALT-pathways unaffected . By using alternative ways to suppress the lethality of mec1Δ mutants we show that sustained telomere elongation by telomerase is promoted by the upregulation of Rnr1 activity , which also maintains high dGTP ratios . Furthermore , we demonstrate that telomere over-elongation is regulated by Mec1ATR-Dun1 pathway in an Rnr1-specific mechanism that cannot be replaced by an upregulation of Rnr3-produced dNTPs . As rnr1Δ mutants do maintain functional telomeres , but are unable to elongate them further , our results distinguish between two different telomerase activity “modes” , one involved in telomere maintenance , and another responsible for sustained elongation/repair .
Budding yeast RNR is a tetrameric complex , which during S-phase consists mainly of a homodimer of Rnr1 and a heterodimer of Rnr2 and Rnr4 subunits ( Fig 1A ) . The deletion of RNR1 results in an impaired growth phenotype: in some yeast genetic backgrounds ( e . g . W303 ) the loss of RNR1 results in cell death , whereas in others , as the BY4741/2 background used here , cells grow slowly , forming extremely small colonies presumably because of a leaky transcription of the alternative Rnr3 subunit [32] . Spontaneous suppressors that allowed better growth of rnr1Δ mutants were found to contain a premature stop mutation in the CRT1 gene that leads to the expression of a truncated Crt1 protein ( Fig 1A ) . This mutation , as well as a full deletion of CRT1 , efficiently suppressed the slow growth phenotype of rnr1Δ mutants after meiotic segregation and tetrad analysis ( Fig 1A ) and resulted in a clear elevation of Rnr3/4 protein levels ( S1A Fig ) . We decided to make use of this system to study the effect of the absence of Rnr1 function on the maintenance of telomeres by telomerase . For this aim , an RNR1/rnr1Δ CRT1/crt1Δ double heterozygote strain was subjected to meiosis , and haploid progeny of all combinations was tested for the ability to maintain telomere length after approximately 30 and 35 cell divisions . Consistent with previous results [32 , 34] , we observed that the absence of Rnr1 resulted in pronounced telomere shortening ( Fig 1B ) . To our surprise , however , an upregulation of Rnr3 expression ( CRT1 deletion ) did not substantially alleviate the telomere shortenings of rnr1Δ mutants . Thus , in contrast to cell viability , which was largely rescued by an upregulation of RNR complexes containing only Rnr3 as large subunits ( Fig 1A ) , the telomere length defect could not be restored in rnr1Δ mutants . We conclude that Rnr1-containing RNR complexes are more effective in the maintenance of telomere length than RNR complexes containing Rnr3 . Next we investigated how these changes in RNR complex composition affect overall cellular dNTP levels in these mutants ( see S4 Table ) . Consistent with previous observations [32 , 34] , cells lacking Rnr1 ( wild type for CRT1 ) , showed a reduction in cellular dNTP levels and particularly low dGTPs ( Fig 1C ) . Interestingly , the upregulation of Rnr3-containing RNR complexes by a deletion of CRT1 was sufficient to restore dATP , dTTP , dCTP but not dGTP levels above wild-type-levels in the absence of Rnr1 ( Fig 1C ) . Thus , Rnr3-containing RNR complexes appear to be less efficient in the generation of dGTPs when compared to Rnr1-containing RNR complexes . In order to determine potential long-term effects of the absence of Rnr1 on telomere length and to validate that the short telomere phenotype of rnr1Δ crt1Δ mutants is caused by a loss of the enzymatic function of Rnr1 , we expressed wild type RNR1 or a catalytically inactive version of Rnr1 ( C428A ) in rnr1Δ crt1Δ mutants for 200 generations . Notably , rnr1Δ crt1Δ mutants did not show any signs of replicative senescence but were able to maintain short telomeres even after 200 generations ( Fig 1D ) . Thus , Rnr3-produced dNTPs are sufficient for compensation of the “end-replication-problem” . Expression of a wild type RNR1 was able to complement the telomere defect of the rnr1Δ crt1Δ strain and a telomere length close to wild type ( crt1Δ , compare Fig 1B ) was restored after 200 generations ( Fig 1D ) . In contrast , telomeres did not become re-elongated in rnr1Δ crt1Δ mutants expressing the catalytically inactive Rnr1-C428A allele . We also assessed potential long-term effects of the loss of Rnr3 function on telomere length . In contrast to a loss of Rnr1 , a deletion of RNR3 did not result in telomere shortening after 200 generations ( S1B Fig ) . Homology-directed repair ( HDR ) between short telomeres can contribute to their maintenance , in addition to telomerase [35] . In order to investigate whether HDR maintains the telomeres of rnr1Δ crt1Δ mutants , we generated heterozygous diploid RNR1/rnr1Δ CRT1/crt1Δ RAD52/rad52Δ mutants and assessed the telomere shortening in the derived spore colonies after meiotic segregation . The absence of RAD52 , a gene essential for HDR , did not exacerbate the loss of telomeric sequences in rnr1Δ crt1Δ double mutants or lead to senescence ( S1C and S1D Figs ) . These results suggest that an Rnr1-specific activity of RNR is required to maintain wild type telomere length by telomerase . Rnr3-containing RNR complexes appear to function less efficiently in this telomere maintenance mechanism . As a consequence telomeres are eventually maintained at a shorter length in the absence of Rnr1 but can become re-elongated when wild type Rnr1 is re-introduced ( Fig 1D ) . When cells enter S-phase Mec1ATR increases RNR activity by degrading the RNR inhibitor Sml1 ( 24 ) . Sml1 is a known inhibitor of Rnr1 [24 , 25] , but has also been reported to bind to Rnr3 [36] . Furthermore , in order to form the active RNR complex , Rnr1 and Rnr3 associate with the small subunits , which are regulated by Crt1 [30] . We were therefore interested in the question: to what extent Rnr1- and Rnr3- containing RNR complexes react to the degradation of Sml1 and Crt1 ? To this aim we quantified dNTP levels in rnr3Δ and rnr1Δ single mutants and compared them to rnr3Δ crt1Δ and rnr1Δ crt1Δ double mutants and rnr3Δ crt1Δ sml1Δ and rnr1Δ crt1Δ sml1Δ triple mutants ( Fig 2A ) . The deletion of CRT1 resulted in an increased activity of Rnr3-containing RNR complexes ( Fig 2A , compare rnr1Δ and rnr1Δ crt1Δ mutants ) but only mildly affected Rnr1 activity ( Fig 2A , compare rnr3Δ and rnr3Δ crt1Δ mutants ) . This small effect of a CRT1 deletion on Rnr1-activity might be caused by the upregulation of Rnr2 and Rnr4 , which might result in an increase in functional Rnr1-containing RNR complexes . Regarding the role of Sml1 , we found that in contrast to Rnr1-containing RNR complexes ( Fig 2A , compare rnr3Δ crt1Δ and rnr3Δ crt1Δ sml1Δ ) , Rnr3-containing RNR complexes do not substantially increase their dNTP synthesis rates in response to a deletion of SML1 ( Fig 2A , compare rnr1Δ crt1Δ and rnr1Δ crt1Δ sml1Δ ) . We conclude that the deletion of CRT1 primarily increases the activity of Rnr3-containing RNR complexes whereas the absence of Sml1 upregulates Rnr1 activity ( Fig 2A ) . As mentioned before , the single deletion of MEC1 results in cell lethality that can be suppressed by a co-deletion of SML1 . mec1Δ sml1Δ double mutants show a telomere length that is similar to that of wild type cells [37] . We speculated that in these strains the function of Mec1 in telomere elongation might be masked by the co-deletion of SML1 , which leads to the upregulation of Rnr1 activity independently of Mec1ATR . To test this possibility , heterozygous diploid MEC1/mec1Δ SML1/sml1Δ RNR1/rnr1Δ CRT1/crt1Δ cells ( of wild type telomere length ) were subjected to meiosis , dissected , and the ability to maintain telomeres was analyzed in the derived spores after 30–40 generations . We observed that rnr1Δ is synthetic lethal with mec1Δ even in the absence of SML1 or/and CRT1 ( S2A Fig ) . Thus , the epistatic relationships between rnr1Δ crt1Δ and mec1Δ sml1Δ in telomere maintenance could not directly be addressed . Consistent with previous results [30] , we found that a deletion of CRT1 suppresses the lethality of mec1Δ to a similar extent to that of a SML1 deletion ( S2A Fig ) . We decided to make use of these mec1Δ crt1Δ double mutants to investigate the effect of the absence of Mec1-mediated Sml1 degradation on telomere length . Indeed , in agreement with the loss of an upregulation of Rnr1 activity by Sml1 degradation that promotes telomere elongation , the telomeres of mec1Δ crt1Δ mutants displayed a shorter length and resembled the telomeres of rnr1Δ crt1Δ mutants ( Fig 2B ) . The co-deletion of SML1 and resulting upregulation of Rnr1 activity ( independent of Mec1 ) in mec1Δ sml1Δ crt1Δ mutants , however , resulted in an increased ability to maintain telomere length . Next , we determined the cellular dNTP levels in the different mec1Δ mutants ( Fig 2C ) . dNTP levels were upregulated above those of the wild type when SML1 was deleted , even in the absence of Mec1ATR ( mec1Δ sml1Δ and mec1Δ sml1Δ crt1Δ ) . Interestingly , the deletion of CRT1 was sufficient to upregulate the levels of all 4 dNTPs above wild type levels in mec1Δ mutants even in the presence of the active Sml1 inhibitor ( mec1Δ crt1Δ ) . Thus , an overall cellular increase in dNTP levels produced by Rnr3 is not sufficient to restore telomere length when Mec1ATR activity is lost and Rnr1 is inhibited by Sml1 . Changes in the ratios between dGTPs and the other dNTPs have been proposed to affect the processivity of telomerase leading to telomere length changes [32] . Since upregulation of Rnr3 ( in strains deleted for RNR1 ) results in a relative dGTP deficit ( Fig 1C ) , we asked to what extent the short telomere length of rnr1Δ , rnr1Δ crt1Δ mutants and mec1Δ crt1Δ mutants can be attributed to lower ratios of dGTPs . To this aim we analyzed telomere length in these mutants as a function of cellular dGTP ratios ( dGTP levels relative to overall dNTP levels ) . Strikingly , we found that telomere length strongly correlated with dGTP ratios ( Fig 2D; R2 = 0 . 8960 ) but not with overall cellular dGTP/dNTP levels ( S2B Fig ) . These results indicate that the Mec1ATR checkpoint pathway promotes telomere maintenance by telomerase specifically by keeping high dGTP ratios through Rnr1 activation ( Sml1 degradation ) in S-phase . The upregulation of Rnr3 by a deletion of CRT1 cannot replace this Rnr1 function in the absence of Mec1ATR and eventually results in shorter telomeres due to a reduction in cellular dGTP ratios ( Fig 2E ) . Telomerase-negative yeast cells have recently been reported to show increased replicative stress at telomeres that can be suppressed by a co-deletion of the Rnr1 inhibitor SML1 [38] . We were therefore interested in a potential role of Rnr1 in telomere maintenance that is independent of telomerase regulation . To this aim , the catalytic subunit of telomerase , EST2 , was deleted in a heterozygous diploid RNR1/rnr1Δ CRT1/crt1Δ mutant and the rate of replicative senescence of rnr1Δ crt1Δ est2Δ spores was compared to that of isogenic est2Δ and crt1Δ est2Δ controls . Freshly dissected spore colonies were incubated in liquid YPD medium and re-diluted every 24 hours to an OD of 0 . 02 . The cell density after 24 hours as a function of the number of cell divisions was used to visualize the onset of replicative senescence that is induced by telomere erosion ( Fig 3A ) . As expected , est2Δ and crt1Δ est2Δ double mutants senesced after approximately 55–60 cell divisions in the absence of telomerase activity and eventually recovered from the growth arrest by forming survivors ( Fig 3B ) . Interestingly , rnr1Δ crt1Δ est2Δ triple mutants showed a significant accelerated loss of viability and senesced at around generation 40 , forming survivors prematurely ( Fig 3B ) . In two out of five rnr1Δ crt1Δ est2Δ replicates survivors were already emerging in the first dilution suggesting that the loss of Rnr1 may have a synergistic effect on the promotion of HDR-mediated telomere lengthening in telomerase-negative cells ( see S3A Fig for individual curves ) . Of note , the loss of viability observed at early dilutions in rnr1Δ crt1Δ est2Δ triple mutants was not observed in telomerase-positive rnr1Δ crt1Δ cells , indicating that the senescence is telomere-related and not due to a checkpoint activation that is elicited by DNA damage elsewhere in the genome ( S3B Fig ) . To validate these observations , we directly determined the telomere lengths of the re-diluted mutants by Southern blotting ( Fig 3C ) using the biological replicates that best described the average curves in Fig 3B . After the first dilution , telomeres of rnr1Δ crt1Δ est2Δ mutants showed a similar length to those of est2Δ and crt1Δ est2Δ mutants . With ongoing dilutions , however , the telomeric signals differed significantly . Whereas telomeres of crt1Δ est2Δ and est2Δ mutants continued to shorten progressively and only formed survivors after approximately 30 more generations , the telomeres of rnr1Δ crt1Δ est2Δ mutants showed a heterogeneous length indicative of survivor formation already after 10 more cell divisions ( Fig 3C ) . Overall these results suggest that Rnr1 activity not only promotes telomere length maintenance by telomerase but functions additionally in a mechanism that is crucial for the maintenance of short telomeres in the absence of telomerase . The fact that rnr1Δ crt1Δ est2Δ mutants formed survivors prematurely shows that their telomeres can efficiently be extended by HDR-dependent telomere elongation mechanisms that do not require Rnr1 activity . Mutations in Rnr1 that reduce the ratio of dGTP synthesis have been reported to negatively affect the processivity of telomerase [32] . Since we observed lower dGTP ratios in rnr1Δ crt1Δ mutants ( Figs 1C and 2D ) we next asked whether an increase in telomerase activity would compensate for the complete loss of Rnr1 function at telomeres . Deletions of a number of yeast genes have been shown in the past to lead to telomere over-elongation [hereafter referred as “long tlm ( telomere length maintenance ) mutants”] in a telomerase-dependent manner . We tested whether deleting any of these genes would restore normal telomere size in rnr1Δ crt1Δ mutants . Pif1 acts as a negative regulator of telomere length by inhibiting telomerase processivity and therefore limits the length of the telomeric sequence that is added in a single telomerase reaction [39] . Elg1 is an RFC-like complex , which has been implicated in the coupling of semiconservative replication and telomerase activity [40 , 41] and acts in a different pathway of telomere elongation from Pif1 [42] . As expected , deletion of these genes in a wild type RNR1 background led to telomere elongation ( Fig 4A ) . In stark contrast , telomere length did not change when these long tlm mutations were introduced into rnr1Δ crt1Δ cells . In fact , telomeres remained as short as in the rnr1Δ crt1Δ double mutant controls even after 200 generations ( Fig 4A ) . We tested several additional long tlm mutations for their ability to elongate the short telomeres of rnr1Δ crt1Δ mutants and found that none of them was capable of causing telomere elongation ( S4 Fig ) . We next tried to force elongation in rnr1Δ crt1Δ mutants by expression of a fusion protein between the telomere-binding protein Cdc13 and either the regulatory subunit of telomerase , Est1 , or the catalytic subunit , Est2 . These fusion proteins result in a permanent recruitment of telomerase to telomeres and in telomere elongation in wild type cells [43] ( Fig 4B ) . Strikingly , telomeres of rnr1Δ crt1Δ and rnr1Δ crt1Δ elg1Δ mutants did not become elongated by expression of either of these fusion proteins ( Fig 4B ) . These results indicate that genetic alterations that elevate the activity or processivity of telomerase in the presence of Rnr1 are unable to do so in the absence of Rnr1 . Thus , rnr1Δ crt1Δ mutants are proficient in coping with the "end-replication-problem" , but unable to extend short telomeres in a sustained way . Our results so far highlighted a critical function of Rnr1-containing RNR complexes in the sustained elongation of short telomeres by telomerase ( Fig 4 ) . In order to determine whether this function of Rnr1 is regulated by the Mec1ATR checkpoint we deleted the Dun1 kinase , which acts downstream of Mec1ATR in the regulation of RNR activity ( Fig 1A ) [24 , 25 , 30] . Consistent with previous results [17 , 32 , 44] dun1Δ mutants showed reduced dNTP levels and short telomeres , which were suppressed by a co-deletion of the Rnr1 inhibitor SML1 ( Figs 5A and 5B ) . To address whether reduced dNTP levels also result in telomere shortenings in long tlm mutants we next combined the dun1Δ mutation with deletions of genes that negatively regulate telomerase-dependent telomere elongation . Indeed , the deletion of ELG1 only caused a mild telomere elongation in the absence of Dun1 suggesting that RNR activity contributes to telomere over-elongation in long tlm mutants ( Fig 5B ) . We have shown before that the upregulation of Rnr1 promotes telomere elongation in mec1Δ sml1Δ and mec1Δ sml1Δ crt1Δ mutants whereas the upregulation of Rnr3 is not sufficient to restore wild type telomere length in mec1Δ crt1Δ mutants ( Fig 2B ) . In order to determine whether the same applies for long tlm mutants , we deleted SML1 or CRT1 to elevate the activity/level of Rnr1 and Rnr3-containing RNR complexes in elg1Δ dun1Δ mutants . Interestingly , we found that the deletion of SML1 and therefore Rnr1 upregulation fully rescued the telomere elongation defect of elg1Δ dun1Δ mutants ( Fig 5B ) . Consistent with our results using a mec1Δ crt1Δ mutant ( Figs 2B and 2C ) the co-deletion of CRT1 did not affect the short telomere length of dun1Δ mutants , despite the fact that it was able to elevate cellular dNTP levels above those of the wild type ( Figs 5C and 5D ) . Thus , the short telomere phenotypes in the absence of Dun1 and Mec1 activity does not correlate with a general reduction of dNTPs but specifically with a reduction in Rnr1 activity . Moreover , in stark contrast to SML1 deletion , a deletion of CRT1 did not counteract the suppression of telomere elongation in elg1Δ mutants by a DUN1 co-deletion even though total levels of dNTPs were clearly upregulated in dun1Δ elg1Δ crt1Δ mutants ( Figs 5C and 5D ) . These results indicate that Dun1 promotes telomere elongation by a mechanism that specifically involves the upregulation of Rnr1 activity through Sml1 degradation . This activity of Rnr1 cannot be replaced by that of Rnr3 . The positive effect of Rnr1 activity on sustained telomere elongation was not restricted to an elg1Δ strain . Indeed , several other long tlm mutations , which affected telomere length in a pathway different from that of elg1Δ ( as indicated by the additive effects of the double mutation on telomere length ) showed impaired telomere elongation in a dun1Δ mutant background that could be restored by SML1 but not CRT1 deletion ( S5A Fig ) . Thus , an upregulation of Rnr1 activity through Dun1 seems to have a general positive effect on telomere over-elongation regardless of the original reason for telomere over-elongation in the respective long tlm mutant . Of note , in contrast to rnr1Δ crt1Δ mutants where telomerase-tethering did not result in any telomere elongation ( Fig 4B ) , the expression of Cdc13-Est1 or Cdc13-Est2 fusion proteins caused elongation in dun1Δ crt1Δ mutants even though at a reduced level compared to wild type ( S5B Fig ) . We conclude that , whereas Rnr1 is absolutely required for sustained elongation of telomeres , Dun1 promotes the sustained telomere elongation through Rnr1 activation but is not absolutely essential for it . We have demonstrated that the short telomere length in the absence of Rnr1 or Mec1ATR correlates with a reduction in dGTP ratios ( Fig 2D ) . Surprisingly , we found that elg1Δ single mutants despite showing over-elongated telomeres were characterized by a slight reduction in dGTP ratios ( ~13% ) presumably caused by an upregulation of Rnr3 , as indicated by a largely epistatic effect of deleting CRT1 in the elg1Δ strain on dNTP levels and ratios ( Fig 5C ) . Thus , slightly reduced overall dGTP ratios ( possibly above a certain threshold ) seem to be sufficient for sustained telomere over-elongation . Consistent with these results , dGTP ratios and telomere length of elg1Δ crt1Δ double mutants resembled those of elg1Δ mutants ( Figs 5C and 5D ) . Interestingly , we also observed that dun1Δ elg1Δ mutants showed higher dGTP ratios ( ~18% ) but reduced telomere length compared to wild type . Thus , an increase in dGTP ratios alone does not correlate with telomere elongation when overall dNTP levels are limiting due to a loss of Dun1 activity . Overall these results suggest that neither an increase in dGTP levels ( dun1Δ elg1Δ crt1Δ ) nor an increase in dGTP ratios alone ( dun1Δ elg1Δ ) is sufficient to promote telomere over-elongation in long tlm mutants in the absence of Rnr1 activation ( Figs 5C and 5D ) . We therefore propose that the upregulation of Rnr1 by Dun1 promotes the sustained elongation of telomeres in 2 ways—by increasing overall dNTP levels and simultaneously keeping dGTP ratios at levels close to wild type . The fact that dun1Δ elg1Δ crt1Δ mutant telomeres remained impaired in telomere elongation despite having ratios sufficient for telomere over-elongation ( ~13%; compare dGTP ratios and telomere length of elg1Δ crt1Δ mutants in Figs 5C and 5D ) and dNTP levels above wild-type-levels opens the possibility of an additional level of regulation of sustained telomere elongation that distinguishes between Rnr1- and Rnr3-produced dNTPs .
By suppressing the growth defect of rnr1Δ mutants through an upregulation of the Rnr1-homolog , Rnr3 ( via CRT1 deletion ) , we demonstrate that Rnr1 function not only promotes the maintenance of wild type telomere length but is also essential for telomere over-elongation by telomerase . Indeed , long tlm mutants , which exhibit elongated telomeres in a wild type background [16–18] , were unable to elongate their telomeres in the absence of Rnr1 ( Fig 4A and S4 Fig ) . Strikingly , even forced recruitment of telomerase did not result in telomere lengthening when Rnr1 was absent ( Fig 4B ) . The fact that rnr1Δ crt1Δ mutants are able to deal with the “end-replication-problem” , maintaining functional ( albeit short ) telomeres , but are unable to re-elongate their short telomeres lets us propose the existence of two different activity modes for telomerase , which we call “basal activity” and “sustained activity” ( Fig 6 ) . Whereas the first mode allows the maintenance of functional telomeres and compensation of the “end-replication-problem” in the absence of Rnr1 , it is unable to provide extensive telomerase activity . A previous report suggested a role of Mec1ATR in telomere elongation , as hypomorhic mec1 mutant alleles caused shorter telomeres [32] . Similarly , the short telomeres of dun1Δ mutants have been attributed to reduced dNTPs as both phenotypes could be suppressed by deleting the Rnr1 inhibitor SML1 [32 , 44] Here we have investigated the telomere length phenotype of a full deletion of MEC1 and deciphered the role of the Mec1-Dun1 downstream targets Rnr1 and Rnr3 in telomere length regulation . Surprisingly we found that Mec1-Dun1 regulates telomere length maintenance specifically by Rnr1 in a mechanism that cannot be efficiently replaced by Rnr3 . Indeed , mec1Δ mutants whose lethality was suppressed by upregulating Rnr3 ( CRT1 deletion ) were impaired in telomere length maintenance due to the persistence of Rnr1 inhibition by Sml1 , despite the upregulation of overall Rnr3-produced dNTP levels above those of wild type ( Figs 2B and 2C ) . The same was true when Rnr1 activity was reduced by a DUN1 deletion ( Figs 5C and 5D ) . Thus , the short telomere phenotype of mec1Δ and dun1Δ mutants are not primarily caused by a general reduction in cellular dNTP levels but rather correlate specifically with a reduction in Rnr1 activity . Consistent results were obtained when DUN1 was deleted in long tlm mutants since the full capacity to over-elongate telomeres in dun1Δ elg1Δ mutants could only be restored when SML1 but not when CRT1 was co-deleted ( Figs 5B and 5D ) . Thus , we conclude that similar to the maintenance of wild-type-length telomeres ( Fig 2B ) the Mec1-Dun1 pathway promotes telomere over-elongation in long tlm mutants specifically in an Rnr1-dependent mechanism . Why can’t Rnr3 compensate for this telomeric function of Rnr1 ? Previous experiments using hypomorphic rnr1 mutants attributed their short telomere phenotype to changes in dNTP ratios and , in particular , reduced dGTP levels relative to other dNTPs [32] . Indeed , consistent with this idea , we found that the short telomere length in rnr1Δ and mec1Δ crt1Δ mutants correlated with reduced dGTP ratios ( Figs 2D and 2E ) . Similarly , a reduction in dGTP ratios that was induced by a CRT1 deletion correlated with slightly shorter telomeres ( Fig 1B ) and a small reduction in telomere over-elongation after telomerase tethering ( Fig 4B ) . Of note , high dGTP ratios alone , however , appear to be not sufficient for sustained telomere elongation as dun1Δ mutants were characterized by elevated dGTP ratios and yet contained short telomeres ( Figs 5A and 5B ) . Similarly , when ELG1 was deleted in dun1Δ mutants , telomere elongation remained reduced despite the fact that dGTP ratios where clearly above wild-type-levels ( Figs 5A and 5B ) . This indicates that Rnr1 promotes telomere maintenance and over-elongation in long tlm mutants not only by keeping constant dGTP ratios but also by upregulating overall dNTP levels ( Fig 6 ) . Nevertheless , Rnr3-produced dNTPs cannot replace Rnr1-produced dNTPs in this mechanism , as shown by the inability of a CRT1 deletion to restore telomere length in dun1Δ single and dun1Δ elg1Δ double mutants ( Figs 5C and 5D ) . Overall these results suggest an additional level of regulation that distinguishes between Rnr1 and Rnr3-produced dNTPs during telomerase-dependent telomere elongation ( Fig 6 ) . At least two different explanations are conceivable: ( I ) Since Sml1 specifically inhibits Rnr1 ( Fig 2A ) and becomes degraded in a Mec1ATR-checkpoint-dependent manner in S-phase [24 , 25] a cell cycle regulated production of Rnr1-produced dNTPs might be crucial for efficient telomere elongation by telomerase . ( II ) A local ( nuclear or perinuclear ) activation of Rnr1 through Sml1 degradation might provide sufficient dNTPs for telomere elongation by telomerase . Indeed , even though Rnr1 has been shown to predominantly localize to the cytoplasm [45] , an increased nuclear localization has been reported in response to replicative stress caused by a treatment with the RNR inhibitor hydroxyurea [46] . Human RNR can also be localized to the nucleus [47] . Of note , we found that dun1Δ mutant telomeres ( compared to Dun1 positive cells ) were impaired in over-elongation when long TLM genes were deleted but still could become elongated to some extent ( Fig 5B and S5A Fig ) . Similarly , telomerase tethering was able to elongate telomeres of dun1Δ crt1Δ double mutants ( albeit at a reduced rate ) ( S5B Fig ) . This opens the possibility that there is a threshold of dGTP ratios that is required for the ability to elongate telomeres in the “sustained telomere elongation” mode , which is ensured by the presence of Rnr1 but does not fully depend on its upregulation through Dun1 . When dGTP ratios however drop due to a complete loss of Rnr1 function , telomeres can only be maintained by a basal activity using Rnr3-produced dNTPs ( Fig 6 ) . The concrete molecular mechanism through which Rnr1-produced dNTPs and changes in dGTP ratios affect telomerase dependent telomere elongation remains to be determined . The ability to processively add telomeric DNA depends on two types of telomerase-movements which have been referred as type I and type II translocation [48] . Whereas the first determines the ability to add nucleotides with a simultaneous movement of the RNA-DNA duplex relative to the active site , the second involves an unpairing of the RNA-DNA hybrid that is followed by a re-alignment of telomerase to allow additional cycles of repeat addition . Studies using hypomorphic rnr1 mutants showed that the nucleotide addition processivity ( type I translocation ) of telomerase correlates with changes in dGTP ratios in vivo [32] . Indeed , we find that a deletion of PIF1 did not result in telomere elongation in the absence of Rnr1 ( Fig 4A ) opening the possibility that Rnr1 function is a pre-requisite for elevated nucleotide addition processivity [39] . Interestingly , however , both types of translocations have been shown to be promoted by increasing dGTP concentrations in in vitro experiments using budding yeast telomerase [49–51] . Also , elevated dGTP concentrations have been shown to increase the processivity of recombinant Tetrahymena and human telomerase in vitro [50 , 52 , 53] . Further biochemical experiments are required to address the question of whether rnr1Δ crt1Δ mutants are defective in nucleotide addition and/or repeat addition processivity . Interestingly , upon telomerase ablation , an activation of the replication checkpoint adaptor Mrc1 and its downstream effectors Dun1 and Rad53 have been reported [54] . It therefore appears possible that there is a replication checkpoint-mediated upregulation of Rnr1 activity that facilitates fork progression through the short telomeric tracts and simultaneously promotes their telomerase-dependent extension . Consistent with this idea we found that the absence of Rnr1 accelerated the onset of replicative senescence and promoted survivor formation in telomerase negative cells ( Figs 3B and 3C ) . This may be explained by replication fork collapses in rnr1Δ crt1Δ est2Δ mutants that become restarted by HDR , leading to the premature formation of survivors in the absence of both telomerase and Rnr1 . Indeed , a checkpoint-dependent induction of RNR has recently been shown to promote the restart of stalled replication forks in response to replicative stress [55] . The fact that telomerase-negative rnr1Δ crt1Δ mutants are capable to efficiently elongate their telomeres by HDR further indicates that telomerase-dependent telomere elongation is more sensitive to a loss of Rnr1 activity than HDR-mediated elongation mechanisms . Human RNR complexes consist of hetero-oligomers of two large alpha subunits , RRM1 , and two small beta subunits , RRM2 or p53R2 , which become upregulated in response to DNA damage [56] . Interestingly , RNR has been shown to localize to sites of DNA damage in human cells [47] . Furthermore , stalled replication forks have recently been shown to increase telomerase recruitment in an ATR-dependent manner in human cells [57] . It will therefore be interesting to determine whether the increased localization of telomerase to telomeres in response to stalled replication forks is caused by an S-phase checkpoint-mediated upregulation of RNR activity that promotes the sustained elongation of telomeres in higher eukaryotes . Furthermore , our results in budding yeast suggest that telomerase-positive cancer cells may be more sensitive to RRM1/RRM2 inhibition than ALT-positive cancer cells .
All strains were derived from the BY4741/2 ( MAT a/alpha his3Δ0 leu2Δ0 ura3Δ0 ) background . Detailed genotypes , primers and plasmids used in this study are listed in the S1–S3 Tables . Teloblots were carried out as previously described [18] . The telomeric probe was generated using the primers Y' element fwd / rev in a PCR reaction on genomic DNA of wild type cells ( see S2 Table for primer sequences ) . A size marker ( generated using primer Tel-cont' fwd / rev ) was used as reference . The size-control probe is a specific region of chromosome II ( positions 558490 to 559790 ) and detects two bands in the XhoI digested genomic DNA ( 2044 and 779bp long ) . Radiolabeling was performed following the protocol of the Roche High Prime DNA labeling KIT . Telomere length was measured with TelQuant [34] , a VisualBasic6 program specifically developed for measuring telomere length in yeast and graphically displayed using Prism ( GraphPad ) . Measurements were performed as previously described [58] . S4 Table shows dNTP levels and ratios , as well as telomere length , for all mutants analyzed . Spore colonies of dissected diploids were re-suspended in water and diluted in 5 ml YPD to a final concentration of OD600 = 0 . 02 . After 24 hours of growth at 30°C absorption at 600 nm was measured . Cultures were re-diluted to an OD600 = 0 . 02 in 5 ml YPD and inoculated for further 24 hours at 30°C . Cell samples have been stored daily for genomic DNA extraction and Southern teloblots . Population doublings ( PD ) were calculated as log2 ( OD60024h/0 . 02 ) . Graphs were created with the Prism ( GraphPad ) software package . Protein samples for Western blotting were prepared as described before [59] . Proteins were resolved by SDS-PAGE 10–15% acrylamide gels and transferred to nitrocellulose membranes , blocked with 5% milk in TBST and immunoblotted with the indicated antibodies . Detection was carried out using the ECL SuperSignal detection system ( Thermo Scientific ) . Rabbit polyclonal anti-Rnr1 ( AS09 576 ) , anti-Rnr3 ( AS09 574 ) , and anti-Sml1 ( AS10 847 ) antibodies were produced by Agrisera , Sweden . For the detection of both Rnr4 and α-tubulin we used YL1/2 rat monoclonal antibodies ( Sigma ) .
|
Telomeres protect the ends of eukaryotic chromosomes and as such determine the replicative capacity of a cell . In budding yeast and approximately 80% of human tumors the enzyme telomerase maintains telomere length by adding newly synthesized repeats to telomeres using dNTPs generated by Ribonucleotide reductase ( RNR ) complexes . Similarly , telomerase activity can restore telomere length after more severe telomere shortenings that result from collapsed replication forks or lead to telomere over-elongation in the absence of negative regulators of telomerase . Here we provide evidence for two activity modes of telomerase that differentially depend on the major RNR subunit Rnr1 . We demonstrate that telomere maintenance and a compensation of the "end-replication-problem" is possible under conditions where Rnr1 activity is absent but that a sustained elongation of short telomeres fully depends on Rnr1 activity . We show that the Rnr1-homolog , Rnr3 , cannot compensate for this telomeric function of Rnr1 even when overall cellular dNTP values are restored .
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2017
|
Rnr1, but not Rnr3, facilitates the sustained telomerase-dependent elongation of telomeres
|
Olfactory sensory neurons connect to the antennal lobe of the fly to create the primary units for processing odor cues , the glomeruli . Unique amongst antennal-lobe neurons is an identified wide-field serotonergic neuron , the contralaterally-projecting , serotonin-immunoreactive deutocerebral neuron ( CSDn ) . The CSDn spreads its termini all over the contralateral antennal lobe , suggesting a diffuse neuromodulatory role . A closer examination , however , reveals a restricted pattern of the CSDn arborization in some glomeruli . We show that sensory neuron-derived Eph interacts with Ephrin in the CSDn , to regulate these arborizations . Behavioural analysis of animals with altered Eph-ephrin signaling and with consequent arborization defects suggests that neuromodulation requires local glomerular-specific patterning of the CSDn termini . Our results show the importance of developmental regulation of terminal arborization of even the diffuse modulatory neurons to allow them to route sensory-inputs according to the behavioural contexts .
Serotonin , 5-hydroxytryptamine ( 5-HT ) , an evolutionarily ancient monoamine , plays diverse roles in the brain [1] , [2] , [3] . In the mammalian brain , serotonin is implicated in the regulation of behavioural arousal and control of motor output [4] , [5] with a proposed phylogenetically ancient function in modulating a drive to withdraw from dangerous and aversive environments and seek contentment [6] . In the fruitfly , Drosophila melanogaster , serotonin regulates diverse aspects of behaviour such as aggression , sleep , circadian rhythm , learning and memory [7] , [8] , [9] , [10] , [11] . It is estimated that there is one serotonergic neuron per million in the mammalian central nervous system , yet , when axon terminals are examined in the rat cortex , as many as 1/500 are serotonergic [2] , suggesting that a small set of neurons may act through their broad arborization pattern to play roles in modulating many brain circuits . Understanding how serotonin and other neuromodulators function to modify intrinsic dynamic properties of neuronal circuits and thereby alter animal behaviour , is a daunting task . An iconic preparation in which this has been carried out is the circuit that drives pyloric rhythm in the crab/lobster stomatogastric system [12] , [13] . Such studies have led to the view that understanding the function of brain circuits not only requires a characterization of intrinsic dynamic properties of constituent neurons and their connectivity but also an understanding of how specific neurotransmitters and neuromodulators impinge on the circuit [14] . Functional imaging and electrophysiology suggests that serotonergic modulation of olfactory information is an important conserved feature [15] , [16] , [17] . In the Drosophila antennal lobe ( AL ) , innervated by ∼2500 olfactory sensory neurons ( OSNs ) , ∼150 projection neurons ( PNs ) , and ∼200 local interneurons ( LNs ) , the CSDn is the sole serotonergic neuron [18] , [19] , [20] . This and its accessibility to genetic manipulation [18] , [21] allow the development of the capacity for serotonergic modulation to be studied in the context of the well-characterized olfactory glomerular system . While the CSDn's axonal terminals spread over multiple glomeruli in the adult AL [18] , it also exhibits glomerular-specific differences in innervation pattern ( this study ) . Such wide-field arborizations , with variations in specific glomeruli , are seen in multi-glomerular olfactory LNs [22] , [23] , but the underlying mechanisms that regulate these arborizations have not been studied . This is in contrast with the many elegant studies that have led to significant understanding of mechanisms underlying targeting of the uni-glomerular OSNs and PNs [24] , [25] , [26] . The glomerular-specific pattern of wide-field interneurons is also likely to be important for their function as context- specific modulators of olfactory information , a hypothesis that has not been tested . Serotonergic neurons have been suggested to act in a paracrine manner: serotonin-containing varicosities release serotonin that can diffuse away and act on extra-synaptically located receptors [27] . While the arbors of such diffuse neuromodulatory neurons are suggested to be distributed to optimize efficient coverage of brain regions , the heterogeneous distribution of the terminal arbors of the CSDn in the AL suggests the possibility that arborization in a specific glomeruli is an important functional feature and could be behaviourally relevant , a view which we test and show to be valid . In searching for the mechanistic underpinning of the CSDn's terminal aroborization pattern we homed in on Eph-ephrin signaling as a likely candidate . Eph receptors ( Eph ) form the largest family of receptor tyrosine kinases ( RTKs ) and mediate contact-dependent bidirectional communication between cells through short-range interactions [28] , . Such short-range interactions between axonal arbors and their target cells could be relevant for emergence of regional differences in the arborization pattern of neurons in the CNS . We find that an Eph/ephrin signaling-mediated repulsion plays a key role in glomerular-specific positioning of axonal terminals of the CSDn . Sensory neurons differentially express Eph , which interacts with Ephrin on the CSDn to establish glomerular-specific innervation pattern of the CSDn axonal terminals . Further , we show that this glomerular-specific innervation pattern of the CSDn allows it to modulate olfactory behaviour in an odor-specific manner . We have determined the function of the CSDn in modulating odor-guided behaviour and shown that its glomerular-specific modulatory properties are dependent on the developmental regulation of its terminal arborization . Since the CSDn is the only serotonergic neuron in the AL , our study behaviourally dissects out the role of this important neuromodulator in the olfactory system and shows , for the first time , how its function is developmentally put in place . Our results also point to how sensory neurons , which are targeted to specific glomeruli , could locally regulate terminal arbors of other wide- field neurons . Finally , we examine Eph-ephrin signaling at the resolution of a single neuron , for the first time , to show how short-range signaling can sculpt local pattern , and thereby , function .
We had earlier characterized the development the CSDn in Drosophila [18] , [21] . In these studies , the CSDn [18] is labeled using a combination of cis-FRT/FLP and Gal4/UAS method [31] , [32] . This method can result in activation of CD8::GFP reporter protein expression in the CSDn in one antennal lobe , while the neuron on the contralateral side remains unlabeled , thereby allowing the examination of its arbors without the pattern being obscured by its homolog in the other hemisegment . Although the CSDn's terminal arbors in the contralateral AL innervate all glomeruli [18] , a closer examination showed clear glomerular-specific differences in the innervation pattern ( Figure 1A , 1E ) . We focused on glomeruli whose function in olfactory perception is well established in behavioural assays allowing us to correlate connectivity of the CSDn with its function in modulating behaviour . We therefore analyzed the VA1d , DA1 , VA1l/m , DL3 , which respond to fly- derived odors [33] . Of these , sensory neurons innervating DA1 and DL3 respond to the pheromone cis-vaccenyl acetate - cVA [33] , [34] , [35] . We also examined the V glomerulus , which responds to Carbon dioxide ( CO2 ) [36] , [37] . Quantification of axonal branch tip number of the CSDn in these glomeruli demonstrated prominent glomerular-specific differences in its innervation pattern: VA1d and V were innervated by many arbors while DA1 , VA1l/m and DL3 received fewer inputs from the CSDn ( Figure 1A , 1E; Figure S1 and Table S1 ) . In order to understand the cellular and molecular mechanism ( s ) underlying such differences in innervation pattern of the wide-field neuron we analyzed the possible role of signaling molecules and observed a clear disruption of this pattern in Ephrin hypomorphs ( Figure 1B , 1F; Figure S1 and Table S1 ) . Axonal branch tip number increased dramatically in DA1 , VA1l/m and DL3 glomeruli of Ephrin hypomorphs while innervations to glomeruli VA1d and V is comparable to controls ( Figure 1B , 1F; Figure S1 and Table S1 ) : The glomeruli that normally had fewer arbors of the CSDn ( DA1 , VA1l/m and DL3 ) were densely innervated in Ephrin hypomorphs , whereas arbors in densely innervated glomeruli ( VA1d and V ) remained unchanged in this mutant . Further , CSDn-specific expression of Ephrin rescued glomerular-specific innervation pattern defects observed in Ephrin hypomorphs ( Figure 1C , 1D , 1G; Figure S1 and Table S1 ) suggesting that Ephrin is required autonomously in the CSDn although it is widely expressed in the developing AL ( Figure 1H–1L ) . Overexpression of Ephrin in the CSDn did not change overall pattern of axonal branch tip distribution although a small decrease in final branch tip number was observed ( Figure 1C , 1G; Figure S1 and Table S1 ) . This reduction in the overall branch tip number could either be due to increased Eph-mediated repulsion or due to other as yet unknown molecular interactions within the AL . While Ephrin was required in the CSDn for positioning its terminal arbors in a glomerular-specific manner ( Figure 1A–1D and 1F–1G ) , expression analysis showed that it is uniformly distributed in the developing AL ( Figure 1H–1L ) and thus may not provide the positional information for glomerular-specific branching . We therefore examined the expression of Eph , the receptor for Ephrin , in the developing AL . Interestingly , Eph expression , as revealed by an Ephrin-Fc probe [38] , was detected in a small subset of glomeruli within the developing AL from 50 h after puparium formation ( 50 hAPF; Figure 2A–2D ) . Most prominent Eph expression was detected in DA1 , VA1l/m and DL3 glomeruli . These are the same glomeruli that receive fewer arbors of the CSDn in control animals and show substantial increase in innervation by the CSDn in Ephrin hypomorphs . The observation of commissural expression of Eph ( arrow in Figure 2C and 2E ) along with the above glomerular specific pattern suggests that the OSNs are the source of Eph . Consistent with this interpretation , targeted expression of EphRNAi in sensory neurons ( pebbled-Gal4/+; UAS EphRNAi/+ ) abolished Eph expression in the AL ( Figure 2E–2F; Figure S2 ) . Targeted misexpression of Eph in sensory neurons ( pebbled-Gal4/+; UAS Eph/+ ) lead to Ephrin-Fc labeling in the whole AL , further validating the specificity of the Ephrin-Fc probe ( Figure S2 ) . Targeted expression of the EphRNAi in the projection neurons or in the local interneurons did not affect glomerular-specific Eph expression ( data not shown ) . Furthermore , in amos mutant animals , of the genotype amos1/Df ( 2L ) M36F-S6 [39] , which lack most OSNs , the AL expression of Eph is also substantially reduced ( Figure 2G–2H ) . Taken together , we conclude that Eph is expressed by a small set of sensory neurons and enriched in cognate glomeruli that received reduced arbors of the CSDn compared to other glomeruli where Eph levels are low . Ephrin expressed by the CSDn may initiate repulsive interactions upon encountering high levels of Eph on sensory neurons . This hypothesis predicts that high levels of Ephrin ectopically expressed in other interneurons in these glomeruli would result in their arbors being repelled by high Eph expression . To test this hypothesis , we overexpressed Ephrin in PNs and focused our analysis on their arbors in the high Eph-expressing VA1l/m glomerulus , visualized using the Or47b::rCD2 strain ( Gal4-GH146 , UASmCD8::GFP; Or47b::rCD2; UASEphrin ) . Indeed , targeted overexpression of Ephrin in PNs resulted in a drastic reduction of PN innervations in the VA1l/m glomerulus ( Figure 2I–2J ) , consistent with the view that Eph-ephrin signaling mediates a repulsive interaction within the developing AL . Similar effect of Ephrin misexpression on PN arborization was observed in other high-Eph expressing glomeruli , DL3 and DA1 ( Figure S3 ) . This suggests that under normal circumstances , CSDn-derived Ephrin could interact with sensory neuron-derived Eph to appropriately position terminals of the CSDn in a glomerular-specific manner . In order to directly assess the role of sensory neuron-derived Eph , we used a combination of Gal4/UAS and LexA/lexAOp dual expression system . We generated RN2flp , tub>stop>LexA::VP16; lexAOpCD2GFP line which labels the CSDn ( Figure 3A ) and showed a clear glomerular-specific arborization pattern similar to that seen in the GAL4 reporter ( Figure 3B , 3D ) . OSN-specific knockdown of Eph , achieved by targeted expression of EphRNAi in OSNs driven by the pebbled-Gal4 , leads to increased innervation of CSDn in DA1 , VA1l/m glomerulus ( Figure 3C , 3D; Figure S1 ) similar to the phenotype that we observed in Ephrin hypomorphs ( Figure 1 ) . Such a change was also seen for DL3 glomerulus ( Figure 3D , Figure S1 ) . These results implicate OSNs in a previously unknown role in the development of a central neuron through their regulated expression of Eph . OSN terminals enter the lobe at 22 h APF and are key components of glomerular development [40] . OSN expression of Eph in the developing antennal lobe becomes prominent after 50 hAPF ( Figure 2A–2D ) . To further validate the role of OSNs in CSDn patterning , we examined the CSDn arborization pattern in animals developing without antennae [41] and thus without the antennal OSNs ( Figure 3F ) or in animals in which antennae are transformed to legs ( Figure 3H ) . In both the cases , the innervation pattern of CSDn in the antennal lobe was uniform ( Figure 3F , 3H ) , unlike control animals where axonal terminals exhibited glomerular-specific differences in the innervation pattern ( Figure 3E , 3G ) . Taken together , these data substantiate a role for OSNs in providing positional cues necessary for glomerular-specific arborization patterning of an identified central serotonergic neuron . Eph-ephrin interactions can lead to diverse outcomes in terms of attraction , repulsion and cell adhesion in a context-dependent manner . High affinity Eph/ephrin signaling is known to initiate contact-dependent repulsion while low level signaling can lead to attraction and directed neuronal branch extension [42] , [43] , [44] , [45] . We further investigated how Eph/ephrin signaling levels could control the final arborization pattern of the CSDn . To achieve a complete loss of Eph-ephrin signaling we utilized an allele EphX652 in where Eph expression is completely abolished [38]; Figure S4 . Since Eph is expressed in the OSNs , we first tested the role of Eph during the development of OSNs and projection neurons ( PNs ) , the primary synaptic partners of the OSNs . Terminals of OSNs ( Figure 4A–4F ) and uniglomerular PNs ( Figure 4I–4J ) develop normally in Eph null animals suggesting that Eph is not necessary for development of these components of the AL circuit , which have uniglomerular projections . Next , we asked if misexpression of Eph in the majority of the OSNs during a time window when Eph is expressed in very few glomeruli would affect OSN patterning in the AL . To this end , we used Or83bGal4 [46] , which drives Gal4 expression in ∼80% of the OSNs starting from mid-metamorphosis . Misexpression of Eph using Or83bGal4 did not affect OSN patterning in the AL ( Figure 4G , 4H ) . Overall , these observations allow us to argue that Eph signaling does not play any obvious role in OSN/uniglomerular PN patterning within the AL . Surprisingly , terminal innervations of CSDn were reduced in animals homozygous for EphX652 to all the glomeruli examined ( Figure 5B , 5H and Table S1 ) . This was in marked contrast to the situation where Eph-ephrin signaling was not completely abolished but only reduced in the Ephrin hypomorphs ( Figure 1B ) or where Eph was knocked down specifically in the OSNs ( Figure 3C ) . The CSDn innervation pattern was differentially affected in the latter cases and glomeruli with normally less innervations showed a substantial increase , leaving the densely innervated glomeruli unaffected . These differences in phenotypes indicate a requirement of Eph signaling at multiple stages of the CSDn development . Complete loss of Eph throughout development might influence overall branching and hence we observed reduced arborization of the CSDn in Eph null . On the other hand , OSN-derived Eph controls glomerular-specifc innervation of the CSDn during pupal stages . In any event , our observations suggest a key role for Eph/ephrin pathway in patterning axonal terminals of the CSDn . To further test this , we ectopically expressed Eph in the CSDn . Targeted ectopic expression of Eph in the CSDn resulted in striking reversal of axonal branch tip distribution in the glomeruli ( Figure 5C , 5I and Table S1 ) . Axonal terminals of Eph-expressing CSDn preferentially innervated glomeruli with high Eph and completely avoided VA1d glomerulus , which expresses low Eph ( Figure 5C , 5I ) . This exquisite mistargeting further strengthens the suggestion that levels of Eph/ephrin signaling control glomerular-specific innervation of this serotonergic neuron . One possibility is that preferential targeting to high Eph-expressing glomeruli could be due to attractive homotypic interactions between Eph expressing neurons . Eph-mediated homotypic interactions have been shown to promote cell adhesion between Eph-expressing cells during rhombomere-boundary formation in zebrafish [47] . Another possibility , not excluding the first , is that Eph-ephrin interaction within CSDn could result in ‘cis inhibition’ [28] , [48] of the signaling pathway due to simultaneous presence of Eph and ephrin in the same cell , which in turn could reduce repulsive interaction and increase the attractive one . We next examined if the developmental timing of the CSDn arborization is consistent with OSN derived Eph playing a role in the process . Glomerular-specific innervation of the CSDn involves directed growth of terminals to the target glomeruli . At 50 h after puparium formation ( APF ) , very few arbors of CSDn were seen in the regions of the antennal lobe where VA1l/m , VA1d , DA1 and DL3 glomeruli were developing ( Figure 5D ) . An adult-like pattern was seen by 70 h APF without an intermediate stage where excess arbors were seen ( Figure 5E ) . Terminals of the CSDn failed to innervate these glomeruli in Eph null animals ( Figure 5F–5G ) . The time course of the development of glomerular-specific arborization of the CSDn coincided with the expression profile of Eph , described above and is consistent with a role for Eph/ephrin pathway as regulators of this process . These observations demonstrate that the final arborization of the CSDn is not an outcome of excess growth in every glomerulus , followed by pruning but is an outcome of the repulsive signaling operating in high-Eph expressing glomeruli , which restrict the growth of CSDn terminals during development . We next examined if the extent of glomerular-specific arborizations of the CSDn has functional implications in behaving animals . To address this , an understanding of the role of the CSDn in odor-guided behaviours in Drosophila is first required . The CSDn is the only identified source of serotonin in the Drosophila AL [18] , [49] suggesting an important role for this neuron in modulating olfactory perception . Although functional imaging studies have demonstrated that serotonin can change response properties of neurons in the AL [16] , a direct demonstration of behavioural requirement of this neuron is lacking . We used the R60F02Gal4 strain [50] which consistently labels the CSDn bilaterally in the adult brain ( Figure 6A ) , providing an advantage over the cis-FRT/FLP method , for behavioural analysis . R60F02Gal4's restricted expression in the central brain , with prominent expression in the CSDn and only a few arborizations in the suboesophageal ganglion provides an excellent reagent for behavioural experiments ( Figure 6A ) . We validated that R60F02Gal4 indeed labels the CSDn in two ways . Firstly , the anatomy of its projections ( Figure 6A , 6Ai and 6Aii ) was similar to the described characteristic anatomy of the CSDn [18] . Furthermore by examining serotonin immunoreactivity in a genetic background where R60F02Gal4 expresses GFP , it was found that the only serotonin positive neuron in the AL co-localized with the GFP ( Figure 6Aiii–vi ) confirming that the Gal4 indeed specifically labels the CSDn . For behavioural analysis , we selected two odorants; CO2 ( perceived by the low Eph-expressing V glomerulus ) and cVA ( perceived by the high Eph-expressing DA1 and DL3 glomeruli ) as innervations of the CSDn in the cognate glomeruli have been characterized by us . The behavioural response of wild-type adult Drosophila towards these odorants and the underlying neural circuitry is understood in good detail [34] , [36] . CO2 is a repulsive stress pheromone in flies and is sensed by the V glomerulus [36] . Blocking evoked neurotransmitter release from the CSDn by targeted expression of tetanus neurotoxin light chain [TNTG; 51] rendered animals behaviourally more sensitive towards CO2 and these animals exhibited increased repulsion to CO2 compared to controls ( Figure 6B , p = 0 . 017 ) . Further , suppressing excitability of the CSDn by ectopic expression of an inward rectifying human K+ channel , Kir2 . 1 [52] in the neuron resulted in an increased CO2 avoidance behaviour ( Figure 6C , p<0 . 01 ) . Perturbation of neuronal activity during development has known consequences on the dendritic pattern of the CSDn [18] , [21] and could be argued that this affects the behaviour . In order to circumvent the behavioural effects deriving from a developmental requirement of neural activity we manipulated the CSDn activity only during adulthood by using the temperature-sensitive Gal80 repressor of Gal4 ( Gal80ts ) [53] . Adult-specific suppression of the CSDn excitability by overexpression of the Kir2 . 1 in adult flies lead to increased CO2 sensitivity ( Figure 6D ) suggesting that the CSDn function in modulating olfactory behaviour is required during adulthood . In order to further validate the view that behavioural defects are indeed through serotonin signaling , we analyzed the expression pattern and function of serotonin receptors in the AL and then manipulated them . A Gal4 reporter line for serotonin receptor 5-HT1BDro [9] labels a small set of local interneurons in the adult AL ( Figure 6E ) suggesting that these neurons could be possible downstream target of serotonin released by the CSDn . RNAi-mediated knock down of 5-HT1BDro [9] in 5-HT1BDro expression domain lead to an increase in CO2 avoidance behaviour ( Figure 6F ) . However , 5-HT1BDro is also expressed in the mushroom body neurons [9] , which are a crucial component of the olfactory circuit underlying olfactory learning and memory [54] , [55] . In order to define better , the domain of 5HT1BDro expression relevant in mediating CO2 avoidance behaviour , 5HT1BDro levels were ‘knocked-down’ using an RNAi construct [9] driven by the 5-HT1BDro-Gal4 driver in a context where Gal80 repressor of Gal4 is expressed under a mushroom-body promoter [56] . These animals will have normal 5HT1BDro in the mushroom body neurons , due to Gal80 repressing GAL4 expression in this tissue , but lowered expression in the olfactory local interneurons due to RNAi . Behavioural experiments show that these animals exhibit an increased CO2 avoidance behaviour . Taken together , these observations suggest that the CSDn releases serotonin as a neuromodulatory transmitter and serotonergic receptor-expressing local interneurons play an important role in CO2 sensitivity . Next , we tested the role of the CSDn in cVA-dependent courtship behaviour . cVA , a male pheromone , is transferred to females during mating and renders them less attractive to other males in subsequent encounters . Virgin males therefore , show reduced courtship towards cVA-treated females [35] . The males sense the presence of cVA through OSNs that target to DA1 and DL3 glomeruli [34] , [35] . Blocking neurotransmitter release from the CSDn by targeted expression of tetanus neurotoxin light chain in the CSDn resulted in reduced behavioural sensitivity towards cVA and these males exhibited increased courtship towards cVA-treated females compared to controls ( Figure 7A , p = 0 . 028 ) . Taken together , these experiments demonstrate a role for the CSDn in modulating olfactory perception of behaving animals in an odor-dependent manner . Having established a role for the CSDn function in odor-response modulation , we examined the basis for this modulation . Modulation could be achieved in a variety of ways , such as the differential expression of serotonin receptors in the AL or/and by the differential arborization ( as observed in the present study , Figure 1A ) , which in turn may result in differential levels of local serotonin release by the CSDn . Suppressing the function of the CSDn causes reduced behavioural sensitivity towards cVA , indicating that serotonin release is important for enhanced sensitivity towards cVA ( Figure 7A ) . The level of serotonin release in the cVA-specific glomeruli ( DA1 and DL3 ) is likely to be more in cases where there is an increase in the innervations of the CSDn to these glomeruli . Innervations in these glomeruli increase heavily in Ephrin hypomorphs compared to control ( Figure 1 ) predicting that Ephrin hypomorphs should be much more sensitive to cVA . This was indeed the case; Ephrin hypomorphs showed a remarkable behavioural sensitivity to cVA and thus showed highly reduced courtship towards cVA-treated females ( Figure 7B , p<0 . 001 ) . If increased behavioural sensitivity in Ephrin hypomorphs is indeed due to increased DA1/DL3 innervations by the CSDn then rescuing the CSDn branching pattern to control levels should show a rescue of the behavioural phenotype . Targeted expression of Ephrin in the CSDn in Ephrin hypomorphs leads to a partial rescue of the behavioural sensitivity of Ephrin hypomorphs towards cVA ( Figure 7C , p = 0 . 004 compared to Ephrin hypomorphs ) . This suggests that the increased sensitivity to cVA in the Ephrin hypomorphs is indeed due to the increased innervations of the CSDn . However , the absence of a complete rescue of the behavioural phenotype suggests the possibility that the terminals of other interneurons are defective in the relevant glomeruli in Ephrin hypomorphs . As mentioned earlier , the widespread expression of Ephrin in the lobe indicates that other interneurons may also require the molecule . Nevertheless , a partial behavioural rescue by Ephrin expression in the CSDn in Ephrin hypomorphs suggests that Eph/ephrin signaling has a role in development of the pheromone modulatory circuit and regulates correct positioning of neuronal arbors in a manner relevant for behaviour . Normal courtship in Ephrin hypomrphs ( male courtship index = 0 . 72±0 . 032; n = 33 ) is comparable to controls ( male courtship index = 0 . 76±0 . 037; n = 36 ) suggesting that these animals don't display a defect in courtship behaviour . A similar analysis could not be performed for Eph null animals as these showed severely reduced normal courtship ( data not shown ) . We next checked whether this is true for the other odor we have examined , CO2 . The CSDn innervations in the V glomerulus of Ephrin hypomorphs are comparable to controls ( Figure 1 ) and their response towards CO2 is also comparable to control animals ( Figure 7D , p = 0 . 98 ) . However , EphX652 null animals , which have reduced innervations of the CSDn in the V glomerulus show an increased repulsion to CO2 when compared to controls ( Figure 7E , p = 0 . 003 ) . This phenotype is comparable to what we observed upon silencing or blocking neurotransmitter release from the CSDn ( Figure 6 ) . Thus , the olfactory sensitivity towards CO2 changes only in the contexts where the CSDn branching has been affected in V glomerulus . Taken together , our data suggests that the serotonergic CSDn has a modulatory effect in olfactory behavioural sensitivity and glomerular positioning of its terminals during development is essential for its function in the adult .
Several studies on the OSN and PN targeting in the Drosophila antennal lobe have led to a view that PNs organize the coarse map of the antennal lobe and thus provide spatial information necessary for appropriate fine-targeting of other lobe neurons [26] , [62] . Glomerular organization of the antennal lobe is complete by ∼48 hAPF and synaptogenesis starts between 48 and 72 hAPF [63] . Our work shows that this developmental time window is not only relevant for synaptogenesis in the antennal lobe but also for OSN-driven patterning of wide-field interneurons . A small set of OSNs start to express Eph at the onset of the synaptogenic time window and provide spatial information to growing axonal terminals of the CSDn . Eph expressed by OSNs may not influence gross targeting of PNs as PN targeting occurs much earlier in the AL . However , OSN-derived Eph may regulate patterning of axonal terminals of other interneurons , which elaborate their branches during late metamorphosis . It will be interesting to see if selective Eph expression in the OSNs during the phase of synaptogenesis requires olfactory co-receptor expression or neuronal activity . That the CSDn is a modified larval neuron [18] and that the glomerular-specific terminal pattern is set-up during pupal development both raise the possibility that serotonin release from this neuron has a role in antennal lobe development and plasticity . This possibility emerges from the very elegant set of studies from the Beltz laboratory [64] , [65] , which demonstrate a role of serotonin through its receptors , in adult neurogenesis in decapod crustaceans . One possibility is that the CSDn acts in the Drosophila larva to influence neurogenesis in the adult , during larval or pupal life , by regulating the specific LN and PN stem-cell linages and their neuronal morphogenesis in the antennal lobe [22] , [23] , 66 . Another possibility , not excluding the first , is that serotonin is relevant to experience dependent changes in the glomeruli , such as observed in Drosophila [67] . We see no obvious alteration in the size of the antennal lobe in contexts where the CSDn function is blocked ( data not shown ) and , a detailed developmental role for serotonin is outside the scope of the current study . Nevertheless , the CSDn's singular presence in the antennal lobe make studying the developmental role of serotonin an attractive direction and an area that will surely be embarked on soon . In most brain regions closely studied , each neuromodulatory transmitter is usually released by more than one neuron and co-expression with other neurotransmitters is not uncommon [68] . The Drosophila antennal lobe is likely to be similar and a recent study using mass- spectrometry and genetic tools suggests presence of a large number of neuromodulators in the AL [69] . This makes the linking of the development of identified neurons with their role in behaviour difficult to tease apart . The CSD neuron is special in that it is the only serotonergic neuron that innervates the AL and does not appear to have a co-transmitter . The CSD neuron preparation is thus valuable in that it allows the examination of neuromodulation from development of its anatomy to the role of this anatomy in behaviour . While there may well be a matrix of neuromodulators which together function in the behavioural paradigms we have tested , our results on ablating the function of the CSDn suggest that this neuron is likely to be a key player . Serotonergic neurons usually have a diffuse neuromodulatory role in the CNS . In such contexts , serotonin is able to diffuse from the release site in order to act upon extra-synaptic receptors and serotonergic neurons often branch in a manner to have complete coverage of the neuropil [2] , [14] , [27] , [70] . The context-dependent response to serotonin is mediated by multiple serotonin receptors , which initiate diverse intracellular signal transduction pathways and also differ in their expression pattern in the central nervous system [71] , [72] . Our analysis at the resolution of an identified neuron suggests that contextual specificity is also regulated at the level of innervation pattern and connectivity of serotonergic neurons . Our data points to a general mechanism underlying the emergence of contextual specificity in neuromodulation: peripheral neurons developmentally regulate the extent of innervation by modulatory neurons , which in turn , regulate the extent of neuromodulation of specific sensory pathways and behavioural output in the adult . Our preparation allows the study of neuromodulatory regulation at every scale—from developmental anatomy to behaviour—and does so at the level of a single , identified neuron . A key gap in our study , which we recognize and are addressing as a longer-term direction , is the absence of a neurophysiological response . Published evidence demonstrates the physiological consequences of ectopic serotonin on the antennal lobe . Dacks et al used a genetically encoded Calcium indicator G-CaMP [16] to examine the responses of PNs to ectopic administration of serotonin . They argue that for some odors , serotonin could function by increasing projection neuron sensitivity . Importantly , they show that for odors that activate a wide-range of glomeruli , serotonin enhances PN responses in only some of these glomeruli . The natural suggestion from our study is that this differential alteration of PN responses in a glomerular-specific manner could be , at least in part , due to the specific arborization pattern of the CSDn termini . The response to serotonin is complex and not restricted to PNs alone . Dacks et al also demonstrate that serotonin enhances the responses of inhibitory LNs too . They argue that the effect of serotonin observed on PNs could be an indirect consequence of GABA from inhibitory LNs pre-synaptically acting on OSNs whose modulated function alters PN response . Experiments to test this or related models of serotonin dependent neuromodulation are technically challenging , require substantial time: Such experiments will require , for example , the measurement of OSN , LN and PN physiology when 5-HT receptors are blocked or absent in LNs . For now however , our findings that the CSD neuron's arborization affects behaviour in a manner similar to that seen when its activity is blocked or when 5-HT1BDro receptor levels are down-regulated combined with the studies from Dacks et al [16] strongly suggest that the developmental regulation of local serotonin activity on neurons of the antennal lobe is an important component in the fly's olfactory response . Why might flies differentially modulate two different olfactory responses ? While CO2 is a stress odor for the fruit fly , cVA detection provides information about its mate and thus , each eliciting very different kind of behavioural responses . The mechanism of olfactory processing of CO2 is distinct from that of most other odorants: olfactory perception of CO2 requires co-expression Gr21a and Gr63a which belong to the Gustatory Receptor ( GR ) family rather than the Olfactory Receptor ( DOR ) family [73] , [74] . Further , CO2 and cVA-sensing OSNs exhibit differences in GABABR expression and consequently employ heterogeneous GABA-mediated presynaptic gain control [75] . In a natural context in the wild , presence of multiple odorants is expected . Differential modulation of functionally distinct odor-processing pathways could be used to advantage for an animal in the wild allowing it to adapt and fine-tune innate behavioral responses according to its immediate environment . Our data points to an element in the complex set of parts which puts such a system in place during development . Another level of sophistication might be added to the olfactory circuit by differential expression of serotonin receptors .
RN2flp , tub>CD2>Gal4 , UASmCD8::GFP [18] and w; If/CyO , wg-Z; tub84B-FRT-stop-FRT-LexA::VP16 , RN2Flp ( this study; referred as RN2flp , tub>stop>LexA::VP16 in the manuscript ) flies were used for labeling the CSDn . Gal4-GH146 was a gift from RF Stocker [19]; Gal4-LN1 and Gal4-LN2 were provided by Kei Ito [23]; Or83b-Gal4 was kindly provided by LB Vosshall [46] . 5-HT1BDro-Gal4 and UAS 5-HT1BDro-RNAi lines were kindly provided by Amita Sehgal [9] . EphX652/CiD [38] , UAS Eph and UAS Ephrin lines were kindly provided by JB Thomas [38] , [76] . lexAop-rCD2::GFP was provided by Tzumin Lee [77] . Pebbled-Gal4 [78] was provided by Rachel Wilson . R60F02-Gal4 was a gift from Gerald Rubin and it was generated as described in [50]; the 892 bp enhancer fragment in R60F02 derives from the acj6 gene and is delineated by the PCR primers caccagtgtcctgccggcgggcgaaaaga and aggtgccgcaatggaagtccttttt . UAS TNTG and UAS TNTVIF were kindly provided by Sean Sweeney [51]; UAS Kir2 . 1 was a gift from Richard Baines [52] . amos1/Df ( 2L ) M36F-S6 was provided by Andrew German [39] . wglacZ was a gift from JK Roy [79] . MB-Gal80 [56] was a gift from Andre Fiala . Antp , EphrinKG09118 [38] and Or47b-CD2 were obtained from Bloomington Drosophila stock center , Indiana University , USA . wg1-16 was obtained from the Drosophila genetic resource , Kyoto , Japan . UAS EphRNAi ( v4771 ) was obtained from the Vienna Drosophila RNAi Center [80] . All flies were maintained under standard conditions at 25°C unless otherwise indicated . For pupal timing , white prepupae ( 0 h after puparium formation – 0 hAPF ) were collected and placed on a moist filter paper in humid conditions . This stage lasts for about an hour , thus setting the accuracy of staging; the pupal stage lasts 100 hours under conditions in our laboratory . The tub>stop>LexA::VP16 construct was created by replacing the Gal80 coding region of a pCasper-tub-Gal80 construct with >stop>LexA::VP16 . Therefore the LexA::VP16 was amplified via Polymerase Chain Reaction using the pBluescript-LexA::VP16 vector ( Lai and Lee , 2006 ) as a template with the following primers: forward primer-GGG CTA GAG CGG CCG CGG CTA GCG CTC GCG ATA AGC TT and reverse primer- CAA AGA TCC TCT AGA GCC CCC TAC CCA CCG TAC TC . The resulting NotI-NheI-LexA::VP16-Xba PCR fragment was ligated via NotI and XbaI in an open pCasper-tub-NotI-XbaI vector ( all enzymes from NEB ) . A successful ligation was verified via sequencing . The minimal >stop> cassette was inserted via ligation using the NheI side in front of the LexA::VP16 coding region . The orientation of the cassette was verified via digestion and sequencing . DNA for injection was purified using a Qiagen Midi Kit and transgenic lines were generated by BestGene Inc . , ( Chino Hills , CA , USA ) . Brains from 2–4 days old adults were dissected and stained as described in [81] . Primary antibodies used were mouse anti-Bruchpilot/mAbnc82 ( 1∶20; DSHB ) , rabbit anti-GFP ( 1∶10 , 000; Molecular Probes , Invitrogen , Delhi , India ) , rabbit anti-Dephrin [1∶1000; Kind gift from Andrea Brand , 82] and rabbit anti-Serotonin ( 1∶500; Sigma ) . Secondary antibodies used were Alexa 488 , Alexa-568 and Alexa-647 coupled antibodies generated in goat ( Molecular Probes; 1∶400 ) . Samples were mounted between coverslips with a spacer in 70% glycerol . Optical sections of 1 µm step size were analyzed using Olympus Fluoview version 1 . 4a , ImageJ ( http://rsb . info . nih . gov/ij/; Wayne Rasband , NIH , USA ) and Adobe Photoshop 7 . 0 ( Adobe Systems , San Jose , CA , USA ) . Ephrin-Fc fusion probe [kind gift from Alan Nighorn , 38] was used to visualize Eph receptor expression pattern in the developing antennal lobe . Protocol from Kaneko and Nighorn [83] was used for Drosophila pupal brains . Ephrin-Fc specifically recognizes Drosophila Eph [38] and Ephrin-Fc immunoreactivity is completely abolished in EphX652 mutant pupal brains ( Figure S1 ) . Glomeruli were identified using the standard 3-D map of the Drosophila antennal lobe [84] . Quantification of axonal terminal branch tip number was carried out in ImageJ ( http://rsb . info . nih . gov/ij/; Wayne Rasband , NIH , USA ) using the particle analysis and cell counter plugin . Branch tips of the CSDn in the individual glomerulus were marked manually using the plugin . Data was analyzed and represented as histogram using Microsoft Excel and graphpad instat . Statistical significance was determined using Mann-Whitney test and one-way repeated measure ANOVA test using Sigmaplot software . The assay was performed as previously described [34] . We tested courtship response of individually reared 5–6 day old virgin males of desired genotypes when introduced to age matched virgin CSBz females ( reared in vials with ∼10 females ) , which were applied 0 . 2 µl of cVA ( Pherobank , Netherlands ) ( diluted in Acetone ) or only Acetone ( as control ) . Concentration of cVA used was 1∶100 unless mentioned in particular experiment . The courtship response was recorded by videotaping ( Sony Handycam DCR DVD910E & Sony DSC H9 ) in a chamber ( Diameter = 1 . 5 cm; Height = 5 mm ) for 10 mins , from which the courtship index was calculated manually as described previously [34] . CO2 response index of 4–5 day old flies were measured using an upright Y-Maze apparatus as described elsewhere [67] . CO2was drawn through one arm of the maze , and control air was drawn through the other arm . Flies starved overnight were allowed into the entry tube , and their preference for the arm with the CO2 ( O ) vs . the control arm with air ( C ) was quantified as a response index [RI; the difference in the number of flies in the CO2 and control arms as a fraction of the total flies RI = ( O−C ) / ( O+C ) ] . Behavioural analysis was done in a double-blind manner .
|
Serotonin , a major neuromodulatory transmitter , regulates diverse behaviours . Serotonergic dysfunction is implicated in various neuropsychological disorders , such as anxiety and depression , as well as in neurodegenerative disorders . In the central nervous systems , across taxa , serotonergic neurons are often small in number but connect to and act upon multiple brain circuits through their wide-field arborization pattern . We set out to decipher mechanisms by which wide-field serotonergic neurons differentially innervate their target-field to modulate behavior in a context-dependent manner . We took advantage of the sophisticated antennal lobe circuitry , the primary olfactory centre in the adult fruitfly Drosophila melanogaster . Olfactory sensory neurons and projection neurons connect in a partner-specific manner to create glomerular units in the antennal lobe for processing the sense of smell . Our analysis at a single-cell resolution reveals that a wide-field serotonergic neuron connects to all the glomeruli in the antennal lobe but exhibits the glomerular-specific differences in its innervation pattern . Our key finding is that Eph from sensory neurons regulates the glomerular-specific innervation pattern of the central serotonergic neuron , which in turn is essential for modulation of odor-guided behaviours in an odor-specific manner .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"developmental",
"biology",
"developmental",
"neuroscience",
"cellular",
"neuroscience",
"behavioral",
"neuroscience",
"neuronal",
"morphology",
"genetics",
"biology",
"neuroscience",
"neural",
"circuit",
"formation"
] |
2013
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Sensory Neuron-Derived Eph Regulates Glomerular Arbors and Modulatory Function of a Central Serotonergic Neuron
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Transcriptional regulation in human cells is a complex process involving a multitude of regulatory elements encoded by the genome . Recent studies have shown that distinct chromatin signatures mark a variety of functional genomic elements and that subtle variations of these signatures mark elements with different functions . To identify novel chromatin signatures in the human genome , we apply a de novo pattern-finding algorithm to genome-wide maps of histone modifications . We recover previously known chromatin signatures associated with promoters and enhancers . We also observe several chromatin signatures with strong enrichment of H3K36me3 marking exons . Closer examination reveals that H3K36me3 is found on well-positioned nucleosomes at exon 5′ ends , and that this modification is a global mark of exon expression that also correlates with alternative splicing . Additionally , we observe strong enrichment of H2BK5me1 and H4K20me1 at highly expressed exons near the 5′ end , in contrast to the opposite distribution of H3K36me3-marked exons . Finally , we also recover frequently occurring chromatin signatures displaying enrichment of repressive histone modifications . These signatures mark distinct repeat sequences and are associated with distinct modes of gene repression . Together , these results highlight the rich information embedded in the human epigenome and underscore its value in studying gene regulation .
The genome sequence is a static entity defining the possible transcriptional output of every cell type in the human body [1] . By contrast , chromatin structure dynamically influences the transcriptional potential of each genomic loci in a particular cell . Over 100 different histone modifications are known to exist , and a single nucleosome can contain many modifications [2] . However , while the number of possible combinations of histone modifications far exceeds the number of nucleosomes in the human body , to date only a small number of histone modification patterns have been discovered [2] . Several classes of regulatory elements are marked by different chromatin signatures [3]–[5] . Notably , Heintzman et al recently observed distinct and predictive chromatin signatures at active promoters and enhancers [6] , [7] . Numerous studies have also observed that slight variations in chromatin signatures can distinguish different states of the same regulatory element [3] , [5] . For example , active promoters are generally marked by H3K4me3 , repressed promoters by H3K27me3 , and poised promoters by both marks [3] . Similarly , different chromatin signatures mark enhancers bound by different classes of transcription factors and co-activators [5] . In more recent studies , several chromatin signatures were also found at promoters [4] , enhancers [4] , and even exons [8]–[11] using genome-wide chromatin maps . These observations prompted us to systematically examine the chromatin signatures that exist in known and putative regulatory elements in the human genome . Our goal is to explore whether other frequently occurring chromatin signatures exist , and whether specific functions are associated with these signatures . Focusing on 21 histone modifications mapped in CD4+ T cells [12] , we find only a handful of distinct chromatin signatures at promoters , and that they correlate with gene expression . We then examine signatures spanning almost 50 , 000 regions in the human genome that are distal to previously annotated regulatory sites . We recover 7 distinct chromatin signatures , some containing enrichment of H3K36me3 that has been recently linked to marking exons [8] . Upon further inspection , we observe that H3K36me3 is most strongly enriched at a well-positioned nucleosomes located at the 5′ ends of exons . We also find that stronger enrichment of H3K36me3 correlates with increased exon usage in alternatively spliced genes . Finally , we recover two distinct chromatin signatures rich in repressive histone modifications marking distinct regions of the genome , that are associated with different modes of gene repression .
We hypothesize that loci sharing common regulatory functions may share similar chromatin signatures . To systematically identify chromatin signatures genome-wide , we examine different classes of regulatory loci in turn . These loci may contain chromatin signatures , but they may not be aligned or even oriented in the same direction . We therefore apply an unbiased clustering and alignment method called ChromaSig [5] ( see Methods ) to find over-represented chromatin modification patterns spanning these loci while simultaneously aligning and orienting their enrichment profiles , focusing on histone modification maps profiled recently in CD4+ T cells [12] . As a proof of principle that this approach yields biologically significant results , we first studied promoters . While chromatin signatures at promoters have been studied extensively , we still do not have a complete picture of all the distinct , commonly occurring chromatin signatures spanning all promoters . As such , our understanding of how different signatures relate to gene expression is incomplete . To address this , we apply ChromaSig to the chromatin modifications near the set of manually annotated promoters defined in the Refseq database [13] . We recover 14 clusters spanning 18 , 533 promoters ( Fig . 1 , Table 1 , Table S1 ) . Promoters in the same cluster share a common chromatin signature , and the chromatin signatures of different clusters are distinct in apparent or subtle ways . For example , the P4 cluster contains strong enrichment for various H3K4 methylations while P2 lacks these modifications . On the other hand , P9 and P12 clusters contain enrichment for the same chromatin modifications , but the pattern of enrichment is different , with P12 containing enrichment over a noticeably wider region . It is also evident that there is a high level of redundancy of histone modifications at promoters . Notably , H2AZ , H3K4me1 , H3K4me2 , H3K4me3 , and H3K9me1 are either all found together or all absent together at promoters , consistent with recent findings [4] . Previous studies have shown that there are at least three different classes of chromatin signatures at promoters: actively transcribed promoters marked by H3K4me3 but not H3K27me3 , inactive promoters marked with H3K27me3 but not H3K4me3 , and bivalent promoters having both these marks [3] . ChromaSig recovers all three of these previously known chromatin signatures: P8–14 have the active chromatin signature , P2 contains the repressed chromatin signature , and P4 has the bivalent signature . In agreement with a previous study , we observe that 1379 ( 7 . 4% ) promoters in human CD4+ T cells are bivalent , compared to similar numbers in the differentiated mouse embryonic fibroblasts ( 8 . 6% ) but lower than that found in undifferentiated mouse embryonic stem cells ( 15 . 2% ) . Next , we wondered if different signatures correspond to different gene expression activities . On the basis of gene expression [14] , we observe essentially three super-classes of promoters: P1–7 are generally inactive in CD4+ T cells , P9 , 11 , 13 , 14 show intermediate expression , and P8 , 10 , 12 are most highly expressed ( Fig . 1 ) . Promoters with repressed and bivalent chromatin signatures are generally expressed at low levels , while promoters with active chromatin signatures have intermediate to high levels of gene expression . Consistent with the high expression levels , P8 , P10 , and P12 also display the most enrichment of the elongation chromatin mark H3K36me3 ( Fig . 1 ) [12] , [15] . Interestingly , we observe chromatin signatures of varying widths of H3K4me3 immediately surrounding transcription start sites . We find that clusters with larger H3K4me3 widths tend to correspond to higher gene expression . For example , by visual inspection the average width in P12 is larger than P10 , which is in turn larger than P8 , and which is larger than P9 . Strikingly , median gene expression levels also decrease in the same order . CpG islands often mark the promoters of house-keeping genes that are ubiquitously expressed [16] , [17] . Strikingly , we observe that each distinct chromatin signature contains promoters that are either significantly enriched or depleted of CpG islands ( Table 1 ) . Nine of the 14 recovered clusters , containing 66% of all promoters , are significantly enriched in CpG islands ( hypergeometric p-value of 1E-3 ) . The majority of these CpG-enriched promoters ( 75% ) belong to clusters P8 , P9 , and P12 containing the strongest enrichment of H3K4me3 . As expected from the high CpG content , these promoters are also significantly enriched in Gene Ontology ( GO ) [18] , [19] terms relating to ubiquitous processes such as metabolism and the cell cycle . Another 11% of the CpG-rich promoters are in cluster P4 containing bivalent promoters marked by H3K4me3 and H3K27me3 . Consistent with previous studies [3] , [20] , these promoters are enriched in GO terms relating to human development . In contrast , clusters P2 , 5 , 6 , 7 , 11 spanning 34% of all promoters are significantly depleted of CpG islands . Nearly half of these promoters are marked by H3K27me3 but not H3K4me3 in cluster P2 . Consistent with previous studies suggesting these promoters are inactive [3] , [20] , many of these associated genes are enriched in GO terms relating to development and neurological processes , which are unrelated to T-cell function . Interestingly , P2 and P4 both mark repressed genes involved in development , but with distinct sequence context and chromatin signatures . P5 and P6 are the most CpG depleted clusters , and are not enriched in any histone modifications studied here . The corresponding genes are lowly expressed , and are enriched in GO terms unrelated to T-cells such as secretion and sensory perception [19] . Finally , P11 is the only CpG-poor cluster enriched with activating chromatin marks . Consistent with the notion that the corresponding genes are likely involved in cell-type specific processes [20] , these genes are generally more highly expressed than other CpG poor promoters , and include T-cell specific genes such as cathepsin W , which regulates T-cell cytolytic activity , the T-cell specific protease granzyme A , as well as several lymphocyte antigens including LY86 , CD68 , and CD79A . Together , these results show that ChromaSig can reliably detect distinct chromatin signatures at promoters with unique functional specificities . While transcriptional regulation occurs at the level of promoters , it is also clear that the action of promoter-distal regulatory elements is essential to controlling gene expression [1] . Like promoters , the activity of these regulatory elements is likely dependent on chromatin structure . To determine what chromatin signatures exist at distal regulatory elements , we apply ChromaSig to several classes of regulatory elements in turn: enhancers , insulators , Refseq 3′ ends , and DNase I hypersensitive sites . In eukaryotes , control of gene expression is a complex process involving the coordinated action of a wide assortment of genomic regulatory elements . Of the five classes of genomic regulatory elements examined here , the ones least studied and perhaps most important to controlling gene expression are enhancers and DNase I hypersensitive sites . To examine the potential regulatory roles of these genomic loci , we measure the enrichment of these loci near different classes of expressed genes as defined by the 14 clusters of promoter chromatin signatures ( Fig . 1 ) . When a CTCF-bound insulator falls between a promoter and enhancer , the enhancer is blocked from activating the promoter [26] . As this mechanism may also apply to regulatory elements outside of enhancers , we partition the genome into CTCF-defined blocks and determine enrichment of chromatin signatures having promoters in the same CTCF-defined block ( Fig . S8 ) . At a large scale , we observe that inactive promoters P1–6 generally lack enrichment for all the chromatin signatures cataloged here . In contrast , CTCF-defined domains containing active promoters P8–14 are enriched in numerous chromatin signatures . Strikingly , different classes of promoters are enriched in different classes of enhancers . For example , the two most highly expressed clusters P10 and P12 are uniquely enriched in E6–11 . These enhancers are distinguished from other enhancer classes by strong enrichment of H3K9me1 and H4K20me1 , indicating that these chromatin marks may be an indicator of enhancer activity . Of these enhancers , the class that most distinguishes highly active promoters from all other promoters is E9 . This cluster may contain the most active enhancers , and its chromatin signature may be a general mark for highly active enhancers . In general , we observe weaker enrichment of the DNase I hypersensitive clusters within CTCF-defined blocks containing highly expressed promoters , with the exception of D6–8 which are likely enriched in novel promoters and enhancers missed by the enhancer prediction method . Having observed chromatin signatures at regulatory elements including promoters and enhancers , we next ask if other chromatin signatures exist that mark loci distal to known regulatory elements . By definition , places in the genome with chromatin signatures contain enrichment of histone modifications . Therefore , we identify 85 , 318 loci with strong ChIP enrichment of histone modifications , of which 50 , 183 are distal to promoters [13] , gene 3′ ends [13] , DNase I hypersensitive sites [23] , CTCF binding sites [12] , and sites containing an enhancer chromatin signature [6] , [7] . Applying ChromaSig to these sites , we recover 7 frequently-occurring chromatin signatures , named U1–7 ( for unannotated clusters 1 to 7 ) , spanning 47 , 874 loci ( Fig . 2 , Table 2 , Table S6 ) . The recovered signatures are distinct from the previously defined H3K4me3-rich promoter and H3K4me1-rich enhancer signatures [3] , [6] . Compared to chromatin signatures from randomly aligned and oriented loci , the chromatin signatures observed are significantly better aligned than expected by chance ( p-values ranging from 10−18 to <10−300 ) ( Table S7 ) . The most prominent chromatin feature of these clusters is H3K36me3 , known to mark the 3′ ends of genes [12] and more recently exons [8] , and it is enriched at U1 , U2 , and U4 clusters . The largest clusters recovered , U5 and U6 , both contain enrichment of known repressive chromatin modifications including H3K9me2 , H3K9me3 , H3K27me2 , and H3K27me3 [12] . To gain an understanding of potential functions associated with the above frequently-occurring novel chromatin signatures , we compare the loci bearing each signature to genomic annotations . H3K36me3 is known to be enriched within the body of transcriptionally active genes [30] , [31] , notably towards the 3′ ends [12] . But since all the clustered loci are distal to gene 3′ ends , the H3K36me3-rich clusters must be marking another genomic feature . Noticing that the vast majority of loci in U1–4 are intragenic ( Fig . S9 ) , we ask if these sites are biased towards exons or introns . We observe that 57 . 9% of U1 sites and 63 . 8% of U2 sites are either inside exons or within 1-kb of exon ends , while at random only 26% of the genic regions of the genome match these criteria . To see if H3K36me3 marks exons , we examine the enrichment of this chromatin mark at exons ( Fig . S1 ) . To examine only those exons unambiguously marked by a chromatin signature , we only consider an exon if it is the only exon within 1-kb of a cluster locus . We observe a striking enrichment of H3K36me3 at the 5′ ends of exons unambiguously marked by U1 , U2 , and U4 . This enrichment decreases sharply upstream of the 5′ end , but more gradually into the exon body . This observation also holds for exons larger than 1-kb ( Fig . S2 ) , indicating that the result is not biased by the relatively small exon sizes in the human genome [32] . These results suggest that the clusters with strong H3K36me3 enrichment mark exon 5′ ends . Having observed H3K36me3 at a handful of exons , we next ask if this chromatin mark is a global indicator of exon expression . First , we examine the enrichment of clusters U1–4 within the gene bodies belonging to the promoters in clusters P1–14 . Indeed , we find that clusters U1–4 are enriched within the gene bodies of highly expressed genes belonging to promoter classes P8–P14 , but are depleted in the gene bodies of inactive promoters in other classes ( Fig . S8 ) . Next , profiling H3K36me3 at a catalog of more than 250 , 000 distinct exons [33] , we observe that the majority of exons ( 72 . 6% ) have more than two-fold enrichment for H3K36me3 tags than neighboring introns ( Fig . 3A ) . In the direction of transcription , H3K36me3 enrichment increases sharply at the 5′ end of the exon , and decreases more gradually in the body of the exon , in agreement with our previous observations . In contrast , neighboring introns show no such chromatin signature ( Fig . 3 , S10 ) . The presence of this chromatin mark also correlates strongly with exonic expression ( Fig . 3 ) , as measured previously by exon expression arrays in CD4+ T cells [34]: highly expressed exons having more H3K36me3 enrichment than lowly or moderately expressed exons . Altogether , these results suggest that H3K36me3 is a general mark of exon expression . Recently , it has also been observed that H3K36me3 marks exons in various eukaryotes , though the modification was found to be biased toward the 3′ ends of exons [8] . To resolve this discrepancy , we take advantage of a unique feature of ChIP-Seq technology , which sequences short directional reads directly upstream and downstream of the genomic DNA bound by the protein of interest , allowing clear distinction between sense and anti-sense reads . This information can be used to offer unprecedented resolution of in vivo binding locations of the immunoprecipitated protein [27] , [35] . We can also use this information to more finely resolve nucleosome structure at exons . Examining the distribution of H3K36me3 tags near the top 50% expressed human exons , we observe that reads on the sense strand peak at the 5′ ends of exons , whereas reads on the anti-sense strand peak about 150 base pairs downstream ( Fig . 3B ) . These results suggest that a well-positioned nucleosome modified by H3K36me3 exists at the 5′ ends of expressed exons , and consistent with this conclusion the spacing between sense and anti-sense peaks is roughly the size of a nucleosome . In addition to exon 5′ ends , it also appears that the 3′ ends of expressed exons have well-positioned nucleosomes ( Fig . 3C ) . But given that a typical nucleosome wraps between 145 and 147 bp of DNA [36] , which is roughly the same size as the average human exon at 145 bp [32] , it is difficult to conclude from these observations whether the nucleosomes harboring H3K36me3 are more fixed towards exon 5′ or 3′ ends . To resolve this issue , we re-examine the distribution of H3K36me3 reads , but focus on expressed exons larger than 500 bp ( Fig . 3D–E ) . Again , we observe sense and anti-sense peaks at exon 5′ ends indicative of well-positioned modified nucleosomes , followed by a decrease of H3K36me3 enrichment on both strands in the direction of transcription . However , we also find similar but weaker peaks on both strands at exon 3′ ends , with the sense strand peaking about a nucleosomal distance upstream of the anti-sense strand ( Fig . 3E ) . Thus , we conclude that the nucleosomes harboring H3K36me3 are found at both 5′ and 3′ ends of exons , but the enrichment is stronger at the 5′ ends . To test this conclusion more globally over a larger collection of exons , we also examine the enrichment of H3K36me3 along the exon body as a function of exon length . Indeed , as exon length increases , we observe enrichment of H3K36me3 at 5′ and weaker enrichment at 3′ exon ends , separated by the exon body lacking enrichment ( Fig . S11 ) . As H3K36me3 at the 5′ ends of exons is a global mark of exon expression , we next wondered if the presence of this mark correlates with alternative splicing . A previous study found that the density of H3K36me3 at canonical exons is higher than that at alternative exons in mice [8] . As this observation did not incorporate expression information but instead relied on static exon definitions , the question of whether the presence of H3K36me3 correlates with exonic splicing in humans remains unanswered . To answer this question , we investigate alternative splicing on a global scale by focusing on a list of 13 , 434 exons known to be alternatively spliced as cassette exons ( UCSC Genome Browser “knownAlt” track ) [37] . We examine two sets of transcripts using exonic expression information . The “spliced in” set consists of cassette exons expressed at levels similar to neighboring upstream and downstream exons ( |Δexpr| = 0 . 5 ) , and thus are likely to be included in a mature transcript . In contrast , the “spliced out” set consists of cassette exons expressed at lower levels than both upstream and downstream exons , and are likely excluded from the mature transcript ( exprup , down−expralt>1 ) . For spliced in exons , we observe that the enrichment of H3K36me3 increases gradually from upstream to alternatively spliced to downstream exons ( Fig . 4A ) , consistent with previous results showing a 3′ bias in this chromatin mark [12] . However , H3K36me3 is noticeably depleted at spliced out exons as compared to both upstream and downstream exons ( Fig . 4B ) . These results suggest that , on a global scale , the presence of H3K36me3 at alternatively spliced exons correlates with inclusion of the exon in transcripts . In agreement with these observations , we find that exons marked by U1 or U2 are preferentially included in mature mRNAs ( pU1 = 1 . 65E–26 , pU2 = 5 . 94E–43 , Wilcoxon rank sum test ) ( Fig . S3 ) . U3 , which contains no H3K36me3 enrichment ( Fig . 2 , S1 ) , is a negative control containing no preference of exon inclusion . Interestingly , exons marked by U4 , which are enriched in the repressive H3K9me3 modification , are preferentially excluded from mature mRNAs ( pU4 = 6 . 67E–4 , Wilcoxon rank sum test ) . Taken together , these results suggest that several distinct chromatin signatures are found at exon 5′ ends , that some signatures mark exons for preferential inclusion , and others for preferential exclusion . These different functional specificities may be attributed to specific differences in chromatin signatures ( see Discussion ) . Our initial scan revealed several classes of chromatin signatures marking exons , the largest of which are U1 and U2 . Both of these contain enrichment for H3K36me3 , but U1 contains stronger enrichment for H2BK5me1 and H4K20me1 . This latter modification is known to be localized both at promoters and intragenic regions downstream of the promoters , with enrichment fading in the gene body [12] . These observations raise the possibility that exons marked by U1 are exons closer to promoters ( 5′ exons ) while U2 are exons closer to the 3′ ends of genes ( 3′ exons ) . To test this hypothesis , we partition the highly expressed exons above into first and non-first exons . Non-first exons are further subcategorized into early , middle , and late exons based on distance from the transcription start site ( TSS ) . We then examine the enrichment of histone modifications near these different classes of exons ( Fig . 5 ) . As expected , first and early exons , which are closest to TSSs , are all highly enriched in promoter modifications including H3K4me1 , H3K4me2 , and H3K4me3 . In addition to H3K36me3 , it is clear that there is also a general peak of H2BK5me1 and H4K20me1 enrichment at exons . This enrichment is most pronounced in 5′ exons compared to first , middle , and 3′ exons . In addition , we also observe that 5′ exons , while still marked by H3K36me3 , have weaker enrichment of this mark compared to mid or 3′ exons , but is clearly more enriched than the first exon . H3K36me3 enrichment increases with increasing distance from the TSS , consistent with above results ( Fig . 4A ) and previous observations [12] . These results provide additional evidence for various chromatin modifications marking distinct exons in the human genome . In addition to chromatin signatures U1–4 , ChromaSig also identifies two new chromatin signatures , U5–6 , having strong enrichment of repressive histone modifications ( Fig . 2 ) . Consistently , these signatures are not found near highly expressed genes but are enriched near repressed genes ( Fig . S8 ) . These two chromatin signatures are distinct , with U5 having stronger enrichment of repressive modifications H3K9me2 and H3K9me3 . This subtle difference prompted us to ask if these signatures mark distinct regions of the genome . Indeed , we find that only 23 . 3% of U5 loci are intragenic , a notable depletion over the expected value of about 40% ( Fig . S9 ) . In contrast , U6 loci are closer to the expected value at 36 . 3% intragenic . Additional analysis suggests that the sequences underlying U5 and U6 fragments are associated with distinct properties . First , we compare to the PhastCons database containing over 2 million conserved elements in the human genome conserved over 28 mammalian genomes [24] . We find that U5 loci are significantly depleted of conserved elements ( p = 7 . 12E–182 ) while U6 is significantly enriched ( p = 2 . 09E–26 ) ( Fig . 6A ) . Given that repressive histone modifications have been known to mark repetitive regions of the genome [38] which are highly lineage-specific [32] , the low conservation of U5 loci may be explained by enrichment for repetitive sequences . To test this hypothesis , we use RepeatMasker [39] to define repetitive bases within ±1-kb from each locus in U5–6 . Indeed , 49 . 1% of U5 bases are repetitive , as compared to 32 . 1% of U6 bases ( Fig . 6B ) , suggesting that these two clusters may harbor different classes of sequences . To pursue this further , we next ask if the classes of repeats found in U5 are different from those found in U6 . Counting the repetitive elements found within ±1-kb of each locus ( Fig . 6C , D ) , we find that U5 is significantly enriched for long terminal repeats ( LTR ) ( p<1E–300 , Z-score = 39 . 7 ) , while U6 is neither enriched nor depleted . For the SINE family of repeats , while both clusters are significantly depleted in Alu repeats ( pU5<1E–300 , ZU5 = 81 . 5; pU6 = 4 . 76E–245 , ZU6 = 33 . 4 ) , only U6 is notably enriched in MIR repeats ( p = 2 . 31E–177 ) . Similarly , L2 LINE repeats and simple repeats are notably more enriched in U6 loci than U5 loci . These results suggest that U5 and U6 have different genic distributions and mark distinct sequences of the genome . We next examine whether the different genic distributions and sequence preferences of U5 and U6 relate to gene expression . It is thought that the genome is organized into different domains of transcriptional activity , with the insulator binding protein CTCF defining the boundaries of these domains [26] . Therefore , we partition the genome into CTCF-defined domains and determine the enrichment of U5 or U6 loci in these domains as a function promoter activity . The distributions of U5 and U6 enrichment are significantly different ( p = 5 . 95E–26 , paired Wilcoxon signed rank test ) ( Fig . 7A ) : U5 is more enriched than U6 in domains containing the most repressed genes ( log expression <4 ) , while domains containing genes more expressed ( log expression between 5 and 6 ) have higher enrichment of U6 loci than U5 loci . For moderately and highly expressed genes ( log expression >6 ) , the enrichment of both U5 and U6 loci are depleted relative to random . We next investigate the localization of U5 and U6 with respect to the distinct promoter classes P1–14 . We find that U5–6 are in general depleted near moderately and highly expressed promoters P8–14 . In contrast , U5 and U6 are enriched near distinct classes of repressed genes . U6 is enriched in CTCF blocks containing P1 and P3 compared to U5 ( Fig . S8 ) . In contrast , U5 is enriched near promoters in cluster P6 , which are depleted of U6 elements ( Fig . S8 ) . These results further underscore the notion that these elements repress the genome in distinct ways . While it is not surprising that U5 and U6 are enriched near genes with low expression since they are both enriched in repressive histone modifications , it is remarkable that these two chromatin signatures mark distinctly different populations of lowly expressed genes . One possibility is that U5 and U6 are present in different compartments of the nucleus . To test this , we examine the localization of these loci in lamina-associated domains ( LADs ) , previously mapped in fibroblast cells and known to contain repressed genes and gene deserts . Indeed , more than 60% of U5 loci are in LADs ( penrichment<1E–300 ) , compared to only 37 . 4% for U6 loci ( pdepletion = 1 . 57E–10 ) ( Fig . 7B ) . Taken together , these results suggest that U5 and U6 mark distinct domains of gene expression that may be explained by their enrichment in different nuclear compartments .
In this study , we survey the global landscape of commonly occurring chromatin signatures in the human genome . We recover known signatures at well-studied elements such as promoters and lesser-studied elements including enhancers . In addition , we find 7 distinct signatures spanning 47 , 874 genomic loci distal to known regulatory elements . We observe chromatin signatures marking exons and show at a higher resolution that the 5′ ends of exons are specifically modified by H3K36me3 . Furthermore , we show that the enrichment level of this mark directly correlates with exonic expression , a result that had only been implied before . In addition , we recover two distinct chromatin modifications U1 and U2 marking exons in our genome-wide scan . While both are enriched in H3K36me3 , U1 is uniquely enriched in H2BK5me1 and H4K20me1 , which directly coincides with U1 marking early exons and U2 marking late exons . A previous study by Kolasinska-Zwierz et al also observed that H3K36me3 marks exons in C . elegans and in mammals [8] . Here , we find that this histone modification is specifically enriched at the 5′ ends of exons and also weakly enriched eat 3′ ends of exons . Our results , together with findings by Kolasinska-Zwierz et al , implicate chromatin modifications in regulating splicing , a process until recently thought to be decoupled from transcription both physically and temporally . In yeast , H3K36me3 is deposited by the histone methyltransferase Set2 , which is associated with the elongation form of RNA polymerase [40] , [41] . The observation that H3K36me3 marks exons , a part of gene structure in the realm of splicing rather than transcription , implies that H3K36me3 may directly or indirectly regulate splicing . A large body of work on splicing regulation has been focused on how sequence-specific proteins binding directly to pre-mRNAs affect splicing [42] , [43] . But the static and highly degenerate natures of sequence elements associated with splicing leave unanswered the question of how cell-type specific splicing is achieved . However , recent discoveries physically linking RNA polymerase to the splicing machinery has shifted attention to the roles of the transcription machinery in regulating splicing [42] , [44] . This has led to two models describing co-transcriptional splicing: a kinetic model and a recruitment model [42] . While both models emphasize spliceosome activity during transcription , neither fully explains how cell-type specific splicing is achieved . Our observations that distinct chromatin signatures are present at exons , and that different signatures are associated with either inclusion or exclusion from mature mRNAs , suggest a role of chromatin state in splicing regulation . One possibility is that the writing and reading of dynamic chromatin signatures may direct splicing events . While this model is attractive , further studies will be necessary to verify this hypothesis . Identifying alternatively spliced exons de novo using chromatin signatures is an exciting possibility . A recent study has used the enrichment of H3K4me3 in conjunction with proximal enrichment of H3K36me3 to identify novel long non-coding RNAs [45] , though H3K36me3 enrichment was used more as an indicator of elongation than of exon boundaries . But even if chromatin signatures can be used to detect alternative exons , because exons are transcribed it would be as cheaper , more efficient , and more reliable to employ techniques such as RNA-Seq to completely enumerate alternative exons de novo [46] . In the future as we approach completely mapping all histone modifications of the epigenome , one interesting possibility is that , like promoters and enhancers [3] , [7] , an exon chromatin signature marking poised but inactive exons may also exist . This could allow for identification of alternative exons needed for cellular response to stimuli . We also recover several chromatin signatures enriched in repressive histone modifications marking distinct populations of repetitive elements . Surprisingly , these signatures are associated with different modes of gene repression . One possible explanation for this phenomenon is that U5 loci , which contain H3K9me2 and H3K9me3 , are more highly enriched in nuclear lamina-associated domains than U6 loci . Thus the U5 chromatin signature may be specifically associated with LADs , while U6 is with other types of domains . It is possible that these two different types of chromatin domains correlate with distinct mechanisms of gene silencing , with H3K9-associated U5 domains being more permanently repressed than H3K9-free U6 domains . These results show that studying the human genome on the basis of chromatin signatures is a useful method to cataloging regulatory elements in the genome in a global , unbiased , and systematic way . Future efforts to map chromatin modifications in the human genome may allow us to define more chromatin signatures marking novel regulatory elements or different functional specificities of known regulatory elements .
Data normalization . Genome-wide distributions of histone modifications were obtained from Barski et al [12] . As in Hon et al [5] , we filtered reads for uniqueness and redundancy , partitioned the genome into 100-bp bins , and counted reads in each bin . As the number of reads for each mark was highly variable , normalization was necessary to facilitate comparison . For each bin i and mark h , we normalized the number of reads in this bin xh , i as in [5]: Genome annotations . Genome annotations were downloaded from the UCSC Genome Browser [37] , human genome Build 36 . 1 ( hg18 assembly ) . Gene definitions were given by the Refseq Genes [13] track . CpG island definitions were given by the “CpG Islands” track . Alternatively spliced exons were defined by entries in the “Alt Events” track labeled as “Cassette Exons” . The list of human loci conserved in a 28-way alignment with placental mammals was defined by the phastConsElements28wayPlacMammal table[24] . Repeat definitions were given by the RepeatMasker track [39] , and lamina-associated domains mapped in Tig3 human lung fibroblasts [47] were defined by the “NKI LADs” track . Catalogs of regulatory elements . Using previously published CTCF ChIP-Seq data [12] , we obtained a list of 27 , 110 CTCF sites by running the Model-based Analysis of ChIP-Seq [27] software with default parameters and a p-value cutoff of 1E–5 . We used normalized H3K4me1 and H3K4me3 profiles ( as above ) to predict enhancers as in Heintzman et al [6] . ROC analysis indicated that using a p-value cutoff of 0 . 1 gives optimal recovery ( in terms of sensitivity and positive predictive value ) of DNase I hypersensitive sites [23] , corresponding to 32 , 237 predicted enhancers at least 2 . 5-kb from Refseq TSSs . Finding ChIP-enriched loci distal to known regulatory elements . As in Hon et al [5] , we identified regions of width 2-kb containing enrichment for histone modifications . We modeled the background distribution using 1% of the human genome as defined by the ENCODE regions and defined enriched regions as those significantly deviating ( p = 0 . 0001 ) from the background . To remove redundancy , we removed any enriched locus closer than 2 . 5 kb to another enriched locus . We then removed loci within 2 . 5 kb to regulatory loci at promoters [13] , gene 3′ ends [13] , CTCF binding sites [12] , DNase I hypersensitive sites [23] , and sites having an enhancer chromatin signature [6] . Finding chromatin signatures . We searched for chromatin signatures of width 4-kb using ChromaSig [5] with a background prior p2A = 0 . 01 and a standard deviation factor σanother = 1 . 75 . For loci with well-defined loci ( gene 5′ ends , gene 3′ ends , CTCF binding sites , DNase I hypersensitive sites ) we searched within a region ±500-bp around the sites , but for less-defined loci ( predicted enhancers , ChIP-rich regions ) we relaxed the search to a ±1-kb region . To focus only on the most frequently-occurring chromatin signatures , we analyzed only those clusters output having at least 500 loci and an average normalized enrichment greater than 0 . 25 for at least one modification . Chromatin signature significance . For a given cluster of size N , we defined the motif mh , i to be the mean normalized enrichment of the aligned loci at a specified position i for modification h . Well-aligned motifs have higher values of enrichment . For each motif , we computed the score: Higher values of S indicate more significant motifs . To assess significance of observing a motif spanning N loci with score S or greater , we randomly sampled 100 sets of clusters with random alignment offsets ( within ±1 kb of the aligned sites ) and orientations ( positive or negative strand ) , computed S scores for each random set , and modeled the random distribution of S scores as a Guassian distribution to allow for calculation of significance . We performed this randomization either within loci in the same cluster as the original motif or over loci from all clusters . Heatmaps . All heatmaps consist of normalized data over 100-bp bins ( see above ) , and were visualized using Java TreeView [48] . Expression data . Transcript and exon expression data were measured in CD4+ T cells by Crawford et al [14] ( GEO accession GSE4406 ) and Oberdoerffer et al [34] ( GEO accession GSE11834 ) , respectively . Both studies performed duplicate measurements on microarrays , and the expression data shown here is the average of the replicates . Randomization . To determine enrichment for a given cluster , we compared to 100 random clusters . Each random cluster contains the same number of loci as the original cluster and follows the same chromosomal distribution . Random sampling is limited to bins containing ChIP-Seq reads . Statistical tests . To assess significance of overlap with known genome annotations , we assume that the overlap statistics for 100 random clusters follows a Gaussian distribution . To assess significance of exon inclusion for marked versus unmarked exons , we use a two-sided Wilcoxon rank sum test to compare the median exon expression of the two sets . To assess that U5 and U6 are enriched near different classes of expressed genes , we use the paired two-sided Wilcoxon signed rank test to compare the enrichment profiles .
|
Recent studies have observed that histone tails can be modified in a variety of ways . Analyzing a collection of 21 histone modifications , we attempted to determine what common signatures are associated with different classes of regulatory elements and whether they mark places of distinct function . Indeed , at promoters , we identified a number of distinct signatures , each associated with a different class of expressed and functional genes . We also observed several unexpected signatures marking exons that directly correlate with the expression of exons . Finally , we recovered many places marked by two distinct repressive modifications , and showed that they mark distinct populations of repetitive elements associated with distinct modes of gene repression . Together , these results highlight the rich information embedded in the human epigenome and underscore its value in studying gene regulation .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"genetics",
"and",
"genomics/functional",
"genomics",
"genetics",
"and",
"genomics/gene",
"expression",
"computational",
"biology/alternative",
"splicing",
"computational",
"biology/genomics",
"genetics",
"and",
"genomics/epigenetics",
"genetics",
"and",
"genomics/bioinformatics"
] |
2009
|
Discovery and Annotation of Functional Chromatin Signatures in the Human Genome
|
There is still no safe and effective vaccine against dengue virus infection . Epidemics of dengue virus infection are increasingly a threat to human health around the world . Antibodies generated in response to dengue infection have been shown to impact disease development and effectiveness of dengue vaccine . In this study , we investigated monoclonal antibody responses to an experimental dengue vaccine in rhesus macaques . Variable regions of both heavy chain ( VH ) and light chain ( VL ) were cloned from single antibody-secreting B cells . A total of 780 monoclonal antibodies ( mAbs ) composed of paired VH and VL were characterized . Results show that the vaccination induces mAbs with diverse germline sequences and a wide range of binding affinities . Six potent neutralizing mAbs were identified among 130 dengue envelope protein binders . Critical amino acids for each neutralizing antibody binding to the dengue envelope protein were identified by alanine scanning of mutant libraries . Diverse epitopes were identified , including epitopes on the lateral ridge of DIII , the I-III hinge , the bc loop adjacent to the fusion loop of DII , and the β-strands and loops of DI . Significantly , one of the neutralizing mAbs has a previously unknown epitope in DII at the interface of the envelope and membrane protein and is capable of neutralizing all four dengue serotypes . Taken together , the results of this study not only provide preclinical validation for the tested experimental vaccine , but also shed light on a potential application of the rhesus macaque model for better dengue vaccine evaluation and design of vaccines and immunization strategies .
Dengue virus ( DENV ) , a mosquito-borne flavivirus , causes an estimated 390 million infections annually and presents a formidable burden on the global healthcare system [1] . DENV infection can result in common dengue fever ( DF ) and more severe illnesses such as dengue hemorrhagic fever ( DHF ) and dengue shock syndrome ( DSS ) . DHF and DSS are characterized by increased vascular permeability , hemorrhagic manifestations , and plasma leakage-induced shock that can lead to death . Approximately 40% of the world’s population lives in dengue-endemic regions , so the spread of DENV infection and threat of severe disease remain a significant challenge to global human health [2 , 3] . DENV has four serologically distinct subtypes , DENV1 , DENV2 , DENV3 , and DENV4 , each with an RNA genome of 10 . 7 kb that is translated into three structural proteins , Capsid ( C ) , Envelope ( E ) , preMembrane ( prM ) , and seven nonstructural proteins ( NS1 , NS2A , NS2B , NS3 , NS4A , NS4B and NS5 ) [4] . Dengue virus exhibits substantial genetic diversity , with approximately 30%-40% amino acid sequence divergence between serotypes [4] . Immunity induced by primary infection of DENV is effective in protection against subsequent infection by the homologous viral serotype . However , when secondary infection by a different serotype occurs , pre-existing immunity to one serotype of DENV can be an adverse factor causing life-threatening illness such as DHF or DSS [5–7] . Thus , it is critical for a DENV vaccine to induce a balanced immune response or elicit broadly neutralizing antibodies against all four serotypes [8 , 9] . The licensed dengue vaccine , Dengvaxia , has limited efficacy in protecting against DENV infection . This efficacy depends on the serotype of the infecting virus and the immune status at the time of vaccination [10 , 11] . Unexpectedly , the vaccine has lower efficiency in subjects who were DENV seronegative than in subjects who were DENV seropositive prior to vaccination [12 , 13] . In Phase 3 trials , the vaccine showed 35–80% protection against infection by all DENV serotypes . However , this efficacy is not consistent with the anti-DENV titer as assessed in the clinical trial . In this trial , over 90% of the subjects showed serum neutralization activity against DENV [14 , 15] . This result indicates that titer is not sufficient for dengue vaccine evaluation . Because of the complexity of viral antigens involved and the polyclonal nature of immune sera , other factors may influence vaccine efficacy . An alternative approach to estimate the efficacy of a dengue vaccine in clinical trials is through a better understanding of the monoclonal antibodies ( mAbs ) induced by the vaccine . Antibody responses can be measured accurately , in both qualitative and quantitative terms , and the epitopes can be mapped for potent neutralizing antibodies . Therefore , vaccine efficacy could be evaluated by serum titer , monoclonal antibody response , and epitopes of neutralizing antibodies . Non-human primates ( NHP ) such as rhesus macaques can develop viremia upon DENV infection and have a qualitative immune response similar to that in humans [16] . Therefore , they are often used to evaluate dengue vaccine candidates in preclinical trials . Induction of a humoral immune response by vaccination has been shown to induce protective immunity against dengue infection [17 , 18] . We have developed a dengue subunit tetravalent vaccine comprising truncated E proteins , 80% of the N-terminal of the E protein from all four DENV serotypes ( named DEN-80E ) [19 , 20] . It was shown that a prime-boost strategy ( live attenuated virus prime , DEN-80E boost ) in rhesus macaque induced neutralization titers to all four DENV serotypes that were higher than those induced by a prime-boost regime using only tetravalent virus vaccine [21] . To better evaluate the efficacy of the vaccine , we developed a robust method to clone monoclonal antibodies from the antibody-secreting cells of rhesus macaque after vaccination . By characterizing these antibodies , we are able to identify indicators of vaccine efficacy [22] . The DENV E protein is the dominant antigenic site of DENV neutralizing antibody . It forms dimeric structures that anchor on the M protein through hydrophobic interaction on the mature virus particle [23 , 24] . Each E protein contains three domains: DI , DII , and DIII [25] . DI participates in the conformational changes of the virus particle [26 , 27] . DII interacts with the endosome to lead fusion of the virus after entry using the hydrophobic fusion loop [28] . DIII is involved in receptor binding [29 , 30] . The prM protein forms a heterodimer with E protein to prevent premature fusion [31] . During virus assembly in the endoplasmic reticulum ( ER ) , each set of three prM-E heterodimers form “spikes” on the virion surface . The virus is then transported through the secretory pathway . In the trans-Golgi , the low-pH environment induces the rearrangement of prM-E heterodimers into a flattened conformation . Then the prM protein on immature virions is cleaved by furin or furin-like protease to produce mature virions [29] . However , the cleavage process is incomplete , leading to a mixture of virus particles in different states of maturity [32] . The diversity of virus structures influences epitope accessibility and thereby affects the neutralizing activity of the antibodies targeting different epitopes [33] . Comprehensive studies have identified both serotype-specific and serotype-cross-reactive epitopes in all three domains of the E protein , recognized by various neutralizing mAbs [34 , 35] . The mouse antibodies to DIII exhibit potent neutralizing activity . Their neutralization capacity and specificity vary between DENV serotypes . [36–38] . The lateral ridge ( loops between the β-sheets ) of DIII , fully accessible on the intact virion , is an epitope hotspot for mouse neutralizing antibodies . It is also a neutralizing site for other flaviviruses , such as Zika and West Nile virus [39 , 40] . Besides the lateral ridge , mouse antibody targeting the A strand of DIII , has demonstrated strong cross-serotype neutralizing activity , drawing attention to potential immunotherapy applications . DIII is also the antigenic region of the human antibody response . The antibodies isolated from human patients have been shown to bind the lateral ridge and the stands of DIII [41] . However , the proportion of DIII-targeting human neutralizing antibodies is low in dengue-positive human sera [42] . Humans tend to produce antibodies that bind to epitopes in domains other than DIII [43] . In general , the fusion loop of DII represents a dominant epitope , and the antibodies specific for this loop are weak and tend to have cross-reactive neutralizing activity against different flavivirus infections [44 , 45] . On the other hand , the bc loop adjacent to the fusion loop in DII is an epitope of a highly potent human antibody neutralizing all four DENV serotypes [46] . It has also been reported that mouse antibodies can block virus membrane fusion by targeting the fusion loop and bc loop simultaneously [47 , 48] . These examples highlight the importance of the loop adjacent to the fusion loop in inhibiting DENV infection . As for the DI-binding mAbs , the lateral ridge of DI and the hinge region are the epitopes targeted by neutralizing antibodies from mouse , chimpanzee , and human [49 , 50] . Generally , the DI antibodies neutralize the virus by blocking the conformational change of the E protein during membrane fusion [50] . Another group of antibodies targets the quaternary epitope whose critical residues lie at the domain interface or across different domains . Recently , a new class of potent neutralizing human mAbs was reported , only targeting the E-dimer on the intact virus surface [51 , 52] . These antibodies are E-dimer dependent , locking the prefusion structure of E protein , preventing the conformational change necessary for virus fusion . They are the dominant neutralizing antibodies in human patients . However , a comprehensive understanding of the DENV epitopes targeted by rhesus macaque antibodies has not been described . In this study , we investigated antibody responses to an experimental dengue vaccine in rhesus macaques . We cloned and characterized a large panel of mAbs ( 780 antibodies ) from single antibody-secreting B cells ( ASCs ) using a high-throughput cloning protocol that we reported previously [22] . Six potent neutralizing antibodies were identified . Their binding epitopes were mapped using an alanine-scan mutation library across dengue prM/E [53] . We isolated antibodies with binding epitopes similar to those previously reported [36 , 46 , 50 , 54] . This collection of antibodies exhibited a diverse epitope pattern , with binding sites on the lateral ridge of DIII ( d182 , d511 , and d628 ) , the I-III hinge ( d182 ) , the bc loop adjacent to the fusion loop in DII ( d559 ) , and the β-strands and loops of DI ( d462 ) . More significantly , we identified a broadly neutralizing antibody ( d448 ) recognizing an epitope never previously reported in DII , at the interface of the E and M proteins . Discovery of these antibodies with diverse epitopes will aid the design of more effective vaccines and therapeutic antibodies against dengue infection .
The experimental vaccine of this study uses live attenuated viruses ( LAV ) for priming and tetravalent recombinant E proteins ( DEN-80E ) for boosting [21 , 22] . The immunization schedules and workflow for antibody analysis are outlined in S1 Fig . Briefly , a total of 12 Rhesus macaques received a primer injection of the LAV subcutaneously ( S . C . ) at week 0 , followed by a boost of tetravalent DEN-80E intramuscularly ( I . M . ) at week 16 ( month 4 ) . This vaccine has shown to elicit robust DENV neutralizing antibody titers in rhesus macaques ( Fig 1A ) [19 , 55] . To analyze the antibody response to the vaccine , seven days after the booster dose , we isolated monoclonal antibodies from single ASCs by fluorescence-activated cell sorting ( FACS ) from peripheral blood mononuclear cells ( PBMCs ) of 12 vaccinated rhesus macaques . We pooled the PBMCs together for the ASCs sorting , and cloned a total of 780 antibodies from more than a thousand ASCs collected using a method that we reported previously [22] . The single ASCs were isolated by FACS from PBMCs into 96-well plates ( one cell/well ) . The natively paired heavy and light variable regions from single B cells were cloned using a direct cDNA synthesis and nested-PCR amplification strategy . All paired antibodies were cloned into a framework of human IgG1 and expressed as macaque-human chimeric antibodies in HEK293-F cells for purification and downstream analysis . The initial screening of E protein binders was performed with supernatants of antibody expression cultures to eliminate non-binding antibodies . Positive binding antibodies ( mAbs ) were defined as those with an OD450 ≥ 0 . 1 as assessed by ELISA in the primary screen . Among the cloned 780 mAbs , 130 ( 17% ) of the supernatant antibodies were DEN-80E binders: 106 ( 14% ) mAbs with OD450 in the range of 0 . 1 to 0 . 5 were defined as weak binders; 22 ( 3% ) antibodies with OD450 ≥ 0 . 5 were considered to be strong binders ( Fig 1B ) . Among the 130 binding mAbs , there were different types of cross-recognition of the four serotypes of DEN-80E: single serotype , two or three serotypes , and all four serotypes ( Fig 1C ) . About 50% of the DENV binding mAbs were specific to only one serotype of dengue: 30 mAbs were specific to DEN1-80E ( Fig 1D ) ; 18 mAbs to DEN2-80E ( Fig 1E ) ; 8 mAbs to DEN3-80E ( Fig 1F ) ; and 9 mAbs to DEN4-80E ( Fig 1G ) . A total of 33 mAbs showed binding to DEN-80E proteins from two or three dengue serotypes ( Fig 1H ) . More significantly , 32 cloned mAbs had cross-reactivity to all four dengue serotypes ( Fig 1I ) . In summary , diverse antibodies recognizing complex epitopes formed the macaque antibody response to the vaccination . Positive binding mAbs were purified for further evaluation , most of the cloned antibodies showed low-affinity binding . Eighteen cloned antibodies showed a half-maximal binding concentration ( EC50 ) in the sub-microgram to picogram range for envelope proteins from one or more DENV serotypes ( Table 1 , S2 Fig ) . Next , using purified antibodies , we screened the potent binders for viral neutralizing activity . All four DENV serotypes were evaluated . NT50 ( antibody concentration required to produce 50% viral neutralization ) is estimated based on the antibody titration curves ( Fig 2A ) using a 4-parameter fitting model . NT50 values are listed in Table 2 . Antibodies specific to only one DENV serotype demonstrated higher potency and binding affinity . As shown in Table 2 , d182 neutralized only DENV1 with an NT50 of 0 . 05 μg/ml . Antibodies d511 and d628 neutralized only DENV2 . Antibody d511 neutralized DENV2 with an NT50 of 0 . 0047 . Antibody d628 neutralized DENV2 with an NT50 of 0 . 0046 μg/ml . Two other mAbs , d50 and d145 , inhibit DENV2 infection at higher concentrations , with NT50 of 1 . 02 μg/ml and 0 . 59 μg/ml , respectively . Notably , the DENV3 specific antibody d622 demonstrated no inhibition of DENV infection , despite its high binding affinity to DEN3-80E ( Table 3 ) . Antibody d559 neutralized only DENV4 with an NT50 of 0 . 0226 μg/ml . The cross-reactive antibodies had lower binding affinities and neutralizing potency than did single-serotype specific mAbs . One antibody , d462 , showed neutralizing activity against both DENV3 and 4 with DENV3 at NT50 0 . 9476 μg/ml and DENV4 at NT50 0 . 0029 μg/ml . The antibody d448 showed cross-neutralizing potency against all four serotypes with NT50 for DENV1 at 0 . 13 μg/ml , NT50 for DENV2 at 0 . 33 μg/ml , NT50 for DENV3 at 1 . 25 μg/ml , and NT50 for DENV4 at 3 . 33 μg/ml ( Table 2 ) . Next , using surface plasmon resonance ( SPR ) , we determined kinetic binding constants of neutralizing mAbs , ka ( on rate ) , kd ( off rate ) , and KD ( equilibrium dissociation constant ) . The potent mAbs targeting specific DENV serotypes ( d182 , d511 , d628 , d622 and d559 ) demonstrated slow off rates ( Table 3 , Fig 2B , S3 Fig ) . The neutralizing activity of the mAbs is consistent with the binding affinity , except in the case of mAb d622 . Antibody d462 has similar on-rate ( ka ) for both DENV3 and DENV4 . Interestingly , mAb d462 has a faster off-rate ( kd ) for DEN3-80E than for DEN4-80E and shows weaker neutralizing potency for DENV3 ( NT50: 0 . 9476 μg/ml ) than for DENV4 ( NT50: 0 . 0029 μg/ml ) ( Table 3 , Fig 2B ) . These results suggest that the slow off-rate ( kd ) of mAb d462 plays a key role in its DENV neutralizing activity . Identification of binding epitopes is crucial for understanding the mechanism of antibody function . We conducted epitope mapping for antibodies with strong neutralizing activities ( d182 , d511 , d628 , d559 , d462 , and d448 ) by screening a shotgun alanine scan mutagenesis library of dengue E proteins [53] . The critical residues for antibody bindings were identified within the indicated serotype ( Table 4 , S4 Fig ) . Amino acid residues key for binding of antibodies that have cross-reactive neutralizing activity fall into regions that are more conserved among the four DENV serotypes than regions containing residues key for binding of antibodies specific to a single serotype ( S5 Fig ) . Antibodies d511 and d628 bind to the BC loop ( D329 ) and DE loop ( K361 ) in the lateral ridge of DENV2 DIII . Residues D329 and K361 are located at the tip of the loop turn , displaying their side chains on the external surface of the mature virus capsid ( Fig 3A and 3B ) . The key residues D329 and K361 have been previously reported in mouse anti-DENV2 and DENV1 antibody studies [37 , 56] . Antibody d182 binds in the BC loop ( T329 ) and I-III hinge region ( V300 ) of DENV1 ( Fig 3C ) . It has been demonstrated that mutations in the DI-III hinge impair dengue virus particle assembly in the cell [57] . It is worth noting that the epitopes of d182/d511/d628 are in the same BC loop of the lateral ridge ( S5 Fig ) . This result suggests that the lateral ridge region is essential in dengue virus infection , as antibodies targeting the region possess potent DENV-neutralizing activities . Antibody d559 binds to the bc loop ( Y81 , K83 ) of DII on DENV4 . This epitope adjacent to the fusion loop may contribute to blocking virus fusion with the endosomal membrane ( Fig 3D ) . It has been reported that some highly potent cross-reactive human antibodies have epitopes in the bc loop [46] . However , antibody d559 , with an epitope ( Y81 , K83 ) in the bc loop , showed binding only to DENV4 . Notably , the two amino acids bound by d559 are not conserved among the DENV serotypes ( S5 Fig ) . To confirm the epitopes , we generated DENV reporter virus particles ( RVP ) , in which amino acid residues in the epitope were substituted with relative amino acids for their impact on neutralizing activity of the antibodies . The RVPs with a detectable titer were chosen for neutralization assay ( S1 Table ) . As shown in Fig 3E , residue substitutions of V310 , T329 abolished the neutralizing activity of d182 , suggesting that these are key residues for interaction of the DENV1 E protein with the antibody . The mutations in DENV2-RVP ( D329G/E , K361G ) , and DENV4-RVP ( Y81G , K83G ) inhibited neutralizing activity of the indicated antibodies ( Fig 3F , 3G and 3H ) . These results validated the epitopes mapped by screening the shotgun alanine scan mutagenesis library of dengue E proteins . In addition , it is worthy to note that the Lysine substitution to Arginine in the DENV2 ( K361R ) and DENV4 ( K83R ) retain the neutralizing activity of the antibodies due to the similarity of the residues ( Fig 3F , 3G and 3H ) . Antibody d462 , a cross-reactive antibody , recognized the F0 and G0 β-sheets and the G0H0 loop in DI of DENV4 ( V160 , V173 , D177 ) ( Fig 4A and 4B , S5 Fig ) . It has been reported that binding of chimpanzee antibody 5H2 inhibits the fusogenic conformational change in the adjacent monomer on the virus surface [50] . Antibody d462 has epitopes overlapping those of antibody 5H2 on DENV4 ( Fig 4C ) . We measured the ability of each of the two mAbs to inhibit binding of the other to DEN4-80E using bio-layer interferometry . While d462 completely inhibited binding of 5H2 to DEN4-80E ( S6A Fig ) , 5H2 also inhibited binding of d462 ( S6B Fig ) , confirming that the two antibodies share overlapped epitopes on DEN4-80E . Antibody d462 may neutralize DENV4 by preventing fusion of the viral and endosomal membranes , as does the chimpanzee antibody 5H2 . Although the key amino acids identified for the epitope of d462 on DENV4 are not identical among the four DENV serotypes , DENV3 and DENV4 share the same amino acid at position 160 ( V ) , and very similar types of amino acids in two other locations ( V vs . A at 173 and D vs . E in 177 ) ( Fig 4C ) . The similarity in the amino acids at key positions may provide a structural basis for the cross-reactivity of d462 with DENV3 and DENV4 serotypes ( Fig 4D ) . To validate the epitope in a functional assay , we measured the neutralizing activity of d462 to DENV4-RVPs with residue substitutions ( V160A/G , V173A/G , D177G ) ( S1 Table ) . As shown in Fig 4E , V160 is the critical residue in the epitope , substitution of V160 to either alanine or glycine abolished the neutralizing activity of d462 . In contrast , V173 and D177 are non-essential residues for the neutralizing activity of d462 . Strikingly , antibody d448 showed cross-reactivity to all four serotypes and has an epitope composed of five key amino acids—D215 , P219 , M237 , Q256 , and G266 ( Green spheres , Fig 5A in DENV4 ) . Those key amino acids are located at the buried interface between the M protein and the ectodomain of the E protein ( Fig 5A , 5B and 5C ) . Four out of the five key amino acids of the antibody d448 epitope are conserved among the four dengue serotypes . DENV4 has a methionine ( M ) at position 237 . The other three serotypes have leucine ( L ) at position 237 ( Fig 5B ) . A direct interaction occurs between the two charged amino acids , residue D215 on E protein and R38 in the amphipathic perimembrane helix of one M protein ( Fig 5D ) . It has been reported that the M and E proteins contact mainly through three distinct hydrophobic interaction pockets on the E protein [58] . In addition to the key amino acid D215 , all the key amino acid residues for mAb d448 binding are in contact with the three hydrophobic pockets ( Fig 5E ) . Hydrophobic pocket 2 is composed of H209 and T212 on E protein . Hydrophobic pocket 3 is composed of T206 and H261 on E protein ( Fig 5F ) . The key amino acid residue of the d448 binding epitope , G266 , is located between hydrophobic pocket 2 and pocket 3 . G266 creates a barrier for the two hydrophobic pockets , contacting with H7 and T19 on M protein ( Fig 5F ) . Binding of antibody d488 to the epitope directly blocks both pocket 2 and pocket 3 on the E protein . Residues P219 , M237 , and Q256 in the antibody d488 epitope are adjacent to the hydrophobic pocket 1 . This pocket is composed of L216 , L218 , and M260 on E protein and the V2 on M protein ( Fig 5G ) . Based on the location of those key residues , we propose that antibody d448 interaction with the key structural components in dengue viral coat proteins disturbs the M-E interaction and inhibits virus ‘breathing’ or maturation . To confirm the epitope of d448 , we generated the DENV2-RVPs and DENV4-RVPs with Alanine substitutions ( D215A , P219A , L/M237A , Q256A , and G266A ) . However , two DENV2-RVP constructs ( Q256A , G266A ) and one DENV4-RVP construct ( D215A ) cannot produce infectious particles with detectable reporter signal , and these were excluded in the neutralization assay ( S1 Table ) . As shown in Fig 5H and 5I , mutations of the residues in the epitope dramatically abolished the neutralizing activity of d448 , when compared to the wild type RVP . These results validate the epitope of d448 . Interestingly , the interface epitope of d488 is conserved in the flavivirus family ( S7 Fig ) . We hypothesized that d448 might also neutralize other flaviviruses . We first assessed the cross-reactivity of d448 to the purified E proteins of Zika virus , yellow fever virus ( YFV ) , and West Nile virus ( WNV ) . As shown in Fig 6A , d448 binds to YFV E protein with an EC50 of 0 . 028 and WNV E protein with an EC50 of 0 . 032 μg/ml . In contrast , d448 show the weaker binding to Zika E protein with an EC50 of 0 . 5 μg/ml . Next , we evaluated the neutralizing potency of d448 to the four flaviviruses . Antibody d488 exhibited weak neutralizing activity ( around 30% ) to Zika and WNV at concentrations above 1 μg/ml ( Fig 6B ) . Antibody d488 exhibited minimal neutralizing activity of WNV ( about 30% at a concentration of above 30 μg/ml ) . To determine sequence diversity of those antibodies with potent neutralization function , we used NCBI/IgBLAST to analyze the V ( D ) J-gene family assignment . The V-gene usage for heavy chains of the IgGs shows a high percentage of IgHV3 ( 36% ) and IgHV4 ( 52% ) among the mAbs ( Fig 7A ) . The remaining gene families—IgHV1 , 2 , 5 , 6 , and 7—combined to make up only ~9% of the expressed antibody gene repertoires . For the light chain , the Ig-kappa ( κ ) repertoires predominantly expressed Igκ-V1 ( 64% ) , with the remaining gene families accounting for the other 36% ( Fig 7B ) . The Ig-lambda ( λ ) repertoires predominantly expressed Igλ-V1 ( 42% ) . Igλ-V5 accounted for another 25% and Igλ-V2 accounted for 24% ( Fig 7C ) . The profile of V-gene usage for the cloned DENV antibodies is very similar to the distribution of the V-gene families in circulating IgG repertoires of rhesus macaques [59] . To determine the association among heavy chain and light chain gene families , we analyzed gene families for each paired heavy chain and light chain sequence isolated from single antibody-secreting B cells . As shown in Fig 7D , the VH4-VK1 ( 22% ) and the VH3-VK1 ( 13% ) pairs are the top two types of pairing and represent the dominant germline of VH4 , VH3 , and VK1 . To assess the antibody maturation in DENV neutralizing antibodies in comparison to the entire panel of cloned antibody sequences , we analyzed the rate of antibody V-gene somatic hypermutation ( SHM ) and the CDR3 length . There were no significant differences in the average mutation rate ( Fig 7E ) . The heavy chain CDR3 length of the neutralizing antibodies is similar to that of the other mAbs , ranking from 5 to 25 amino acids . The majority of mAbs ( 87% ) have a CDR3 length of 9–18 amino acids in both groups ( Fig 7F ) . However , it is worth noting that the cross-reactive neutralizing antibodies , d462 and d448 , each have a long heavy chain CDR3 ( 19 amino acids ) . The whole panel of 780 antibodies has an average of 14 amino acids in the heavy chain CDR3 . We further analyzed the VH germline of the neutralizing antibodies in comparison with the usage of total cloned antibodies . Distribution of the VH4 germline of neutralizing antibodies was 81% , while the antibodies overall showed 52% usage of VH4 ( Fig 7G ) . Interestingly , the six potent neutralizing antibodies express the VH4 germline ( S2 Table ) . In contrast , the germlines for light chain sequences did not show any preferential distribution ( S8 Fig , S2 Table ) .
The objective of this study was to: ( 1 ) evaluate the efficacy of an experimental DENV vaccine by the elicited neutralizing antibodies , and ( 2 ) characterize the epitopes recognized by the mAbs and examine their potency of neutralization . Although NHPs typically do not develop the disease as humans do following dengue viral infection , they are still the most valuable models available for evaluating the efficacy of experimental dengue vaccines [16 , 55] . In our previous study , the primer-boost strategy was shown to induce balanced type-specific and broadly neutralizing humoral responses to the desired DENV serotypes in rhesus macaques [20 , 21] . Therefore , characterization of macaque mAbs against dengue vaccine is valuable for better understanding the immunologic responses of NHPs . The NHP antibodies with epitopes more closely related to those of human antibodies may better inform the preclinical development of vaccines . They are particularly relevant if antibodies can be characterized that recognize diverse epitopes that are neutralizing antibody binding sites also targeted by the human antibody repertoire . In this study , we cloned and analyzed a large panel of DENV reactive antibodies from single ASCs including both serotype specific and broadly neutralizing mAbs . Among the 780 antibodies cloned , 130 showed binding to the DENV-80E protein , with a positive rate of 17% in ELISA . We used LAV displaying dimeric E protein as a priming vaccine and monomer subunit E proteins to boost the immune response . With the booster dose , only about half of the E-antibodies binding to monomeric E protein could be enriched , according to the previous human study [51] . Nevertheless , we were able to isolate six potent antibodies with diverse neutralizing epitopes from the rhesus macaque model . These included antibodies binding to the lateral ridge of DIII ( d182 , d511 , d628 ) , the I-III hinge ( d182 ) , the bc loop adjacent with the fusion loop ( d559 ) of DII , the β-strands and the loops of DI ( d462 ) , and the interface of E protein with the M protein in DII ( d448 ) . Epitopes within some of those motifs or domains have been reported for dengue neutralizing antibodies from different species including mouse , chimpanzee , and humans [46 , 50 , 54] . As expected , the vaccination using monomeric E protein in our study did not yield any neutralizing antibodies targeting E dimer epitopes as have been reported for antibodies from human patients [51 , 60] . Nevertheless , isolation of potent neutralizing antibodies with diverse epitopes in this study provides a strong validation for the efficacy of the vaccine and immunization strategy . DIII was identified as the dominant epitope for the mouse antibodies but not for human antibodies [42] . Mouse antibodies elicited by DENV1-4 immunization target DIII and exhibit potent neutralization [38 , 61–63] . Epitope mapping of these antibodies , with random recombinant DIII mutants , identified the lateral ridge in DIII as the target of strongly neutralizing antibodies . Similarly , we identified three potent neutralizing antibodies recognizing critical residues T/D329 ( d182 , d511 , d628 ) and K361 ( d511 , d628 ) in the lateral ridge of DIII . We propose that the lateral ridge epitope represents an important neutralizing site on DENV for rhesus macaque . In this study , we also isolated neutralizing antibodies recognizing the DI-DIII hinge ( d182 ) , DI ( d462 ) , and DII ( d559 , d448 ) . Prior to our study , little was known as to how rhesus macaque neutralizing antibodies recognized DENV . A non-DIII antibody response has been shown to be induced by live DENV infection in rhesus macaque . However , in the same study , the alphavirus vector-based dengue E dimer vaccine-induced predominantly DIII neutralizing antibodies in rhesus macaques [64] . According to our data , the existence of other epitopes in distinct regions should not be ruled out for rhesus macaque antibodies . The deviations of the epitopes from those reported in different studies may be due to various factors that cause differences in the B cell repertoire . Namely , variations in the immunization strategy , primary screening , and genetic profiles of the rhesus macaques could be reflected in the antibody response . Identifying the target epitopes of the antibodies , generated by rhesus macaque post vaccination , is the best way to understand the molecular determinants of the NHP immune response to DENV . It is worth noting that the epitopes we reported in this study are critical for human dengue antibodies as well as for dengue antibodies from other species [46 , 54 , 65] . The mAb d182 recognizes V300 in the I-III linker ( E299-304 ) and the T329 in DIII . A panel of mouse DIII-neutralizing antibodies also targets this region [38] . The I-III linker is essential for dengue virus particle assembly in the cell and interaction of E protein with the heparin receptor [57] . These results suggest that d182 may inhibit virus entry by blocking DENV binding to the cell surface heparin sulfate , which is the initial step of infection . We identified that the mAb d559 binds the bc loop of DII , adjacent to the fusion loop . Previously , the potent human antibody 1C19 was reported to recognize this epitope and could neutralize all four serotypes better than any antibodies targeting the fusion loop [46] . Our study also indicates that d462 recognizes DI epitopes are overlapping that targeted by chimpanzee neutralizing antibody 5H2 [50] . This result shows that the d462 may block the virus infection by the same mechanism , that is , by preventing conformational change during fusion . The unique epitopes revealed by this study suggests a focus for rational vaccine design based on the novel immunogens of DENV . This study , for the first time , identified a broadly neutralizing DENV mAb d448 with a novel epitope at the interface of the E and M proteins . The mAb d448 is likely binding to the E protein in the immature DENV virus , interfering with DENV “breathing” by blocking the E-M protein interaction [66 , 67] . As the E protein dynamically switches between dimers and trimers during DENV “breathing , ” mAb d448 may block the E protein transition from dimer to trimer . The dengue E protein bears greater than 50% homology to the Zika virus E protein [68] . Monoclonal antibodies isolated from dengue infected patients could inhibit Zika infection [69 , 70] . However , the broadly dengue neutralizing antibody d448 identified in this stuy showed weak neutralizing activity to Zika and other flaviviruses . It has been reported that “group B” antibodies isolated from infected human patients also interact with the D215 and A267 residues in the DII domain in DENV4 , which partially overlaps with the epitope of d448 [71] . The “group B” antibodies are relatively weaker neutralizers in vitro , which may explain the fact that antibody d448 is not a particularly strong neutralizer when compared with other neutralizing antibodies isolated in this study . Significance of the d448 epitope in dengue neutralization in the clinical setting warrant further study . The cross-reactivity of serum from patients with DENV and Zika virus , and the antibody dependent enhancement ( ADE ) of infection has presented a challenge for accurate identification of the infecting agent , and diseases control [72 , 73] . This raises questions regarding the role of the DENV antibodies in protective immunity and impact on pathogenesis . In our study , most of the antibodies are relatively weak in neutralizing activity or binding affinity . The less potent cross-reactive antibodies can induce ADE to different dengue serotypes and the Zika virus . We have not yet assessed the ADE effect of the isolated rhesus antibodies . Further studies to determine whether the rhesus antibodies can induce ADE activity will be essential for understanding the efficacy of the experimental dengue vaccine . The NHP could be used as a model for the study of ADE in flavivirus pathogenesis [74] . In our study , two neutralizing antibodies ( d511 , d628 ) recognizing the lateral ridge of DENV2 DIII were isolated from two different macaque subjects . They share similar CDR3 sequences and both heavy and light chains are of the same germlines . The “unique” paired genetic restriction to specific V-genes , and CDR3 sequences indicate that the antibodies might be dominant in the DENV2-neutralizing lineage of the B cell repertoire . Similarly , detection of the dominant VCR01 classes of antibodies has been reported in HIV studies and the restricted IGHV1-69 germline in influenza studies [75 , 76] . Based on the analysis of our entire panel of 780 mAbs , a similar divergence in antibody germlines was shown in this study as those reported for normal and HIV-infected macaque [59 , 77] . However , neutralizing antibodies are biased towards the VH4 germline , and the light chain did not show a clear preference for any particular germline . These results indicate that the heavy chain may be subject to more selection pressure on humoral response to this vaccination . In summary , this study profiled antibody responses to an experimental vaccine in NHP and laid a foundation for further evaluation of the vaccine in the preclinical trial . The novel neutralizing antibodies can be evaluated for therapeutic potential for the treatment of dengue infection . Epitopes of the very potent neutralizing monoclonal antibodies offer insights for better dengue vaccine design .
The live attenuated vaccine ( gifted by Dr . Stephen S . Whitehead , National Institutes of Health ) comprised dengue types 1–4 ( rDEN1-rDEN1Δ30–1545; rDEN2-rDEN2/4 Δ30 ( ME ) -1495 , 7163; rDEN3-rDEN3Δ30/31-7164; and rDEN4-rDEN4 Δ30–7132 , 7163 , 8308 ) . The tetravalent dengue subunit vaccine ( V180 , Merck and Co . Inc . ) comprises truncated forms of envelope proteins ( DEN-80E ) , derived from strains of all four dengue virus serotypes ( DENV1 strain 258848 , DENV2 strain PR159 S1 , DENV3 strain CH53489 , and DENV4 strain H241 ) . The immunization strategy has been reported previously [22] . Briefly , all the subjects received the live attenuated vaccine subcutaneously at 0 weeks and then received subunit DEN-80E vaccine formulated with Alhydrogel adjuvant ( Brenntag Biosector ) intramuscularly at 16 weeks . Bleeds were taken 7 days after a boost . PBMCs were isolated and stained for FACS sorting as described [22] . The targeted sorting population was CD3−/CD19low to +/CD20– to low/ sIgG−/CD38+ /CD27– or + . The sorted cells were stored at -80°C until analysis . Indian Rhesus macaques were domestically bred , raised and maintained at New Iberia Research Center ( NIRC ) of University of Louisiana at Lafayette , New Iberia , LA , USA . Flavivirus naïve ( i . e . sera negative for Dengue virus 1 , 2 , 3 , 4 and West Nile virus ) monkeys of either sex , weighing more than 3 kg were used in this study . The animal studies were approved by the University of Louisiana at Lafayette Institutional Animal Care and Use Committee ( IACUC ) and conducted in accordance with the US Public Health Service ( PHS ) Policy on Humane Care and Use of Laboratory Animals . All animals were socially housed for the study . The dimensions of the cage for paired housing is 8 . 6 ( floor area , square footage ) by 30 ( height , inch ) . Each animal’s holding cage was cleaned daily . Animals were provided with object ( s ) to manipulate or explore . Harlan Teklad Monkey Chow , or its equivalent , was provided daily in amounts appropriate for the size of the animal . The basic diet was supplemented with fruit and novel treats including small quantities of fresh fruits , nuts , or seeds , 2 to 3 times weekly as part of the site’s environmental enrichment program . Tap water was provided ad libitum via automatic watering device . No contaminants are known to be present in the food or water which would interfere with the results of this study . Food was withheld at least 2–3 hours on days of study procedures to insure safe sedation and was offered upon recovery from sedation . Animals were observed twice daily throughout the study for any abnormal clinical signs , signs of illness or distress . All animals were returned to the colony at NIRC at the end of the study . The natively paired antibody genes were cloned from single cells , as previously described [22] . Briefly , reverse transcription was carried out to synthesize the cDNA from each single cell using a one-step cDNA synthesis kit ( Bio-Rad ) according to the manufacturer’s protocol . Antibody variable regions were amplified by a two-step nested PCR with 3 . 5 μl of the template cDNA . All PCR reactions were performed in 96-well plates in a volume of 25 μl per well with our validated primers [22] . The purified gene fragments were inserted into the vector with the human IgG1 constant region . The human-macaque chimeric antibodies were expressed in HEK293-F cell lines ( Thermo Fisher Scientific ) . IgG sequences were analyzed for their CDR3 length , germline IgH , Igκ , and Igλ V-gene family distribution using IgBLAST with IMGT V domain delineation ( https://www . ncbi . nlm . nih . gov/igblast/ ) . The method of antibody expression in mammalian cells and purification by Protein A has been described previously [78 , 79] . Briefly , 0 . 5 μg aliquots of heavy chain plasmid and light chain plasmids ( total of 1 μg plasmids/1 ml of the transfected cell ) were co-transfected into HEK293-F cell lines for transient expression with TrueFect reagent ( United BioSystems ) . The supernatants were harvested 7 days after transfection . Antibodies were purified with Protein A resin ( Repligen ) according to the manufacturer’s instructions . Recombinant flavivirus envelope protein was coated on the 96-well plates with the concentration of 1 μg/ml at 4°C overnight . Plates were blocked with 3% BSA in PBS , then incubated with the supernatants or purified mAb in 3-fold serial dilutions . After incubation for 1 . 5 hours at room temperature , the plates were washed 3 times with PBST ( 0 . 5% Tween-20 in PBS ) , followed by addition of horseradish peroxidase ( HRP ) coupled goat anti-human IgG ( Sigma ) and detected using TMB Substrate ( Thermo Fisher Scientific ) for absorbance signal at OD450 nm using a 96-well plate reader ( Molecular Devices ) . Samples having an OD450 nm of above or equal to 0 . 1 were designated as positive binders . The OD450 of native control were below 0 . 4 . All assays were replicated three times . Micro-neutralization assay was based on staining viral E protein with near-infrared fluorescent dye ( IRD ) -labeled reagents , as previously described [80] . Antibody concentration titrations were used in each assay and concentration to generate 50% neutralization activities ( NT50 ) was derived from the antibody titration curve using GraphPad Prism ( GraphPad Software Inc . ) with a nonlinear regression and 4-parameter curve fitting model . For the rhesus macaque sera tittering , two-fold serial dilutions of heat-inactivated serum samples from vaccinated animals were incubated for 1 h at 37°C with 50 PFU of each DENV . This virus-serum mixture was then added onto Vero cells in 96-well plates and incubated for 4 days . Serum end-point neutralization titers ( LiCor50 ) were defined as the reciprocal of the highest serum dilution that blocks 50% of the DENV infection when compared to virus control included on each assay plate , using an infrared Odyssey Sa imaging system ( Li-Cor Biosciences ) [81] . Zika ( SMGC-1 strain ) , yellow Fever virus ( 17D strain ) , and West Nile virus ( Kunjin , MRM61C strain ) were propagated in Vero cells as previously described [82 , 83] . For antibody neutralizing activity of d448 , the flow cytometry-based neutralization assay was conducted using our previous method [82 , 83] . Briefly , 2×105 of Vero cells were seeded in each well of 24-well plate 24 h before virus infection . The purified antibody d448 was serially diluted and incubated with the virus ( 5×103 PFU ) for 1 h at 37°C . Vero cells were then incubated with the antibody and virus mixture for 40 h at 37°C supplied with 5% CO2 . Afterward , infected cells were collected by trypsin digestion , fixed and permeabilized by Fixation and Permeabilization solution ( BD Biosciences ) ; and stained with pan-flavivirus antibody Z6 ( for ZIKV , YFN , and WNV ) at 2 μg/ml [82] . Cells were stained with FITC-conjugated secondary antibody on ice for 30 m . The percentage of positive cells were measured using BD FACSCanto II . Sigmoidal neutralization curves were generated using GraphPad Prism 5 . The CM5 sensor chip was pre-activated according to the manufacturer’s instructions ( Biacore T100 , GE Healthcare ) . The dengue antigens ( DEN-80E ) were immobilized on the sensor chips and reached targeted 1500 RU . Each antibody ( analyte ) was serially diluted 2 folds down with concentration ranges at 0–200 nM in running buffer . Association and dissociation were conducted at a flow rate of 30 μl/min with a 5-min association followed by 5-min of dissociation , at 25 °C . Then , the chip was regenerated by two 20-second pulses of 3 M MgCl2 . The data was analyzed using the standard Biacore T100 evaluation software with the 1:1 Langmuir binding model for ka , kd and KD determination . An Octet equipped with protein A-coated sensor ( ForteBio , Octet RED96 ) was used to bin DEN4-80E with the mAbs d462 and 5H2 . First , 30 μg/ml of d462 and 5H2 were captured onto protein A sensor for 600 seconds . Followed by a blocking antibody ( 200 μg/ml ) to saturate the unoccupied sensors for 300 seconds , the DEN4-80E was loaded in an association step onto the sensor at 30 μg/ml for 300 seconds . The Sensors with DEN4-80E provide a surface for the binding of the secondary mAbs d462 , 5H2 , d559 , d182 . If the second antibody showed mass accumulation to the sensors , it was considered to bind to a different epitope than the captured antibody . The mAb d559 was used as positive control , d182 as a negative control . Epitope mapping was conducted using comprehensive mutation libraries made of prM/E from all four DENV serotypes [53] . The DENV1 library consists of random mutations introduced at each residue of the DENV1 prM/E polyprotein ( strain WestPac ) , while for DENV2 ( strain 16681 ) , DENV3 ( strain CH53489 ) , and DENV4 ( strain 341750 ) , each prM/E residue was individually changed to alanine ( and alanine residues to serine ) . All mutant clones were sequence confirmed and arrayed into 384-well plates ( one mutation per well ) . Mutagenesis achieved >97% coverage of prM/E residues for each serotype , a total of over 2 , 400 mutations . Antibody d182 was mapped on the DENV1 library , antibodies d511 and d628 on the DENV2 library , and antibodies d462 , d448 , and d559 on the DENV4 library . For each screening , a DENV prM/E library was expressed in HEK-293T cells and assayed by immunofluorescence for mAb binding as described previously [84] . In some cases , mAbs were also screened as Fabs after conversion by papain digestion . Antibodies were detected using AlexaFluor488-conjugated secondary antibody ( Jackson ImmunoResearch Laboratories ) . Cells were washed three times with PBS/+0 . 2% saponin followed by two washes in PBS . Mean cellular fluorescence was detected using a high-throughput flow cytometer ( HTFC , Intellicyt ) . Antibody reactivity against each mutant protein clone was calculated relative to wild-type protein reactivity by subtracting the signal from mock-transfected controls and normalizing to the signal from wild-type prM/E-transfected controls . Mutations within clones were identified as critical to the mAb epitope if they did not support reactivity of the test mAb but supported reactivity of other antibodies . This counter-screen strategy facilitates the exclusion of DENV protein mutants that are misfolded or have an expression defect . The mAbs D004 , D449 , D168 , D413 , D341 , and D195 were used as positive control . DENV-RVPs were produced in HEK-293T cells by co-transfection with plasmids of DENV structural genes ( CprME ) and WNV subgenomic replicon . The plasmids encoding DENV2-CprME ( strain: 16681 ) and the WNV subgenomic replicon with Renilla luciferase reporter gene ( pWNII-rep-Ren-IB ) were described previsuly [85] . We replaced the DENV2-CprME with CprMEs from DENV1 ( strain: Hawaii ) and DENV4 ( strain: H241 ) , respectively , to generate other serotype RVPs . The amino acid residue substitution mutants were created using site directed mutagenesis by PCR . Briefly , HEK-293T cells were plated at a density of 400 , 000 cells per well in 12 well plate overnight , the cells were then co-transfected with 1 . 5 μg of DENV structural gene and 0 . 5 μg of WNV subgenomic replicon . After 4 hours , the culture medium was replaced with low glucose formulation of DMEM ( 7% FBS ) , then the cells were cultured at 33°C under 5% CO2 for another 48 hours . The RVPs in the supernatants were harvested by passing through 0 . 45 μm filters . For infection assay in 96-well plate , Vero cells were added to each well at a density of 30 , 000 cells per well in 100 μl of DMEM complete medium ( 7% FBS ) , then 100 μl RVP were added to each well with the neutralizing antibodies at the final concentration of 3 μg/ml followed by incubation at 37°C for 48 hours . The infected cells were lysed in 20 μl of lysis reagent and assayed using luciferin-containing substrate ( Promega: Renilla Luciferase Assay System , E2810 ) . Luminescence was measured using a luminometer . The relative titer of the mutants was normalized to the wildtype RVP . The assays were performed in triplicates and data were analyzed using GraphPad Prism . Protein sequence alignment of DEN-80E of DENV1 , DENV2 , DENV3 , and DENV4 was generated and displayed using ClustalW2 and ESPript 3 . x [86 , 87] . The NCBI accession numbers are ACJ04226 ( DENV1 ) , AGS49173 ( DENV2 ) , AJA37731 ( DENV3 ) and ACW82884 ( DENV4 ) , respectively . The E protein model and the amino acid representation of the epitopes were displayed using UCSF chimera ( http://www . rbvi . ucsf . edu/chimera ) . The protein data bank ( PDB ) accession numbers of the dengue envelope protein are 1UZG [88] , 3G7T [89] , 1OK8 [25] , 3UC0 [50] , 3C6E [24] , 3J27 [67] .
|
Dengue virus ( DENV ) is a leading cause of human illness in the tropics and subtropics , with about 40% of the world’s population living in areas at risk for infection . There are four DENV serotypes . Patients who have previously been infected by one dengue serotype may develop more severe symptoms such as bleeding and endothelial leakage upon secondary infection with another dengue serotype . This study reports the extensive cloning and analysis of 780 monoclonal antibodies ( mAbs ) from single B cells of rhesus macaques after immunization with an experimental dengue vaccine . We identified a panel of potent neutralizing mAbs with diverse epitopes on the DENV envelope protein . Antibodies in this panel were found to bind to the lateral ridge of DIII , the I-III hinge , the bc loop adjacent to the fusion loop of DII , and the β-strands and the loops of DI . We also isolated one mAb ( d448 ) that can neutralize all four dengue serotypes and binds to a novel epitope at the interface of the DENV envelope and membrane proteins . Further investigation of these neutralizing monoclonal antibodies is warranted for better vaccine efficacy evaluation and vaccine design .
|
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2019
|
Potent neutralizing antibodies elicited by dengue vaccine in rhesus macaque target diverse epitopes
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Terpenoid synthases construct the carbon skeletons of tens of thousands of natural products . To predict functions and specificity of triterpenoid synthases , a mechanism-based , multi-intermediate docking approach is proposed . In addition to enzyme function prediction , other potential applications of the current approach , such as enzyme mechanistic studies and enzyme redesign by mutagenesis , are discussed .
The terpenoids , also called isoprenoids , are one of the largest and most structurally diverse classes of natural products , and play vital roles in almost all life forms [1] , [2] . In the biosynthesis of terpenoids , the isoprene units ( C5 ) are assembled by polyprenyl transferases to give long chain terpenes such as geranyl diphosphate , farnesyl diphosphate , geranylgeranyl diphosphate , and squalene , which can then be converted into diverse carbon skeletons by the terpenoid synthases ( TPSs ) [3] , [4] . Understanding the specificity of TPSs is of great significance to biochemistry , organic chemistry and medicinal chemistry . According to the number of isoprene units ( C5 ) of the substrates , TPSs can be classified into hemiterpenoid ( C5 ) , monoterpenoid ( C10 ) , sesquiterpenoid ( C15 ) , diterpenoid ( C20 ) , sesterterpenoid ( C25 ) , triterpenoid ( C30 ) and sesquarterpenoid ( C35 ) synthases . Most TPSs have one of two distinct protein folds [5]–[7] , an α fold ( class-I ) and a βγ fold ( class-II ) . For “class I” enzymes , the reaction is initiated by Mg2+-assisted removal of the diphosphate group , e . g . , in limonene synthase [8] ( Figure 1a and Figure 2a ) , while for “class II” enzymes , an acidic residue ( normally Asp ) initiates protonation of a double bond or an epoxy oxygen , e . g . , in squalene-hopene cyclase [9] , [10] ( Figure 1b and Figure 2b ) . Both reaction types produce carbocation-olefin intermediates that undergo diverse cyclizations ( rearrangements ) , followed by quenching of the carbocations via deprotonation or hydroxylation [5] , [11] , [12] . Some diterpenoid synthases that have the αβγ fusion fold can sequentially use both class I and II active sites to catalyze even more complicated reactions , e . g . , the abietadiene synthase [13] . Some TPSs are promiscuous , e . g . the baruol synthase from Arabidopsis thaliana converts oxido-squalene into baruol ( 90% ) as well as 22 other minor products [14] . Other TPSs are highly specific , e . g . the human lanosterol synthase generates only lanosterol , which has 7 chiral carbons [15] . Sometimes , even a single mutation in the TPSs can completely alter their product specificity , e . g . the H234S and H234T mutants of the lanosterol synthase from Saccharomyces cerevisiae produce 100% protosta-12 , 24-dien-3β-ol and 100% parkeol , respectively [16] . Crystal structures of TPSs [5] , [6] , [8]–[10] , [13] , [15] , [17]–[30] provide a basis for understanding reaction mechanisms and specificity . As carbocations are short-lived , trapping the enzyme-bound intermediates is experimentally difficult . Therefore , high level quantum mechanics ( QM ) [31]–[35] and quantum mechanics/molecular mechanics ( QM/MM ) [36]–[40] calculations have been performed in order to understand the mechanisms of TPSs . Some in silico predicted catalytic mechanisms have been confirmed by experiments , e . g . a recent kinetic isotope effect ( KIE ) study on the mechanism of pentalenene synthase confirmed the QM-derived mechanism [41] . Hong et al . studied the catalytic mechanisms of a series of mono- , sesqui- and di-terpenoid synthases using QM methods , which have been summarized in a review article [32] . Based on QM/MM calculations , Rajamani et al . proposed that the product specificity of squalene-hopene cyclase is achieved by balancing thermodynamics and kinetic properties [39] . The aim of this and predecessor studies [42]–[53] is the development of robust methods for enzyme function prediction , using available sequence and structural information . In a recent work [50] involving a combination of bioinformatics , docking , homology modeling and enzymology , we have successfully predicted and experimentally validated the functions of 79 diverse members of the trans-polyprenyl transferase subgroup , which produces substrates for TPSs . Our long-term goal is essentially the same for the TPSs , i . e . building models to predict function of unknown enzymes [43] . However , due to the diversity of possible products , the TPSs present a more difficult problem than the polyprenyl transferases . Both the polyprenyltransferases and the TPSs create challenges for purely sequence-based function prediction , because small sequence changes ( including single point mutations ) may result in a different product profile [16] . We thus believe , and have demonstrated for the polyprenyltransferases , that structure-based modeling approaches can provide important information about function . In the case of the polyprenyltransferases , product specificity is determined , to a large extent , by the depth of the cavity in which the growing polyisoprenoid chain binds . The situation for TPSs is considerably more complicated , in that the size and shape of the binding site , as well as the ability to differentially stabilize multiple carbocationic intermediates ( and the transition states connecting them ) all contribute to product specificity [54] . In principle , QM/MM methods [55]–[62] are ideal for studying these complex sequence-structure-function relationships , as has been demonstrated in focused studies of the mechanisms of certain TPS enzymes [37]–[40] . However , these methods are computationally too expensive to be used in large-scale function prediction of uncharacterized enzymes . Even for a single TPS , studying all known reaction channels by QM/MM is time consuming ( to our knowledge , no such study has yet been reported ) . We hypothesize that molecular-mechanics-based “docking” methods , although they have a number of well-documented limitations , can nonetheless provide useful guidance concerning product specificity of TPS enzymes , with a throughput that is suitable for prospective investigations of large numbers of enzymes , as we have demonstrated for other classes of enzymes . The goal of our approach is not to eliminate experimental studies , which will be needed ( for the foreseeable future ) to test predictions , but rather to guide and focus the experimental studies . For TPS enzymes , long-term goals include the prediction of when/how changes in the binding sites impact specificity , and identification of TPS enzymes that may have novel activity ( or conversely , guide the design of such enzymes ) . We now describe a mechanism-based carbocation docking approach to predict function , and use the triterpenoid synthases [12] , [63]–[66] ( a subgroup of the class II TPS , proton initiated ) to illustrate this approach . Triterpenoid synthases , which are found in a wide variety species including bacteria , archaea , plants , fungi , and animals , are involved in the biosynthesis of multicyclic metabolites such as sterols and saponins [64] . In this work , we dock against crystal structures and homology models for a wide variety of experimentally characterized triterpenoid synthases , in order to test the mechanism-based carbocation docking approach . Previous enzyme function prediction studies using intermediate docking [42] , [44] , [67] , [68] have been conceptually simpler in that a single intermediate maps to one or a small number of possible substrates and products . In the case of TPSs , the number of possible substrates is small , but the number of potential products is enormous , and the generation of most products involves multiple carbocationic intermediates . Thus , instead of docking a single intermediate per reaction , we dock multiple intermediates along diverse reaction channels , in order to capture the mechanistic diversity ( reaction channels ) and product diversity of TPSs .
Triterpenoid synthases ( also called triterpene cyclases ) catalyze the cyclization of squalene or oxido-squalene into hundreds of natural products [63] , most of which are tetra- or pentacyclic structures such as lanosterol [15] and hopene [9] , [10] . Triterpenoid synthases utilize one of three distinct reaction channels ( Figure 3 ) [54]: 1 ) the hopene channel ( Channel A ) ; 2 ) the lupeol channel ( Channel B ) ; and the lanosterol channel ( Channel C ) . In this work , we used the two known crystal structures for triterpenoid synthases , squalene-hopene cyclase from Alicyclobacillus acidocaldarius ( PDB: 1SQC ) [9] , [10] and human lanosterol synthase ( PDB: 1W6K ) [15] , for docking and building homology models , both of which are wild-type and have ligand bound ( inhibitor for 1SQC and product for 1W6K ) . Figure 4 and Figure S1 show protein sequence similarity networks summarizing the known functions of the triterpenoid synthases , a bioinformatics tool that we have used extensively in the context of enzyme function prediction ( for details of network generation , see Methods ) . Enzyme functions can be defined by the Enzyme Commission ( EC ) numbers , which describe the overall reaction being performed by an enzyme . The EC number consists of four levels , where the first three levels broadly describe the types of reaction being performed , and the fourth level generally describes the substrate specificity of the enzyme's overall chemical transformation . EC numbers and other related chemical information ( e . g . , reaction channels ) can be mapped onto the sequence similarity networks ( Figure 4 and Figure S1 ) . To study enzyme functions with sequence similarity networks , different BLAST E-values [69] are scanned to gradually break the sequence similarity networks into smaller clusters until known enzyme functions are well segregated . At an E-value of 1E−60 ( an average sequence identity of 40%; obtained from the quartile plot see Figure S2 ) , the sequences are separated into two major clusters , each of which contains the structure of one enzyme; for this reason , we label them as the 1SQC cluster and the 1W6K cluster ( Figure 4a and Figure S1a ) . As the products of triterpenoid synthases are diverse , it is difficult to identify trends if we color the nodes according to EC numbers ( Figure S1a ) . Even at an E-value of 1E−220 or 1E−300 ( the average sequence identities are 50% and 70% , respectively; Figure S2 ) , enzymes with different EC numbers still do not segregate well ( Figure S1b ) , implying that it will be challenging to precisely predict function ( full EC number ) based on sequence alone . It is worth noting that the EC number generally only describes a single overall chemical transformation , thus is not well suited to categorizing promiscuous enzymes , which will catalyze several different EC numbers . However , the products of triterpenoid synthases group into a few classes based on their carbon skeletons , which are related to the “reaction channels” ( i . e . the series of carbocationic intermediates leading to various classes of products ) . Most of the reaction channels for the experimentally characterized enzymes can be separated at an E-value of 1E−300 in the sequence network ( Figure 4 ) , with only a few exceptions in cluster 1 ( Figure 4b ) . Thus , functional relationships that are obscured by EC numbers , based on the exact products , are revealed by focusing instead on the nature of the carbocationic intermediates ( and by implication the transition states connecting them ) that are , presumably , differentially stabilized by the various classes of enzymes . It should be noted that in Figure 4 , besides the three major reaction channels ( Channel A–C ) mentioned above , we also include a fourth Channel D ( cyan; Figure 3 ) , representing a recently discovered sesquarterpenoid ( C35 ) synthase [70] , [71] . As the crystal structure for this enzyme is not available and the sequence identity between this enzyme and 1SQC is low ( ∼25% ) , we cannot create a high quality model for this enzyme . In addition , the C35 intermediates corresponding to Channel D are predicted to bind poorly for most of our models ( in comparison to the other three channels; Table S1 ) , because the intermediates along Channel D are significantly different from those along Channel A–C in terms of size and shape [70] , [71] . Hence , we do not consider Channel D further , and focus only on C30 carbocationic intermediates corresponding to Channels A–C . As classical molecular mechanics methods do not correctly describe transition states , docking transition states is impractical . Invoking assumptions similar to those in the “high-energy intermediate” approach of Shoichet and co-workers [67] , we dock carbocationic intermediates . The primary difference is that , in this case , there is only one plausible substrate , but multiple possible intermediates that lead to different products . We hypothesized that by docking multiple intermediates ( and ranking the results hierarchically ) , we could predict the dominant reaction channels for triterpenoid synthases , and then predict the likely product/precursor intermediates along the predicted reaction channel ( rather than precise structures for the final products ) . At a minimum , we expected that we could at least exclude some implausible reaction channels , which have intermediates that are poorly stabilized by the enzyme , due to either steric clashes or electrostatic incompatibility . We do not dock every possible carbocation intermediate but only those that help distinguish the different reaction channels and product precursors . We first discuss the docking results for the two crystal structures mentioned above , i . e . , 1SQC and 1W6K , as an important test of the methodology . The key difference between the three major reaction channels ( Channels A–C ) is the stereochemistry of the 6 , 6-bicyclic and 6 , 6 , 6 , 5-tetracyclic carbocationic intermediates I1 and I2 , respectively ( Figure 3 ) . It should be noted that A-I1 and B-I1 are chemically identical but are represented by different conformations which can convert to chemically different intermediates A-I2 and B-I2 ( Figure 5 ) . The rule of configuration transmission in triterpenoid synthases has been extensively discussed [54]; the key concept is that , with limited rotational freedom in the active site cavity , conformational differences in the upstream intermediates will be transferred to the downstream intermediates . As a practical matter , docking different conformations of the same intermediate ( e . g . A-I1 and B-I1 ) results in different docking scores ( see Methods for details ) , which we interpret in terms of the predicted reaction channel . In order to take active site flexibility into account , an induced fit docking protocol is used for all docking calculations . Receptor flexibility is important to the current work because rearrangements of the carbocationic intermediates may slightly change the conformations of the active site residues . ( In addition , when using homology models , as described below , receptor flexibility can compensate for small errors in the models . ) To ensure the ligands are docked into a catalytically-relevant position , constraints were applied during the docking , which are essential for maintaining consistent poses of the carbocationic intermediates along the same reaction channel . Detailed procedures and parameters are provided in Methods . According to previous QM/MM studies on squalene-hopene cyclase [39] and lanosterol synthase [38] , there is only one transition state between I1 and I2 , whose reaction barrier is significant ( >10 kcal/mol ) . Therefore , we suggest that the transition state between I1 and I2 is a key specificity determinant for the three reaction channels defined above , and that stabilization of intermediates I1 and I2 can be used to distinguish reaction channels . However , we are aware that for some cases in which the binding affinities of the intermediates along different channels are very similar , this assumption may be insufficient . Figure 6a shows the docking scores for intermediates along three major reaction channels docked to the squalene-hopene cyclase crystal structure ( 1SQC ) . Intermediates along reaction channel A ( blue ) , which leads to the correct product hopene , clearly receive the most favorable docking scores . At present , we are unable to predict the specific products based simply on the docking scores . That is , the product precursor hopanyl cation ( A-I4 ) is only the third best binder , implying that the current docking approach is not able to accurately predict the correct precursor cation of the major product; quantum mechanical methods may be necessary to achieve such a goal . However , as the TPSs are often promiscuous , carbocation docking can at least identify several possible intermediates that could lead to the final products , e . g . the second best binder A-I2 is the precursor of 6 , 6 , 6 , 5-tetracyclic byproducts of squalene-hopene cyclase . In a previous QM/MM study on 1SQC [39] , the free energy barriers for the formation of the A-I2 and A-I4 intermediates were determined to be very similar ( 1 . 8 kcal/mol difference ) , but A-I4 is thermodynamically more stable ( >10 kcal/mol difference ) . One possible way to improve the prediction results is to run further QM/MM calculations to evaluate the most likely intermediates from our docking hits , as well as transition states between the intermediates , but this approach is computationally expensive and beyond the scope of the current work . Figure 6b shows the carbocation intermediate docking results for the lanosterol synthase crystal structure ( 1W6K ) . The sequence identity between 1SQC and 1W6K is only 25% , and most of the active site residues are different . In this case , the intermediates I1 and I2 for reaction channel C receive the most favorable docking scores ( I1 and I2 in Figure 6b ) . We also find that the product precursor C-I6 is the best binder among the intermediates along channel C ( from C-I1 to C-I9; Figure 6b ) . Figure 7a shows the docking pose of the product precursor intermediate C-I6 ( orange ) , which is in good agreement with the product lanosterol in the crystal structure ( grey; RMSD 0 . 23 Å ) . Figure 7b shows the docking poses of the intermediates A-I2 , B-I2 , and C-I2 . The pose of the correct intermediate C-I2 ( lime; RMSD 0 . 42 Å ) is more similar to that of lanosterol in the crystal structure ( grey ) than the poses of A-I2 and B-I2 ( RMSD 0 . 63 Å and 0 . 86 Å ) , which differ from the crystal structure in the orientation of the 6 , 6 , 6 , 5-tetracyclic core ( Figure 7b ) . Interestingly , C-I8 , which can form the product cycloartenol ( EC 5 . 4 . 99 . 8 ) , is a non-binder , suggesting that the reaction will terminate at C-I6 or C-I7 , both of which are precursors of lanosterol ( C-I7 can also form other products such as parkeol and cycloartenol ) . These results suggest that the intermediates after C-I8 ( e . g . C-I9 , which is the product precursor of cucurbitadienol; EC 5 . 4 . 99 . 33 ) will be unlikely to occur . Hence , the docking results for 1W6K suggest that the carbocation docking approach could make qualitative , but meaningful , predictions concerning the end point of a reaction channel in some favorable cases . That is , the inability of a given binding site to significantly stabilize certain intermediates can , at a minimum , rule out downstream products . We explore this concept further below , using homology models to create a much larger test set . We further tested our approach by docking carbocationic intermediates against homology models of 54 triterpenoid synthases with annotations in Swiss-Prot ( human-curated annotations ) . We exclude from consideration one triterpenoid synthase-like enzyme with a reported preference for a C35 substrate , both because it is not a triterpenoid synthase , and because it cannot be modeled reliably ( only 25% sequence identity to 1SQC ) . Guided by the results from docking carbocationic intermediates against the two available crystal structures , we use the docking scores for intermediates I1 and I2 to predict the reaction channel ( see Methods for details ) . The overall success rate for reaction channel prediction of these sequences is 80% ( Table 1 ) . Details for each test case , including sequence alignments and docking scores , can be found in Table S1 , S2 and S3 . Three of the test cases are close homologs of 1W6K ( 88% sequence identity ) , and unsurprisingly , these are correctly predicted to follow Channel C , as does 1W6K . The remaining test cases have sequence identity to either 1SQC or 1W6K ranging between 33–49% , and thus are much more challenging . All 4 of the test cases in the 1SQC cluster were correctly predicted . Of these , 3 of 4 are squalene-hopene cyclases , i . e . , the same function as 1SQC , upon which the homology models are based . However , the remaining case is correctly predicted to follow channel B ( dammara-20 , 24-diene synthase ) . Note that sequence identity alone does not distinguish these cases; the dammara-20 , 24-diene synthase actually has slightly higher sequence identity to 1SQC than the hopene synthases . Fifty of the test cases were in the 1W6K cluster , and thus their homology models were based on this structure ( lanosterol synthase , channel C ) . The products of these enzymes correspond to a mix of channel B ( 27 cases ) and channel C ( 23 cases ) . The overall accuracy of channel prediction is 78%; nine of the 11 incorrect predictions are based on homology models with 40% or lower sequence identity to 1W6K . Reaction channel prediction for 21 out of 23 triterpenoid synthases in the 1W6K cluster that follow Channel C are successful ( Table S1d ) . For these 21 triterpenoid synthases , we further docked the downstream intermediates ( Table S2 , Figure 8 and Figure 9 ) . The binding energy profiles , on average , follow a characteristic pattern where the docking scores are highly favorable for I1 in all cases , and much less so for I2 , followed by gradually more favorable scores , on average , from I3 to I9 . It should be kept in mind that these scores do not , at present , take into account the intrinsic ( gas phase ) relative energies of the carbocations ( I2 being more stable than I1 , for example ) . Nonetheless , the profiles for enzymes that generate different products show qualitative differences that correlate well in most cases with the product specificity . For the triterpenoid synthases that produce lanosterol , the most favorable docking score ( other than for I1 ) in 6 of 7 cases is either C-I6 or C-I7 , both of which are product precursors for lanosterol ( Figure 8 and Figure 9a ) . Moreover , in all cases , one or more of the intermediates subsequent to the intermediate with the most favorable docking score cannot be docked successfully into the binding site . Similarly , for the triterpenoid synthases that produce cycloartenol , 7 out of 10 models predict precursors C-I7 or C-I8 to have the most favorable docking scores ( Figure 8 and Figure 9b ) . However , in 3 cases , C-I9 is predicted to have the most favorable docking score , and in 2 of these cases , there is no energy increase at C-I8 . Thus , even in our very simple qualitative interpretation of these results , we consider these cases to be failures . The remaining 4 cases—enzymes that produce cucurbitadienol , parkeol , and protostadienol—are more ambiguous . One of the two protostadienol cases shows a strikingly different profile that is broadly consistent with being unable to proceed beyond C-I2 or C-I3 , while the other case does not ( Figure 9c ) . Overall , we conclude that carbocationic intermediate docking against homology models may be useful to make qualitative predictions concerning product specificity , but further improvements to the methodology are likely needed to provide robust predictions . Beyond enzyme function prediction , the current approach may have two other potential applications: 1 ) guiding mutagenesis experiments to alter the product specificity of an enzyme; and 2 ) exploring the catalytic mechanisms of enzymes . Although high-level quantum mechanical calculations are no doubt needed to make quantitative predictions , we illustrate here how the much simpler qualitative predictions from carbocation docking can nonetheless provide useful insights . Specifically , we examine 3 mutants of 1SQC . The experimental data for these mutants were obtained from an earlier study [72] , and our docking results are summarized in Table 2 . The Y609C , Y609L and Y609S mutants generate aborted product A-P1 as the major product , and minor amounts of A-P2 and A-P4 ( Table 2 ) . The much lower yield of product A-P4 for the Y609X mutants suggests that the reaction channel leading to A-I4 is affected by Y609X mutations . We thus compared the MM/GBSA scores of intermediates of the Y609X mutants to those of wild type . As with all of the docking results , the scores should be interpreted qualitatively . In this case , the scores of A-I1 , A-I2 and A-I4 do not vary significantly between wild-type and the mutants , while A-I3 becomes a much weaker binder for all three Y609X mutants . A comparison of the docking poses of A-I3 in the wild-type and the Y609C mutant Figure S6 also suggest that the Y609X mutants affect the binding of A-I3 . We interpret these results as follows ( Figure 10 ) . In a previous QM/MM study [39] , the barrier height from A-I2 to A-I4 was computed to be 27 . 8 kcal/mol , while for the A-I3 like transition state that directly links A-I1 and A-I4 , the barrier height was only 9 . 1 kcal/mol . Thus , for wild type , most A-I4 is likely generated through A-I3 . In the mutants , binding of A-I3 is greatly destabilized , and we speculate that formation of A-I4 proceeds , much more slowly , through A-I2 , and product formation from A-I1 and A-I2 competes with conversion to A-I4 . Hence , our mechanistic findings from docking calculations are qualitatively consistent with the QM/MM results that the direct conversion from A-I1 to A-I4 is the major productive channel for 1SQC . The docking results are not accurate enough , however , to make any quantitative predictions concerning product distributions . We also considered the L607K mutation of 1SQC , which generates gamma-polypodatetraene as the major product , presumably from A-I1 . Consistent with this observation , only the A-I1 intermediate could be docked successfully . This appears to result from the strong repulsion between the positive charge on K607 and the carbocation on A-I2 , A-I3 and A-I4 .
Although the results obtained with the current methodology are more qualitative when compared to more rigorous methods such as QM/MM , the major advantage of docking carbocationic intermediates is its computational efficiency , which enables its application to large numbers of protein structures or models ( over 50 in this proof-of-concept study ) . In the foreseeable future , these calculations will not replace experiments in providing reliable assignments of function , but as with other computational prediction methods , they can motivate experiments , or help to interpret the results . As in our prior work on enzyme function prediction , we anticipate that one of the most important uses will be identifying cases that are interesting or unusual , and thus high priorities for time- and resource-intensive in vitro or in vivo experiments ( e . g . , cyclases predicted to have novel specificity , or cases of convergent evolution ) . Docking studies with carbocationic intermediates may also complement more accurate , but computationally intensive , QM/MM methods . For example , in cases where the reaction mechanism is poorly understood , the docking results may suggest plausible pathways that can be further explored by quantum mechanical methods ( or perhaps more importantly , reject implausible pathways ) . Similarly , docking of carbocationic intermediates can be used to evaluate large numbers of possible mutations to identify ones more likely to modify product specificity in a desired manner . We are aware of limitations of the current approach: 1 ) our carbocation library currently only considers the naturally occurring reaction channels , which cannot cover the complete chemical space of possible carbocationic rearrangements; 2 ) as our calculations are based on classical molecular mechanics and docking , the common limitations of MM and docking exist in all our calculations , e . g . the atomic charges are not polarizable ( although we have used the QM-derived atomic charges ) ; 3 ) other limitations such as neglecting the dynamics of the enzymes and the role of waters bound in the active site , which may also affect the final results; 4 ) the final deprotonation or hydration steps are not modeled . For the first limitation , we are developing an algorithm that can automatically generate all possible reaction channels , which will be published in due course . However , from our preliminary results , such efforts will dramatically increase the computational cost , due to the much larger size of the carbocation library .
The sequence set of triterpenoid synthases were downloaded ( October 2013 ) from Structure-Function Linkage Database [73] through the link http://sfld . rbvi . ucsf . edu/django/subgroup/1016/ . The procedure for generating sequence similarity networks for these sequences follows our previous work [50] . Briefly , all pairwise BLAST E-values [69] were computed , and the sequence similarity networks were then generated by using Pythoscape [74] . A “quartile plot” is used to relate the average sequence similarity to the BLAST E-values ( Figure S2 ) . Cytoscape [75] is used for the visualization of the sequence similarity networks . In this visual representation , nodes represent sequences , and edges correspond to BLAST E-values that are smaller than a specified cutoff . Crystal structures of triterpenoid synthases ( PDB codes 1SQC [9] , [10] and 1W6K [15] ) were downloaded from the RCSB Protein Data Bank and processed using Schrödinger Protein Preparation Wizard [76] , followed by restrained energy minimizations ( RMSD tolerance 0 . 35 Å , in the presence of the co-crystallized ligand ) . All crystal water molecules were removed after the minimizations . Homology modeling procedures are similar to our previous work on the polyprenyl transferases [50] . Query sequences were aligned to the templates ( 1SQC or 1W6K , depending on sequence similarity ) using PROMALS3D [77] , and models were created by Schrödinger Prime [76] , [78] , [79] . In brief , the homology modeling procedure closes chain breaks associated with gaps in the sequence alignment by iterative application of the PLOP loop prediction algorithm , followed by side chain optimization ( for all residues that are not identical between target and template in the sequence alignment ) , and complete energy minimization on all portions of the protein whose coordinates were either not taken from the template at all , or were modified during the model building procedure . All the homology models are then processed by using constrained minimizations ( RMSD tolerance 0 . 35 Å , in the presence of the co-crystallized ligands ) with Schrödinger Protein Preparation Wizard . The quality of the homology models is assessed by using the discrete optimized protein energy score ( a statistical potential score for evaluating protein models ) in MODELLER ( Table S4 ) [80] . The OPLS 2005 force field [81] , [82] was used throughout this study . The carbocationic intermediates were manually created and atomic charges were assigned using Jaguar [76] , [83] quantum mechanical calculations ( HF/6-31G*; geometry optimization in gas phase; electrostatic potential fitting ) . The carbocation library used in the current work is online available through the link www . jacobsonlab . org/carbocation/triterpene_docking_ligands . tar . gz ( in ‘mol2’ format ) . The Schrödinger induced fit docking ( IFD ) protocol [84] , [85] is used for all the docking calculations , with small modifications of default procedures and parameters . The IFD protocol consists of three stages: 1 ) Schrödinger Glide docking [86]–[89] with a reduced van der Waals scaling factor ( 0 . 5 for both receptor and ligand; top 5 poses are retained for the following steps ) ; 2 ) minimization of the ligand as well as a conserved set of active site residues within 5 Å of the ligands defined by crystal structures ( using the ‘RESIDUES_TO_ADD’ option of IFD; Table 3 ) ; 3 ) computation of MM/GBSA [78] , [79] docking scores . To ensure the ligands are docked into the correct position , we applied constraints and core restraints during the initial Glide docking stage , which are essential for maintaining consistent poses of the carbocationic intermediates along the same reaction channel . For example , in the 1W6K crystal structure , we add a hydrogen bond constraint between the ligand and the key aspartate that protonates the oxido-squalene ( D455 for 1W6K; c . f . Figure 11 ) . In addition , we use a Glide core restraint ( Figure 11 in red , 13 atoms , defined by ‘SMARTS’ pattern , i . e . “[#1][C-0X4] ( [#1] ) ( [#1] ) [C-0X4] ( [C-0X4] ( [#1] ) ( [#1] ) [#1] ) [C-0X4] ( [#1] ) ( [C-0X4] ( [#1] ) [#1] ) [O-0X2]”; 1 . 0 Å RMSD tolerance ) to ensure that all the docked poses have the same orientation as the lanosterol ligand in the crystal structure ( Figure 11 ) . We also changed the Coulomb and van der Waals cutoff parameter during initial docking to a large positive number ( ‘CV_CUTOFF’ = 999999999 . 9 vs default 0 . 0 ) , to retain more poses for the next stage . Both the IFD and MM/GBSA steps use ligand partial charges derived from quantum mechanics , as described above , for all energy calculations and minimizations . MM/GBSA , which is a force field-based scoring function ( as opposed to empirical/knowledge-based scoring functions commonly used in docking ) , is used to accommodate the unusual carbocations studied in this work . That is , empirical or knowledge-based scoring functions will not have been trained on carbocation intermediates . To ensure maximal consistence between the binding modes of I1 and I2 , we first dock I2 , and then copy the coordinates of I2 to I1 , followed by energy minimization . We then check the key dihedral angle Φ[C16-C17-C18-H18] ( shown in Figure 5 ) of all the poses to ensure that the dihedral angles are consistent with those before energy minimization ( Φ[C16-C17-C18-H18]>0 for A-I1 , and Φ[C16-C17-C18-H18]<0 for B-I1 and C-I1 ) . A hierarchical ranking strategy is used to rank different reaction channels and carbocationic intermediates ( Figure 12 ) . Figure 12 shows a hypothetical relative binding affinity ( MM/GBSA score ) profile obtained from carbocation docking along three different reaction channels . In Figure 12 , the x-axis is a reaction coordinate ( e . g . the conversion SubstrateA→A1→A2→A3→ProductA in Channel A ) , and the y-axis is the docking score . A1 , B1 , C1 , A2 , B2 and C2 are the first and second representative intermediates of reaction channels A , B and C , respectively . In this hypothetical example , the binding affinities of A1 and B1 are similar ( <1 kcal/mol ) , and both are higher than that of C1; thus , the channel ranking in the first round is A = B>C . As for second representative intermediates , the docking score of A2 is more favorable than that of B2 , and thus the final channel ranking is A>B>C . After the second representative intermediates , we are able to select the best reaction channel . All the intermediates along the best channel are then ranked by MM/GBSA ( without considering further branching points ) .
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The rapid growth in the number of protein sequences presents challenges for enzyme function assignment . Computational methods , such as bioinformatics , homology modeling and docking , are becoming increasingly important for predicting of enzyme functions from protein sequences . Terpenoids are one of largest classes of natural products , and many drugs ( e . g . taxol ) consist of terpenoids or terpenoid derivatives . Understanding the biosynthesis of the terpenoids is of great interest . Terpenoid synthases catalyze the key cyclization steps of the biosynthesis of terpenoids via carbocation rearrangements , generating numerous multiple-ring carbon skeletons . Triterpenoid synthases , as an important class of terpenoid synthases , catalyze the cyclization of either squalene or oxido-squalene into cyclized products such as sterols ( e . g . lanosterol ) . In this work , we propose a computational approach that can be used to predict product specificity of the triterpenoid synthases . Our approach provides insight into the ‘design principles’ of these fascinating enzymes , and may become a practical approach for function prediction and enzyme engineering .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"biochemistry",
"simulation",
"and",
"modeling",
"biology",
"and",
"life",
"sciences",
"enzymology",
"enzyme",
"chemistry",
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2014
|
Predicting the Functions and Specificity of Triterpenoid Synthases: A Mechanism-Based Multi-intermediate Docking Approach
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Kaposi sarcoma-associated herpesvirus ( KSHV ) has been linked to Kaposi sarcoma and B-cell malignancies . Mechanisms of KSHV-induced oncogenesis remain elusive , however , in part due to lack of reliable in vivo models . Recently , we showed that transgenic mice expressing the KSHV latent genes , including all viral microRNAs , developed splenic B cell hyperplasia with 100% penetrance , but only a fraction converted to B cell lymphomas , suggesting that cooperative oncogenic events were missing . Myc was chosen as a possible candidate , because Myc is deregulated in many B cell lymphomas . We crossed KSHV latency locus transgenic ( latency ) mice to Cα Myc transgenic ( Myc ) mice . By itself these Myc transgenic mice develop lymphomas only rarely . In the double transgenic mice ( Myc/latency ) we observed plasmacytosis , severe extramedullary hematopoiesis in spleen and liver , and increased proliferation of splenocytes . Myc/latency mice developed frank lymphoma at a higher rate than single transgenic latency or Myc mice . These data indicate that the KSHV latency locus cooperates with the deregulated Myc pathways to further lymphoma progression .
Myc encodes a multifunctional protein which is involved in many biological functions , including transcriptional control , cell cycle , signal transduction , oncogenesis , and development ( reviewed in [1] ) . Recurrent deregulation of c-Myc ( Myc ) is a hallmark of many lymphoma such as Burkitt lymphoma ( BL ) and a fraction ( ~20% ) of diffuse large B cell lymphomas ( DLBCL ) , including post-germinal center ( GC ) , non-Hodgkin’s lymphoma [1 , 2] . The most frequent chromosomal translocation is t ( 8;14 ) ( q24;q32 ) found in BL , which relocates Myc from 8q24 to the immunoglobulin heavy chain ( IgH ) locus on 14q32 . Some cases of DLBCL , such as anaplastic lymphoma kinase ( ALK ) positive large B-cell lymphoma do not carry Myc translocation per se , but overexpress Myc protein [3 , 4] . This suggests that deregulated expression of the Myc protein by any means contributes to B cell lymphomagenesis . Over the years , multiple mouse models of Myc-driven lymphomas have been developed [5–12] . The first and most aggressive transgenic model used the mouse Myc gene , driven by the IgH μ enhancer ( EμMyc mouse ) ; here the transgene induced tumors , expansion of lymph nodes , and lymphoid malignancy within 6–15 weeks [5] . Transgenic mice expressing a translocated Myc gene from a human BL cell line under the Igλ light chain regulatory sequences also readily developed lymphomas [8] , whereas transgenic mice with a specific , single copy targeted insertion into the Cα of the IgH locus ( iMycCα mouse ) , which mimic the t ( 8;14 ) in BL , developed B cell lymphomas with very low incidence [7] . In sum , the phenotypes of Myc mouse models range from moderate to fully penetrant , aggressive lymphomagenesis depending on the particulars of the transgene regulatory context , each mimicking different types and/or stages of lymphomagenesis . Using these mouse models , many factors were uncovered that cooperate with Myc . Targeted overexpression of N-ras in B cells promoted B cell neoplasia in conjunction with Myc [13] . There is also evidence for cooperation of interleukin-6 ( IL-6 ) with Myc in plasma cell tumor development [14] . Furthermore , B cell receptor ( BCR ) activation was shown to promote B cell lymphomagenesis in conjunction with Myc [15]; and using a CD19 knockout mouse model , the CD19 signaling loop was revealed to promote development and progression of B cell lymphoma [16] . CD19 is an essential accessory to the BCR signaling leading to phosphoinositide-3-kinase ( PI3K ) activation [17] . Myc itself was shown to synergize with PI3K signaling to provoke BL [18] . Kaposi sarcoma-associated herpesvirus ( KSHV ) is an oncogenic human γ-herpesvirus . KSHV is implicated in the pathogenesis of Kaposi sarcoma , primary effusion lymphoma ( PEL ) , multicentric Castleman’s disease ( MCD ) , and some instances of DLBCL ( reviewed in [19] ) . Whereas MCD is a pre-malignant , relapsing-remitting-type GC hyperplasia , PEL is a highly aggressive post-GC DLBCL . An association between KSHV and microlymphoma has been suggested as well [20 , 21] . Typically KSHV persists in the B cell compartment for many years prior to overtly symptomatic MCD or lymphoma . Latency is the default replicative pathway of KSHV in B cells ( reviewed in [22] ) . Only very few of the more than 80 viral genes are expressed [23 , 24] . Those , which are consistently detectable in every single infected B cell , include the latency-associated nuclear antigen ( LANA ) , a viral homolog of cellular cyclin D2 ( vCYC ) , a viral FLICE inhibitory protein ( vFLIP ) , K12 ( kaposin ) , all viral micro RNAs ( miRNAs ) , and v-IRF3/LANA-2 [24 , 25] . Many of these genes have been implicated in B cell signaling in tissue culture , but only few have been explored in vivo . This represents a gap in our understanding and a barrier towards pre-clinical testing of targeted anti-KSHV lymphoma agents . Expression of LANA alone in B cells resulted in hyperplasia , low-penetrance lymphoma , and drastically increased BCR responses to a T cell-dependent ( TD ) antigen . Analogous to the transgenic Myc models , this phenotype was dependent on CD19 [26–28] . Mice expressing the entire KSHV latency locus , including all viral miRNAs , in a pure C57BL/6 background exhibited even more increased BCR responses to TD antigen , and also displayed marginal zone ( MZ ) enlargement , as well as plasmacytosis and frank lymphoma [29] . Whereas these KSHV latency mice exhibited GC and MZ hyperactivity akin to MCD with 100% penetrance and at a normal age , long latency was needed for lymphoma development with incomplete penetrance . This suggested that additional , cellular driver events would accelerate lymphomagenesis . Recent studies suggested that Myc is frequently deregulated by KSHV latent proteins such as LANA and vIRF3 [30–32] . Though structural abnormalities involving Myc translocations are not seen in PEL [30 , 33] , this does not mean that Myc couldn’t be activated at the transcriptional and post-transcriptional level either by viral or cellular events . To test the hypothesis that Myc was one of the host factors , which can augment KSHV-driven B cell lymphomagenesis , we utilized transgenic mice carrying the very weak IgH Cα Myc allele . As mentioned above this particular Myc allele on its own induced hyperplasia , but not lymphoma [7] . We found that the KSHV latent genes synergized with Myc to drive lymphoma in vivo .
We had previously reported the KSHV latency locus transgenic mouse line , which expresses the KSHV latent genes and all miRNAs in B cells , albeit at low levels [29] . In the latency mice , the MZ and plasma cell frequencies were increased and frank tumors developed ( ~16% / 300 days ) [29] . We chose Myc transgenic mice , where a Histidine-tagged Myc coding region was inserted into IgH Cα locus under its own promoter and Eα enhancer to mimic the Myc-activating chromosomal translocation t ( 12;15 ) . T ( 12;15 ) defines 90% of plasma cell tumors found in pristane-treated BALB/c mice [7 , 34] . These plasmacytomas develop as liquid ascites in the body cavities of the animal and represent a phenotype of mouse lymphoma closely resembling human PEL . However , the tumor incidence rate of the iMycCα single transgenic mice was low and lymphoma developed only after a long latency period ( ~9%/ 300 days ) [7] . This made them ideal to uncover synergy between host Myc and KSHV latent genes . To study the cooperative interaction between the KSHV latency locus and Myc in viral lymphomagenesis , the latency mice were crossed to iMycCα mice to generate a double-transgenic mouse line , which expresses the KSHV latency locus in the context of activated Myc , termed Myc/latency . Genotyping for the KSHV transgene was done as previously described [29] . The presence of the Myc transgene was confirmed by allele specific PCR ( S1 Fig ) . We confirmed that the KSHV miRNAs and mRNAs of KSHV latent genes were expressed in the presence of the Myc transgene similarly as in the latency mice line ( S2 Fig; see also reference [29] ) . The KSHV latency locus alone induced plasmacytosis [29] , and this phenotype was maintained in the compound Myc/latency mice , though other phenotypes of original latency mice , such as increased frequency of mature and MZ B cells , were not recapitulated in the Myc/latency mice ( S1 Table ) . Plasmablasts ( PBs; CD19-B220+CD138+ ) and plasma cells ( PCs; CD19-B220-CD138+ ) were increased in the spleens of Myc/latency mice compared to Myc mice ( Fig 1A and 1B ) . This increase was statistically significant to p ≤ 0 . 03 by ANOVA ( Fig 1E ) . The increased numbers of PCs were confirmed in situ using Igγ chain immunohistochemistry . The intensity and prevalence of the staining was more robust in spleen sections of Myc/latency mice compared to those of Myc single transgenic mice ( Fig 1G–1M ) . This phenotype was consistently seen in all mice ( S3 Fig ) . Next , the frequencies of PBs and PCs in Myc/latency mice were compared to those of the latency mice . PBs were increased significantly in the Myc/latency mice compared to the latency mice ( p ≤ 0 . 001 by ANOVA ) , while PCs were not augmented obviously ( p ≤ 0 . 08 by ANOVA , Fig 1F ) . The direct comparison of splenic PBs and PCs from the latency , Myc , and Myc/latency mice strongly suggests that the additive effect of KSHV latency locus and Myc overexpression induces higher PC and PB frequencies ( Fig 1F ) . These data demonstrate that increased frequency of PCs in the Myc/latency mice is not a single effect of the KSHV latency locus , but the result of cooperation between the KSHV transgene and the Myc transgene . Thus , activated Myc may cooperate with KSHV latent genes to drive plasma cell proliferation/activation . A similarly increased frequency of PBs was not observed in bone marrow ( BM ) ( Fig 1C and 1D ) rather , PCs in BM of the Myc/latency mice was considerably decreased compared to that of Myc mice ( Fig 1E; p ≤ 0 . 002 by ANOVA ) . This suggests that the KSHV latency locus induces PBs , short-lived PCs , and some long-lived PCs in GC of the spleen , but interferes with homing of long-lived PCs and migratory PBs to BM in the presence of deregulated Myc . We observed elevated peripheral blood IgG1 levels , while IgA and IgG3 levels were decreased in the Myc/latency , compared to the Myc mice ( Fig 1N ) . The elevation was consistent and pronounced enough in the absence of any specific antigenic stimuli to be diagnosed as hyperglobulinemia . KSHV latency transgenic mice alone also displayed hyperglobulinemia of IgG1 , IgG3 , and IgM [24] , while no significant difference in Ig levels has been reported for Myc mice compared to wild-type mice [7] . As before [29] , the phenotype of the KSHV latency locus manifested itself in the context of forced Myc expression . Peanut agglutinin ( PNA ) is a known activation marker for the GC [26 , 29] . Enlarged PNA-positive patches in the GC of spleen is a phenotype of the KSHV latency locus [29] , but not of this particular strain of Myc transgenic mice . PNA-positive foci were significantly larger in the Myc/latency double transgenic mice than those of either the latency or Myc mice ( Fig 2A–2C ) . The area of PNA-positive foci in spleen was larger in the Myc/latency than in the Myc mice ( Fig 2J ) . This data evidences KSHV-Myc cooperation in GC expansion . The increased proliferative phenotype was confirmed using another clinically validated marker , Ki-67 . The Myc/latency mice exhibited striking reactivity for Ki-67 in the red pulp region of spleen , where extramedullary hematopoiesis occurs in rodents . By contrast , the KSHV latency single transgenic and Myc single transgenic mice showed weak expression of Ki-67 ( Fig 2D–2I ) . The difference in Ki-67 staining was significant to p ≤0 . 0004 by ANOVA ( Fig 2K ) . The higher degree of Ki-67 positivity was verified in all mice per genotype ( S4 Fig ) . One hypothesis to explain how viral infection can facilitate B cell hyperplasia and lymphoma , is that the viral latent genes render infected B cells hyperresponsive to BCR and Toll-like receptor ( TLR ) signaling . We showed earlier that purified B cells from KSHV latency mice respond better to lipopolysaccharide ( LPS ) , anti-IgM , and anti-CD40 [29] . As a polyvalent antigen , LPS activates both TLR and BCR signaling [35] . To test the hypothesis that the KSHV latency locus conferred a similar hyperresponsiveness in the Myc background , ex vivo proliferation of splenic B cells was assessed . Splenic CD19+ cells from the Myc/latency mice showed dose-dependent hyperresponsiveness to LPS , but no longer to anti-IgM or anti-CD40 or a TLR7 agonist , loxoribine or a TLR9 agonist , CpG-containing oligonucleotides ( Fig 3 ) . In the case of the LPS response , the difference between Myc and Myc/latency was significant to p ≤ 0 . 05 by ANOVA . The presence of the KSHV transgene increased the response to LPS . The presence of the KSHV transgene dampened the response to BCR crosslinking by anti-IgM antibody . This suggests that the KSHV latency locus augments TLR but not BCR-only or CD40L-only signaling pathways in the context of activated Myc . The most stringent test for the presence of fully transformed B cells is the ex-vivo outgrowth assay in the absence of supportive growth factors . To examine the outgrowth potential of the Myc/latency mice , primary cells from spleen or BM in 9–11 week-old Myc ( n = 6 ) or Myc/latency ( n = 6 ) mice were seeded on methylcellulose media without B cell growth factors , and the number of colonies was counted . With the exception of one animal , splenocytes of the Myc/latency or the Myc mice did not produce colonies ( S2 Table ) , though we routinely observed colonies from BM derived cells which were not significantly different between both genotypes ( 32 . 3 ± 14 . 3 for 6 Myc mice , 38 . 3 ± 9 . 0 for 6 Myc/latency mice; p ≤ 0 . 41 ) . To formally test the hypothesis that Myc and KSHV latent genes cooperate to induce lymphoid hyperplasia and neoplasia , Myc transgenic ( n = 42 ) , the KSHV latency locus transgenic ( n = 41 ) , and Myc/KSHV latency locus double transgenic mice ( n = 40 ) were monitored for 500 days ( Fig 4A and 4B ) . Wild-type B6 mice were tumor-free for 500 days . Single transgenic Myc mice remained tumor-free until 200 days , while both latency and Myc/latency mice started to develop tumors around 130 days . The overall survival rate was significantly lower in the Myc/latency mice , when compared to that of Myc mice ( p ≤ 0 . 021 by log-rank test ) ( Fig 4B ) . Given the weak tumor phenotypes of these particular Myc transgenic mice [7] , we surmise that the increased rate of tumor incidence is attributable to cooperation of KSHV latent genes and Myc . Pathological evaluation was performed on all mice . 11 ( 27 . 5% ) and 20 ( 50 . 0% ) out of 40 Myc/latency mice developed frank lymphoma and lymphoid hyperplasia in the spleen , respectively ( Table 1 ) . The lymphoma incidence rate of Myc/latency mice was marginally higher than that of the single KSHV transgenic mice , which was 17 . 1% , but significantly higher than in the Myc mice ( 4 . 8% , p ≤ 0 . 016 by F-test ) . Lymphoid hyperplasia can progress to frank lymphoma [36–38] . The combined rate for incidence of lymphoma and lymphoid hyperplasia was higher in Myc/latency mice ( 77 . 5% ) than latency ( 69 . 1% ) or Myc mice ( 43 . 9% ) . Examples of severe splenomegaly are shown in Fig 4C and an example of pathology for Myc/latency mice compared to normal spleen architecture in Fig 4D–4G . Mitotic figures were found in spleen from a mouse diagnosed as lymphocytic lymphoma ( Myc/latency ) , while none were found in spleen diagnosed as lymphoid hyperplasia ( Myc ) ( Fig 4E and 4G ) . The number of mitotic figures was significantly higher in the Myc/latency than the Myc mice ( p ≤ 0 . 002 by ANOVA ) ( Fig 4H ) . Lymphoma observed in the Myc/latency mouse cohort is summarized in Table 2 . Mice with early lymphoma or lymphocytic lymphoma exhibited disrupted splenic architecture and white pulp expanded by large lymphocytes with frequent mitotic figures , whereas mice diagnosed as normal had regular splenic architectures with clearly defined GCs . Mice with lymphoma also displayed severe extramedullary hematopoiesis , showing augmented frequency of megakaryocytes in spleen ( Fig 5A and 5B ) and elevated numbers of erythroid precursors in portal area of liver ( Fig 5C and 5D ) . BM was examined to see if a failure in hematopoiesis from the Myc/latency mice may induce severe extramedullary hematopoiesis ( EMH ) in the spleen and liver for compensation ( Fig 5E and 5F ) . Frequencies of myeloid and erythroid precursors were not significantly different between the Myc and the Myc/latency mice . However , the number of megakaryocytes was decreased in the Myc/latency mice ( Fig 5G; p ≤ 0 . 017 by ANOVA ) , suggesting that inadequate hematopoiesis in BM from the Myc/latency mouse drives severe EMH in the spleen and liver . Mice diagnosed with lymphoid hyperplasia retained normal splenic follicular architecture , but lacked discernible GCs with pale MZ ( Table 2; mouse #176 ) . In sum , even the weak Cα Myc allele can cooperate with the KSHV latent locus to foster lymphoma development in vivo .
Chromosomal translocation of Myc has been identified as the defining cellular driver mutation in BL [1] . Deregulated Myc activity is seen in the majority of DLBCL , though in PEL the myc locus appears to be normal [33 , 39] . Previous studies identified a number of chromosomal locations that were reproducibly amplified in PEL , such as FHIT and WWOX [40 , 41]; as well as activation of the BCR/PI3K and TLR/MyD88/IRAK signaling pathways [40 , 42 , 43] . Based on the biology of B cell lymphoma and the broad transcriptional phenotype of activated Myc , we hypothesized that deregulated Myc signaling can cooperate with BCR/TLR activation and KSHV latent genes to drive lymphomagenesis . These experiments are not trivial , since the most penetrant Myc single-transgenic mice already exhibit a strong tendency towards multiple types of lymphoma . This made it difficult to detect cooperation of moderate human oncogenes . The one exception is BCL-2 , which dramatically accelerates tumor development in the context of the Eμmyc mice [44] . BCL-2 is a broad-spectrum apoptosis suppressor , which counteracts the apoptosis signaling that emerges from many oncogenes , including heavily overexpressed Myc ( reviewed in [1] ) . Myc is known to induce apoptosis by repressing the activity of Bcl-XL , an anti-apoptotic factor of BCL-2 family; mice expressing Myc and Bcl-XL developed plasma cell tumors with a higher incidence rate and shorter onset time than single transgenic Myc mice [7] . By contrast to BCL-2 , the KSHV latency locus seems to modulate B cell development more modestly with the aim of fine-tuning signals from exogenous antigens . Fine-tuning is the general modus operandi of miRNAs , including of viral miRNAs . The KSHV latency mice showed dramatically increased plasma cell frequency and an increased propensity to respond to TLR4 stimulation in vivo and in vitro [29] . Here we show that these phenotypes were maintained in the context of activated Myc . These new data informed our working model that the role of latent viral infection , EBV in the case of endemic BL ( but not sporadic BL ) and KSHV in the case of PEL and MCD , is to ( i ) increase the overall number of hyper proliferative cells in response to low-level , polyvalent antigen , and ( ii ) perhaps modulate their B cell fate towards immediate responder MZ B cells . This would provide a fertile “soil” or cellular environment into which additional host events , such as Myc pathway activation , can develop their fully transforming potential [45] . Expression of the KSHV latency genes in the context of activated Myc resulted in drastic plasmacytosis in the double transgenic mice . The PBs and PCs from spleen and BM in the latency mice were increased , while only splenic PBs were increased and PC development was dampened in the Myc mice [7 , 29 , 46] . The expansion of both PBs and PCs in the Myc/latency mice suggests that most of the PBs survived and differentiated terminally into PCs in spleen . However , the frequency of PCs was decreased in the BM , consistent with the idea that the some PCs failed to home to the BM after leaving the spleen or failed to survive in the BM . It is conceivable that the KSHV latency locus promotes the development of PBs into short-lived PCs in spleen , but not survival and/or homing of long-lived PCs to the BM . Understanding this aspect of KSHV biology is subject to further study . Myc/latency compound transgenic mice developed lymphoma around 130 days with an incidence rate of 28% . In our colony , the iMycCα single-transgenic mice developed neoplasms at ~200 days with an incidence rate of 5% , which was slightly lower , but within the margin of error , than that observed in the original report ( 9% ) [7] . KSHV latency single transgenic mice started to develop neoplasms at ~200 days , and the incidence rate was 17% , which was similar to our initial cohort ( 16% ) [29] . This provides genetic evidence that the KSHV latency locus cooperates with Myc to drive B cell lymphoma . The mechanism by which the KSHV latency locus cooperates with Myc to promote human PEL is not well understood . Structural deregulation of Myc is not common in PEL; rather various KSHV latent proteins have been proposed to deregulate Myc . Post-translational mechanisms typically lead to a lesser degree of oncogene activation than genomic translocation . LANA activates and stabilizes Myc in certain culture systems [31 , 39] . Myc also seems to be required for the maintenance of KSHV latency [47] . In cultured cells , mLANA , the murid herpesvirus-4 ortholog of KSHV LANA , stabilizes Myc through heterotypic polyubiquitination [48] . The KSHV vIRF-3/LANA2 also stimulates the transcription of Myc [32 , 49] . vFLIP cooperates with Eμ-driven Myc to promote lymphoma in double transgenic mice [50] . It also upregulates Myc , leading to protection of anti-IgM-induced apoptosis in the mouse indicator cell line , WEHI-231 [51] . The in vivo experiments reported here support these prior observations . Recent data suggest that the vFLIP protein is only very inefficiently expressed in natural infection of B cells [52] , suggesting that even minute amounts of viral proteins have potent biological phenotypes . The current in vivo experiments reaffirm the ability of the KSHV latency locus to confer hyperresponsiveness to naïve B cells , which in the presence of elevated Myc activity leads to lymphoma . Most likely , KSHV latent genes act on multiple checkpoints along the pathway: initially by enhancing receptor-initiated signaling , and downstream of Myc , by ameliorating Myc’s tendency to induce apoptosis . Rather than dying , the activated plasmablasts continued to proliferate in the KSHV latency/Myc double transgenic mice ( Fig 2 ) . One limitation of the current model is that it still lacks the contribution of the KSHV receptor homologs K1 and K15 . Analogous to the EBV LMP-1 and LMP-2 proteins , these are believed to have an important role in modulating B cell biology [53–58] . In fact , the phenotypes seen here with only the nuclear KSHV latent genes are somewhat similar to early experiments using EBV nuclear latent proteins . The EBV+ eBL shows extremely restricted viral gene expression , i . e . only the EBV EBNA1 protein and the EBV miRNAs are detectable [59 , 60] . These seem , nevertheless , necessary for eBL cell survival [61] . By itself the EBV EBNA1 gene exhibits only weak phenotypes in vivo . It is associated with no , low , or late hyperplasia and lymphoma incidence , if driven by the IgH Eμ enhancer in transgenic mice [62–64] . EBNA-1 and Myc cooperate in inducing lymphoma [65] . LANA is the homolog of EBNA-1; it alone has only a minor growth modulating effect; is associated with low and late lymphoma incidence in transgenic mice [26 , 28] . Perhaps the missing factor in the initial LANA and EBNA-1 transgenic experiments was the absence of the viral miRNAs , which motivated us to use the complete KSHV latency model rather than the LANA single transgenic mice for our studies . Taken together , this study reports that KSHV latency locus cooperates with Myc to promote lymphoma development in vivo . Compared to the low oncogenic potential of the iMycCα mice [7] , this elevated tumorigenicity of the Myc/latency mice demonstrates pivotal roles of KSHV latency genes in viral lymphomagenesis in vivo .
Transgenic mice which express the KSHV latency locus were previously described [29] . Myc transgenic mice [C . 129S1-Ighatm1 ( Myc ) Janz/J] were obtained from the Jackson Laboratory ( Bar Harbor , ME ) [7] . All mice were maintained under pathogen-free conditions using ventilated cages . All experiments were approved by the Institutional Animal Care and Use Committee ( IACUC ) at the University of North Carolina at Chapel Hill ( UNC ) . Genomic DNA was isolated from mouse tail clipping using a Wizard SV genomic DNA kit ( Promega ) . qPCR was performed for LANA and ApoB primers as previously described [28] . Mice with overexpressed Myc were typed by PCR according to supplier’s protocol using primers oIMR8447 & oIMR8448 for wild-type mice and oIMR8450 & oIMR8453 for the Myc mice ( http://jaxmice . jax . org/protocolsdb/f ? p=116:2:3011848657952163::NO:2:P2_MASTER_PROTOCOL_ID , P2_JRS_CODE:5234 , 008341 ) . Gross pathology evaluation and tissue extraction were done at the time of euthanization or death due to serious illness . Pathological diagnosis , including lymphoma and other types of malignancies , was done by a veterinary pathologist ( Y . Kim ) based on H&E staining and the morphological and histological aberrations observed in spleen , liver and bone marrow . Myeloid , erythroid precursors , and megakaryocytes were also evaluated on the all tissues . All pathological evaluation was performed using a microscope ( Nikon ECLIPSE Ci Y-TV55 , Japan ) . Images were captured using a camera ( Jenoptik ProgRes SpeedXT core 3 , Germany ) , and acquired using ProgRes CapturePro ( Version 2 . 8 , Jenoptik ) . The magnifications of the objective lenses were x2 or x10 or x40 . The following antibodies were used for flow cytometry and immunohistochemistry . Polyclonal anti-mouse CD3 ( Abcam ) ; Fluorescein isothiocyanate ( FITC ) -conjugated anti-mouse IgD ( clone 11-26c . 2a ) , phycoerythrin ( PE ) -conjugated anti-mouse CD138 ( clone 281–2 ) , anti-mouse CD21/CD35 ( clone 7G6 ) , and anti-mouse IgM ( clone R6-60 . 2 ) ( BD Biosciences ) ; biotin-conjugated anti-mouse ki-67 ( clone SP6; Fisher ) ; allophycocyanin ( APC ) -conjugated anti-mouse CD19 ( clone 6D5 ) , anti-mouse CD23 ( clone B3B4 ) , and FITC-conjugated anti-mouse CD45R ( clone RA3-6B2 ) ( Invitrogen ) ; goat polyclonal anti-mouse IgG , and biotinylated anti-mouse CD45R ( clone RA3-6B2 ) ( Southern Biotech ) ; biotinylated peanut agglutinin ( PNA ) ( Vector Laboratories ) . All tissues were extracted at the time of euthanization or death due to serious illness and were paraffin embedded and sectioned at the Animal Histopathology Core facility of UNC Lineberger Comprehensive Cancer Center ( LCCC ) . Sections were stained with H&E at the same facility . Immunohistochemistry was performed as previously described [29] . Formalin-fixed paraffin-embedded spleen sections were incubated with PNA ( 1:200 dilution ) , anti-mouse Ki-67 ( 1:200 dilution ) , or anti-mouse IgG ( 1:100 dilution ) . The area of PNA-positive foci was measured using ImageJ [66] . The staining was visualized using a microscope ( Leica DMLS , Germany ) with the magnifications of the objective lenses of x4 or x10 or x40 . Images were captured using a camera ( Leica DFC480 ) and acquired using FireCam ( Version 3 . 0 , Leica ) . Staining intensity and prevalence was evaluated as previously described [67] . Flow cytometry was performed as previously described [28] . Briefly , single cells were isolated from the spleen or bone marrow in 7–11 week-old Myc or Myc/latency mice . After red blood cell lysis , one million cells were subject to staining and flow analysis . Data acquisition was performed using a CyAn instrument ( Beckman Coulter ) at the UNC Flow core and the analysis was done using Flowjo Ver . 7 . 6 . 5 ( Tree Star ) . Splenic B cells were purified from 11–13 week-old Myc ( n = 5 ) or Myc/latency ( n = 5 ) mice using an EasySep Mouse B Cell Enrichment Kit ( StemCell Technologies ) . B cells were cultured in RPMI 1640 medium supplemented with 20% fetal bovine serum , 2 mM L-glutamine , penicillin ( 0 . 05 μg/mL ) , and streptomycin ( 5 U/mL ) ( Invitrogen ) with CD40 mAb ( clone HM40-3 , Biolegend ) , F ( ab′ ) 2 goat anti-mouse IgM Ab ( Jackson ImmnuoResearch Laboratory ) , LPS ( from Escherichia coli 0111:B4 ) , loxoribine , or Class B CpG oligonucleotide ( Invivogen ) at 37°C under 5% CO2 . The ex vivo cell proliferation rate was determined using a Click-iT EdU microplate assay kit ( Invitrogen ) according to the supplier’s protocol . The incorporated EdU in DNA was conjugated with Oregon Green-azide , and coupled with horseradish peroxidase-labeled anti-Oregon Green antibody . The relative fluorescence unit ( RFU ) at 485 nm excitation/585 nm emission was measured using a Fluostar Optima instrument ( BMG , Inc . ) , and expressed as the ex vivo proliferation rate of the B cells . Ten million splenic B cells from each mouse ( 7–11 weeks old ) were cultured on semisolid methylcellulose media ( M3134 from StemCell Technologies ) supplemented with 20% fetal bovine serum , 2 mM L-glutamine , penicillin ( 0 . 05 μg/mL ) , streptomycin ( 5 U/mL ) , 7 . 5% sodium bicarbonate , and 55 mM 2-mercaptoethanol ( all from Invitrogen ) . The number of colony-forming cells ( CFC ) was counted on 14 days after culture . One half million bone marrow ( BM ) cells from each mouse ( 7–11 weeks old ) were cultured on semisolid media ( M3630 from StemCell Technologies ) and the number of CFC was counted on 9 days after culture . Serum was collected from both the Myc and the Myc/latency mice ( 7–10 weeks old ) . Igs were measured as previously described [29] . Data in figures and text were represented as mean ± standard deviation . Continuous data were analyzed using ANOVA and adjusted for multiple comparisons by Dunnett method using R version 3 . 1 . 1 ( 2014-07-10 ) . Incidence data were analyzed using Student’s t-test or Fisher’s exact test . A p value ≤ 0 . 05 was regarded as significant . For box plots , the box represents the interquartile range and the line within the box represents the median . The lower limit of a lower vertical segment points 5% percentile and the upper limit of an upper vertical segment is 95% percentile . All animal work was approved by the IACUC committee of the University of North Carolina at Chapel Hill under #13–219 . 0/KSHV latency mice . All work has been conducted in accordance with the Public Health Service ( PHS ) policy on Humane Care and Use of Laboratory Animals , the Amended Animal Welfare Act of 1985 , and the regulations of the United States Department of Agriculture ( USDA ) .
|
Kaposi’s sarcoma-associated herpesvirus ( KSHV ) is associated with Kaposi sarcoma as well as the B-cell malignancies primary effusion lymphoma ( PEL ) and multicentric Castleman’s disease ( MCD ) . Only a few KSHV genes , including all micro RNAs , are expressed in latent infection of B cells . We already showed that KSHV latency locus transgenic mice consistently develop B cell hyperplasia . To find out possible host contributions to lymphomagenesis we evaluated the Myc oncogene . Compound KSHV latency locus and Myc mice developed plasmacytosis exemplified by increased frequency of plasma cells in the spleen , a high accelerated lymphoma development , and severe extramedullary hematopoiesis . These data show that the KSHV latency locus can cooperate with Myc activation in viral lymphomagenesis .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
KSHV Latency Locus Cooperates with Myc to Drive Lymphoma in Mice
|
Echinostome metacercariae are the infective stage for humans and animals . The identification of echinostomes has been based until recently on morphology but molecular techniques using sequences of ribosomal RNA and mitochondrial DNA have indicated major clades within the group . In this study we have used the ITS2 region of ribosomal RNA and the ND1 region of mitochondrial DNA to identify metacercariae from snails collected from eight well-separated sites from an area of 4000 km2 in Lamphun Province , Thailand . The derived sequences have been compared to those collected from elsewhere and have been deposited in the nucleotide databases . There were two aims of this study; firstly , to determine the species of echinostome present in an endemic area , and secondly , to assess the intra-specific genetic diversity , as this may be informative with regard to the potential for the development of anthelmintic resistance and with regard to the spread of infection by the definitive hosts . Our results indicate that the most prevalent species are most closely related to E . revolutum , E . trivolvis , E . robustum , E . malayanum and Euparyphium albuferensis . Some sites harbour several species and within a site there could be considerable intra-species genetic diversity . There is no significant geographical structuring within this area . Although the molecular techniques used in this study allowed the assignment of the samples to clades within defined species , however , within these groupings there were significant differences indicating that cryptic speciation may have occurred . The degree of genetic diversity present would suggest the use of targeted regimes designed to minimise the selection of anthelmintic resistance . The apparent lack of geographic structuring is consistent with the transmission of the parasites by the avian hosts .
Echinostomes are intestinal trematodes of humans and animals that are endemic to Southeast Asia and the Far East , i . e . mainland China , Taiwan , India , Korea , Malaysia , Philippines , Indonesia , and Thailand , and present a public health problem [1] . Human echinostomiasis has been attributed to at least twenty species belonging to eight genera ( Echinostoma , Echinochasmus , Acanthoparyphium , Artyfechinostomum , Episthmium , Himasthla , Hypoderaeumm , and Isthmiophora ) of digenea trematodes that use snails as intermediate hosts [2] , [3] . Clinical symptoms of echinostomiasis include severe epigastric or abdominal pain accompanied by diarrhea , fatigue , anorexia , and malnutrition in humans [2] , [3] . Numerous cases of human echinostomiasis have been reported in Japan ( E . cinetorchis , E . hortense , and E . japonicum ) , India ( E . malayanum and Paryphostomum sufrartyfex ) , and Thailand ( E . malayanum , E . revolutum , E . echinatum , and Hypoderaeum conoideum ) and are associated with the eating of raw fresh-water fish , snails , and tadpoles [4] , [5] , [6] , [7] . In Thailand , stool examination is used to detect echinostome eggs in Thai women . The most common parasite found in both pregnant and non-pregnant women is Opisthorchis viverrini , ( hookworm ) while Echinostoma spp . , Strongyloides stercoralis , Taenia spp . , Trichuris and Hymenolepis diminuta are more rarely found under these circumstances [8] . The identification of the species of echinostomes has been based in the past on morphology with major clades being defined on the basis of the number and distribution of the collar spines [9] . However , due to a large number of morphological similarities , this has become difficult in many cases . Molecular techniques have revealed differences among morphologically similar parasites [10] , [11] , [12] . An additional benefit of these techniques is that they can permit the identification of species , strains , and populations from a small quantity of tissue from any stage in their life-history [10] , [13] . Generally , an investigation of the phylogenetic relationships between echinostomes uses sequence data from the mitochondrial cytochrome c oxidase subunit 1 ( CO1 ) and nicotinamide adenine dinucleotide dehydrogenase subunit 1 ( ND1 ) genes [10] , [12] , [13] , [14] , [15] . These have been determined to be valuable for a more accurate estimate of echinostome diversity [13] , [16] . The internal transcribed spacer region ( ITS ) of ribosomal RNA ( rRNA ) has also provided a means of discriminating between species that have similar morphology [17] , [18] . In this study , molecular sequencing of the ITS 2 region and ND1 gene of echinostomes were utilized . The treatment of echinostomiasis is largely reliant on two anthelmintics: albendazole and praziquantel . Both of these drugs have been associated with the development of anthelmintic resistance ( AR ) [19] . It is important that their application follows a regime which will minimize the development of anthelmintic resistance . The rate of development of AR is a function of the genetic diversity of the target echinostome population [20] , consequently we were interested in determining the variety and genetic diversity of echinostomes in the area from which our patient population was drawn .
Echinostomes were obtained from naturally infected fresh water snail intermediate hosts; Filopaludina martensi martensi . They were collected from permanent and seasonal ponds from eight field sites in Lamphun Province in northern Thailand ( Table 1 ) . The metacercariae were removed from the snails by crushing and the parasites were examined for the presence/absence of the collar spines . Those metacercariae found to have collar spines were taken from each snail and were frozen immediately for later DNA extraction . DNA from all collected metacercariae was extracted as described in [21] . Briefly , 150 µl of 5% Chelex ( Fluka ) solution containing 10 µl of proteinase K ( Sigma ) at a concentration of 20 mg/ml was added to approximately 20 mg of trematode tissue . It was then heated at 55°C for 1 h , followed by gentle vortexing and heating at 95°C for 30 min , again followed by gentle vortexing . The mixture was centrifuged at 13 , 000 g for 10 sec . The supernatant was removed and stored at −20°C until it was to be used . Approximately 1000 base pairs ( bp ) of the ITS2 region were amplified by using the primers , forward BD1 ( 5′-GCT GTA ACA AGG TTT CCG TA-3′ ) and reverse BD2 ( 5′-TAT GCT TAA ATT CAG CGG GT-3′ ) . The PCR conditions used were the same as those previously described in [10] with amplification steps as follows: 2 min initial denaturation at 94°C , followed by 39 cycles of 1 min DNA denaturation at 94°C , 1 min primer annealing at 57°C , and 1 min at 72°C for extension and a final extension of 72°C for 10 min . The amplification of ND1 and the PCR conditions used were those previously described in [10] with amplification steps as follows: 2 min initial denaturation at 94°C , followed by 39 cycles of 30 sec DNA denaturation at 94°C , 20 sec primer annealing at 48°C , and 1 min at 72°C for extension and final extension of 72°C for 10 min . Approximately 530 base pairs ( bp ) of the ND1 gene were amplified under these conditions by using the primers: forward JB11 ( 5′-AGA TTC GTA AGG GGC CTA ATA-3′ ) and reverse JB12 ( 5′-ACC ACT AAC TAA TTC ACT TTC-3′ ) as those described in [10] . Successful production of the amplicons and their quality was checked using agarose gel electrophoresis with ethidium bromide staining to visualize the ITS and ND1 products . All ITS and ND1 PCR products were purified using the Cleanup PCR Kit ( Sigma ) and were subjected to sequencing . The raw sequencing data were assembled by Chromas Pro ( Technelysium Pty . Ltd , Australia ) . Bio Edit software [22] was used to make sequence alignments which were compared to GenBank deposited sequences using BLASTN . The sequence data produced in this study was combined with the data of 40 GenBank of echinostome sequences ( ITS and ND1 ) and was aligned using BioEdit . Haplotype diversity and nucleotide diversity were both calculated by DNAsp [23] . Phylogenetic trees were generated for each gene using all sites with maximum likelihood . Branches were tested for all inferred trees using bootstrap analysis on 1 , 000 random trees . The relationship between the genetic diversity and the geographic distance within and among the species groups were calculated for each gene with MEGA version 5 . 0 [24] . The intra specific variation within each of the suggested clades and haplotype networks were constructed with statistical parsimony analysis for ND1 sequences ( Network 4 . 6 . 1 . 1 , fluxus-engineering . com , Fluxus Technology Ltd . , UK , 2004 ) . The 4× rule/K/θ ratio species criterion was applied to determine the likelihood of cryptic speciation [25] .
High quality sequence data ( ITS2 and ND1 ) was obtained for forty metacercariae . Figure 1 shows the analysis of the ITS2 data obtained in this study , along with relevant sequences from GenBank as a Maximum Likelihood bootstrap consensus tree with 1000 bootstrap iterations . There is strong support ( >70% ) for monophyletic clades for E . malayanum , E . revolutum , E . paraensei , E . trivolvis and Echinoparyphium spp . Three of the samples from Ban Thi were grouped with the E . malayanum clade and two from Mae Ta with the Echinoparyphium/Euparyphium clade . The remainder of the samples formed two distinct monophyletic clades . The larger of these consisted of a single haplotype and both showed 98% identity within a range of Echinostoma ITS2 sequences . A Neighbor-Joining tree gave identical topology ( not shown ) . Figure 2 shows a Maximum Likelihood tree based on the ND1 sequences and relevant GenBank sequences . As with the ITS2 sequences , there was good support for monophyletic clades for E . malayanum , E . revolutum , E . paraensei , E . trivolvis and Echinoparyphium spp . In this analysis , four of the samples from Ban Thi were associated with E . malayanum and nine of the samples formed a monophyletic group with the Echinoparyphium/Euparyphium clade . The remaining twenty-seven samples ( labelled “Clade 3” ) formed a monophyletic group containing four haplotypes . The statistics associated with these samples are shown in Table 2 . In order to determine whether the echinostome-like samples were within the limits of the genetic diversity found in the Echinoparyphium , E . trivolvis and E . revolutum clades ( there are insufficient sequences of E . robustum in the database to allow it to be included in this analysis ) , we applied the K>4θ test . The statistics associated with this calculation are shown in Table 3 . This analysis indicated that the samples Ban Thi 2–5 should be considered as E . malayanum , but that the rest of the isolates , although sharing ancestry with either the Echinoparyphium or the Echinostomatrivolvis/revolutum/robustum clades , could be regarded as separate species by this criterion . There was considerable variability in the diversity of the species found at the different sites . Most sites had more than one species present and this parameter did not seem to be correlated with the permanence of the site . Figure 3 shows a schematic Median-Joining network constructed from the ND1 sequences . This analysis , using an alternative algorithm , confirmed the division of the samples into three clades . The genetic distances between the clades and their geographic spread are shown . The most frequent isolates were from the unidentified “echinostome-like” clade 3 grouping , which was found at six of the seven sites investigated .
The results presented in this paper provide the most extensive use to date of molecular techniques for the characterization of echinostomes from Thailand . Our results indicate that even small seasonal ponds may contain infected snails carrying a range of species . The samples in our study could be grouped into three distinct clades , E . malayanum , an Euparyphium/Echinoparyphium-like clade and an Echinostoma trivolvis/revolutum-like clade; worms identified as belonging to all of these groups have been shown to endemically infect humans in South East Asia [26] . Our findings are in agreement with those of [27] , [28] , who reported that E . malayanum and E . revolutum as being prevalent in Thailand . Although they recorded fixed genetic differences at 19% of the loci examined between Thai E . revolutum and those from the Lao PDR , they did not consider this as evidence of cryptic speciation as there was little divergence in the 200 bp of the mitochondrial cytochrome oxidase 1 ( CO1 ) sequences for the worms from these two regions . In contrast , our analysis of the mitochondrial diversity of the “echinostome-like” clade 3 was based on approximately 800 bp of the ND1 region of the mitochondrial genome – this region is known to be more susceptible to changes [10] , and thus may be more informative than the CO1 region . The analysis presented in Table 3 indicates that the “Echinostoma-like” clade 3 worms found in Thailand are genetically distant from E . trivolvis from North America and E . revolutum from northern Europe , and may be considered to constitute a cryptic species by the K>4θ criterion proposed by Birky [25] . Likewise the Euparyphium/Echinoparyphium-like Thai clade would appear to be a separate species from the North American Echinoparyphium spp . , with which it was compared . As we were able to group some of our isolates with worms that were previously identified as E . malayanum from Thailand , this may indicate that on the continental scale there is geographical structuring of the Echinostomatidae family . It has been shown for other trematodes that the involvement of a highly motile host in the parasite's life cycle will reduce local geographic structuring [29] . All of the Echinostomatidae in this study are known to be capable of using avian species , such as ducks , as their definitive host , and Thailand is situated on the East Asian-Australasian Flyway , which has been implicated previously in the spread of zoonotic diseases [30] . Support for this suggestion may be given by the analysis of an E . revolutum isolate using the ITS 1 sequences [26] , which indicated that it was more closely related to an Australian isolate than to those from North America . In conclusion , we have shown that people living in a relatively small and homogeneous geographic area of South East Asia may be exposed to infection by at least three species of Echinostomatidae . There is sufficient genetic diversity present among these populations to allow for the selection of praziquantel resistance , as has occurred in the case of schistosomiasis [19] , [31] and this finding emphasizes the need for targeted administration of chemotherapies .
|
Infections by food-borne trematodes are estimated to infect over 40 million people worldwide , although infections by echinostomes make up only a portion of these cases , usually in regions where their prevalence is high . In South East Asia and in the far east of Asia , human infection is associated with cultural and dietary factors and the prevalence of infection may reach 50% in parts of Thailand , Cambodia , and Laos . Treatment is generally dependent on the use of praziquantel or benzimidazole drugs but with the occurrence of anthelmintic resistance to these compounds it would be desirable to have an understanding of the diversity present in the echinostome populations within a given locality . This study deals with the systematics of echinostomes and informs various aspects of the epidemiology of echinostomiasis which may aid the development of future control strategies .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"gene",
"identification",
"and",
"analysis",
"genetics",
"biology",
"and",
"life",
"sciences",
"molecular",
"genetics",
"zoology",
"parasitology"
] |
2014
|
Molecular Analysis of Echinostome Metacercariae from Their Second Intermediate Host Found in a Localised Geographic Region Reveals Genetic Heterogeneity and Possible Cryptic Speciation
|
Recent experimental and clinical studies have provided diverse insight into the mechanisms of human focal seizure initiation and propagation . Often these findings exist at different scales of observation , and are not reconciled into a common understanding . Here we develop a new , multiscale mathematical model of cortical electric activity with realistic mesoscopic connectivity . Relating the model dynamics to experimental and clinical findings leads us to propose three classes of dynamical mechanisms for the onset of focal seizures in a unified framework . These three classes are: ( i ) globally induced focal seizures; ( ii ) globally supported focal seizures; ( iii ) locally induced focal seizures . Using model simulations we illustrate these onset mechanisms and show how the three classes can be distinguished . Specifically , we find that although all focal seizures typically appear to arise from localised tissue , the mechanisms of onset could be due to either localised processes or processes on a larger spatial scale . We conclude that although focal seizures might have different patient-specific aetiologies and electrographic signatures , our model suggests that dynamically they can still be classified in a clinically useful way . Additionally , this novel classification according to the dynamical mechanisms is able to resolve some of the previously conflicting experimental and clinical findings .
Neocortical focal seizures are episodes of pathological brain activity that appear to originate from spatially localised regions of the neocortex . The classical understanding of such seizures is that localised pathological tissue generates epileptic discharges ( epileptogenic zone [1] ) , which subsequently recruit connected tissue , resulting in an epileptic seizure . Hence , the removal of the epileptogenic zone would result in seizure freedom [1] . Such a view is particularly applicable to focal epilepsy patients with e . g . cortical dysplasia , where a clearly localised anatomical abnormality of the cortex is present . However , the classical understanding of neocortical focal seizures has not remained unchallenged , especially when treating patients without any clearly localised anatomical abnormalities . For instance , it is proposed that instead of a localised region of pathological tissue , an epileptogenic network [2]–[4] could underlie the generation of focal seizures . The spatial extent , and the participating regions of such a network are not yet clearly identified . Some indication is provided by the work of Stead et al . ( 2010 ) and Schevon et al . ( 2008 ) , who report the recording of highly spatially localised epileptiform activity on the scale of cortical columns [4] , [5] . Such electrographic activities , termed “microseizures” [4] , [5] , were recorded more frequently and for longer durations near the seizure onset zone [4] . Interestingly microseizures were also observed in non-epileptic control subjects , albeit in fewer locations and occurring less frequently than in epilepsy patients [4] . The authors hence proposed the hypothesis that “pathological microdomains” ( i . e . microdomains that are able to generate and sustain isolated epileptiform hyperactivity states ) might be found in healthy brains without leading to seizure onset . However , when occurring with sufficient density in one spatial area , they can form an epileptogenic network causing focal seizure onset from that area . An alternative mechanism underlying ( focal ) seizure onset is proposed on the macroscopic scale . Badawy et al . ( 2009 ) demonstrated that the motor threshold of drug naive focal epilepsy patients decreased up to 24 h before a seizure on the ipsilateral side to the seizure focus [6] . A similar study using patients with mesial temporal lobe epilepsy also hints at an elevated motor cortex excitability preceding the seizure onset [7] . Hence , a change in overall cortical excitability has been suggested as a mechanism for focal seizure onset [8] , [9] . This hypothesis is in line with the long-standing concept that seizures are a consequence of changing excitability of the brain [10] . However , the mechanism by which this general increased excitability over large parts of the cortex leads to focal onset dynamics is not specified . An essential point of recent debate that is not explicit in either of the above suggestions of focal seizure onset mechanisms concerns the mechanisms of seizure recruitment and propagation . Based on the observation of single unit activity in human focal onset seizures , Truccolo et al . ( 2011 ) proposed that the recruitment process is “highly heterogeneous , not hypersynchronous , suggesting complex interactions among different neuronal groups even at the spatial scale of small cortical patches” [11] . In contrast , Schevon et al . ( 2012 ) suggests that seizure propagation is a well-structured process , where the recruitment progresses as a smooth wavefront . Recruited tissues show a synchronous firing activity that is phase locked to the local field potential . It becomes clear that focal seizure onset and recruitment is still far from understood , and that prevailing hypotheses and observations lack a unifying framework in which they can be tested and analysed . In order to achieve this , we turn to mathematical models of cortical spatio-temporal dynamics . Traditionally , two types of models have been used: ( i ) Continuum models ( e . g . [12] , [13] ) or neural field models ( see [14] , [15] for reviews ) treat cortical tissue as a homogeneous continuous medium . The spatial extent often ranges from a few millimetres to a few centimetres [16] , [17] . Pattern formation and travelling waves of activity have been studied extensively in these systems ( see [18] , [19] for reviews ) . Such spatio-temporal patterns have been related to epileptic activity . For example [17] , [20] model the recruitment and propagation of a focal onset seizure as a propagating wave over a continuous medium . ( ii ) Network models treat the cortex as a connected network of cortical units ( nodes ) , where often nearest neighbour , random , small-world or hierarchical connectivities are used . Depending on the definition of the network nodes , these models are used across all scales from local populations of neurons [21] to the whole brain level [22] , [23] . Network based models have investigated how network structures impact seizure synchronisation dynamics [23]–[25] , seizure frequency [26] , or the spread of seizure activity from an epileptic focus [27] , [28] . To specifically model the mesoscopic epileptic dynamics of extended cortical tissue , [29] suggests arranging coupled units of neural mass models ( see [30] for review ) as a sheet . Similarly , [31] , [32] arrange neural mass models according to the tessellated surface of the brain and coupled neighbours to simulate scalp and intracerebral dynamics of focal seizures . Such an approach , although technically a network approach , can approximate the behaviour of continuum models ( compare [29] and [20] ) . Recently , [33] also relates a network of mass models to an equivalent field model directly . However , the connectivity in realistic cortical tissue appears to require a combination of both continuum and network approaches . Connections to nearby neighbours are very dense [34] , such that it approaches the continuum case . Nevertheless structured long-range connections can form a complex network that is best described by a networks approach [35] . Hence , to describe the mesoscopic scale of the cortex , combinations of both network and continuum approaches have also been suggested , e . g . including heterogeneous long-range connections in neural field models ( see for example [36]–[39] ) . Starting from a network perspective , Voges et al . ( 2010 ) propose to use a network model that includes dense local connections , approximating the continuum case , and incorporate remote excitatory connections that bridge distances of several millimetres [35] . The remote connections are furthermore structured and tend to target remote clusters or patches . In this work , we advance upon previous spatio-temporal network models of cortical tissue on the mesoscopic scale and use a dense array of cortical units ( cortical columns ) that reflect the activity of local neuronal populations . For connectivity between the units we use the suggestion in [35] and incorporate dense local connections as well as patchy remote connections . This model has the advantage of combining both types of modelling approaches and thereby we create a spatially hierarchical model to study multi-scale dynamics . Using this model , we investigate the dynamical mechanisms leading to the observation of focal onset seizure activity . We find that three different classes of dynamical mechanisms are compatible with a focal onset of an abnormal rhythm . Each of these classes show particular distinguishing features in terms of their dynamics and response to stimulation . Furthermore , they suggest alternative treatment strategies that could provide the basis to improve treatment options for patients in the future .
We commence by defining the smallest unit in our model: the cortical minicolumn . This choice is based on the highest spatial resolution of the clinical observations with which we compare the model output . To reflect the electric activity of a minicolumn , we use an established model of excitatory and inhibitory neural population activity: the Wilson-Cowan model [40] . This model expresses the percentage firing activity in an excitatory ( ) and an inhibitory ( ) neural population over time . It assumes that the and populations are coupled to each other and that the inputs to a target population sum together and influence the firing activity of this population . We use such a coupled unit to represent a single cortical minicolumn ( see Fig . 1 ) . The equations for our model are: ( 1 ) where is the fractional firing activity in the excitatory population; is the fractional firing activity in the inhibitory population; and denote the basal activity level of the excitatory and inhibitory populations , respectively; is the noise input to the excitatory population ( e . g . subcortical input ) with as the coupling strength of the noise input; and the connectivity constants ( with or ) regulate the coupling strength between the populations . is a sigmoid function , which derives from a distribution of firing thresholds in the underlying neural population [40] . It is defined as , where is the steepness of the sigmoid and is the offset ( in ) of the sigmoid . We fix the sigmoid parameters ( ) following previous work [41] , as variations in the other parameters effectively result in a change of the sigmoid shape . The Wilson-Cowan model has been subject to extensive studies in the last decades [42]–[44] . The slightly simplified version in Eqn . 1 ( see also [45] ) was shown to maintain the same bifurcation structures as the original model [41] . The simplification removed the bracket of ( or ) that the sigmoid was multiplied by in the original equations . Mathematically , the term has little impact on the dynamics . It is essentially rescaling the phase space and parameter space . In order to model cortical tissue , we connect an array of minicolumn units to form a cortical sheet ( also referred to simply as a sheet ) . We also refer to each of these minicolumn units simply as units . This formulation assumes an effectively two dimensional structure for the cortex . In reality , there is interplay between the three dimensional cortex , subcortical structures and other brain regions . However by making the simplifying assumption above , these influences in brain dynamics are absorbed into the intrinsic parameters of a minicolumn . Similar approaches of modelling the cortex as a 2D sheet can be seen in [12] , [15] , [29] , [46] , [47] . As an approximation we assume all minicolumns to be in size [48] . A macrocolumn is then formed by minicolumns , which agrees with the size suggested in [49] . Furthermore we investigate cortical sheets with minicolumn units ( i . e . , or macrocolumns ) . Thus and in Eqn . 1 become vectors of the length ; and the connectivities become matrices of the dimension . We limited the size of the sheet to minicolumns in length , as we assume the mean activity of such a sheet reflects the signal recorded on a single ECoG electrode . Each excitatory population is additionally driven by noise ( ) representing input from other unmodelled regions , e . g . subcortical input . The noise is the same within each macrocolumn in agreement with experimental findings and the definition of macrocolumns [48] , [50] . We used noise values drawn from a standard normal distribution as input . The effective noise coupling strength is set to . In this setting the system is not entirely dominated by the noise input but the noise influences the deterministic dynamics . Simulations of the system used a fixed step solver , with a stepsize of 2 ms . Qualitatively equivalent results are found for smaller stepsizes . Fig . 1 schematically summarises the model . In the model we use three types of connections between minicolumns , based on the cortical connectivity suggested by Voges et al . ( 2010 ) [35] . All choices for parameters of the connectivity are also based on [35] , where they are derived from tract tracing experiments in human cortical tissue . ( I ) The first type consists of local excitatory connections , where each excitatory population of a minicolumn unit connects to the excitatory populations of neighbouring units in its immediate proximity ( Fig . 2 ( a ) , top ) . Here , each unit has a probability to connect to its neighbours that follows a Gaussian fall-off with distance . The standard deviation of the Gaussian is set to , as within radius most local connections are found [35] . We furthermore do not allow for local excitatory connections beyond a radius as these are incorporated into a specific longer range connectivity scheme , as described below . Fig . 2 ( a , bottom ) shows an example of one unit ( red ) and the neighbouring units ( black ) is sends local excitatory connections to . The connectivity matrix for the local excitatory connections is denoted , where each connection has the weight ( subscript denoting local connections ) . ( II ) The second type of connections is from the excitatory population of each unit to the inhibitory populations of close neighbours ( Fig . 2 ( b ) , top ) . We use the same algorithm and parameters as in ( I ) to generate these connections . Fig . 2 ( b , bottom ) shows an exemplary realisation of local inhibitory connections from one unit . We refer to the connectivity matrix for the local inhibitory connections as , where each connection has the weight . ( III ) The third type are remote patchy overlapping connections from each excitatory population to excitatory populations at some distance ( Fig . 2 ( c ) ) [35] . All the parameters are following the suggestions in [35] . We generate random patches for each macrocolumn and all minicolumns within the macrocolumn can connect to these patches with outgoing connections . This fulfils the suggested average ratio of of local connections to remote connections [35] . Each patch consists of minicolumns ( the patch radius is , i . e . 5 minicolumns radius , i . e . minicolumns ) and is located within distance . Macrocolumns share patches with one direct neighbouring macrocolumn , which can increase the distance between macrocolumn and target patch to more than . These parameters are in line with the suggested and experimental values listed in [35] . The algorithm that generates the remote connectivity matrix is described in Text S1 . We call the connectivity matrix for the remote excitatory connections , where each connection has the weight , ( subscript denoting remote connections ) . The connectivity matrix therefore consists of , and the self-excitation value of each excitatory population on the diagonals ( , subscript denoting self connection ) . Similarly consists of and the connection value of the connection within the minicolumn unit on the diagonals ( ) . The other matrices are diagonal matrices only , as they are exclusively connections within a minicolumn . Long-range inhibition is not included , following [35] . In order to aid the understanding of the resulting connectivity being created by the aforementioned rules , Fig . S1 additionally show the in/out degree and the distance distributions of the local , as well as remote connections . Text S1 further explains the details of the connectivity . A cortical sheet with toroidal boundaries was used in the construction of the connectivity matrices , following [35] , to avoid boundary cut-off effects caused by lack of basal input due to lack of neighbours . Text S2 discusses in detail how different boundary conditions affect the model dynamics and we will show that all our presented results are not affected qualitatively by the choice of boundary conditions . The choice of model parameters for the isolated Wilson-Cowan unit was based on dynamical reasoning . The dynamics of a single minicolumn unit ( in the following referred to as unit ) has been subject to extensive studies . The invariant dynamic behaviour in an unit is limited to either a stable fixed point ( node or focus ) or a stable limit cycle , and two stable fixed points ( see [41] , [42] for details ) . As we are interested in the transition between fixed point and limit cycle we select model parameters in the vicinity of the transition to oscillations . Depending on the combined parameter variation , either a homoclinic or a Hopf bifurcation occurs . However the single unit in model is incapable of oscillations , even with increased constant input . Text S3 shows details of the current parameter setting for a single node . Based on the dynamics of a single unit , the dynamics of the fully coupled sheet is classified as: ( i ) fixed point ( corresponding to the lower fixed point in the unit; the spatial average of the whole system does not show prominent regular oscillations over time ) ; ( ii ) oscillation ( corresponding to the limit cycle in the Wilson-Cowan unit; the spatial average of the whole system shows high amplitude oscillations over time ) ; or ( iii ) bistability between fixed point and oscillatory state . Although the coupled Wilson-Cowan systems are known to show a complicated repertoire of oscillatory states ( in term of regularity and phase relationships [43] ) , we do not sub-classify the oscillatory states further . The epileptic EEG or ECoG has a considerable noise component and is non-stationary such that a reliable classification from clinical data is challenging . Also , a theory of spatio-temporal patterns in large heterogenous networks of nonlinearly coupled nonlinear oscillators is lacking . However , it was shown previously that a combination of mathematical understanding of a single network unit and computational studies of the network can lead to improved understanding of clinically relevant phenomena ( e . g . the generation of oscillatory afterdischarges in epileptogenic cortical tissue [29] . In the clinical setting diverse waveforms can be observed in electrographic recordings of neocortical focal seizures . However , we seek a simplification of this situation in our model , which captures some essence of abnormal dynamics during seizures . We therefore focus on the existence of high-amplitude oscillations in the model output as representative of seizure activity , in contrast to a low-firing state , which is representative of “background” or inter-ictal activity . This idea follows previous modelling studies ( for example [51]–[53] and references therein ) . The approach is further supported by the suggested clinical definition [54] of a seizure state as oscillations in unit firing , which are phase locked to high amplitude local field potential oscillations . The background state is characterised by irregular firing patterns , which do not correlate with any oscillations in the local field potential . In a single unit , we shall hence identify the background with the fixed point . As the Wilson Cowan oscillator only has one limit cycle representing synchronous rhythmic firing activity on the local population level , we shall identify this limit cycle with the local seizure state . Our model is additionally capable of a third state: the permanently firing state ( referred to as “upper fixed point” in the Wilson-Cowan model ) . This state is not identified with any clinically observable state , and we hypothesise that the parameter settings required to reach this third state do not play a functional role during focal-seizure onset . In the simulated coupled sheet , we understand high-amplitude synchronous ( plus minus phase shift ) oscillations in firing and LFP over several connected units as the seizure core [54] . Hence , full recruitment will be understood if the whole sheet is in such a state , where all units are in a synchronous oscillatory state . Text S5 describes how we detect these full recruitments or localised non-recruiting seizure cores for each figure . Other types of oscillations ( e . g . non-synchronous low amplitude oscillations , which could represent non-pathological oscillations ) on the full-sheet level were not specifically identified or analysed . The matlab code for the model is published online ( ModelDB Accession number: 155565 ) .
To chart the dynamics of the model cortical sheet with respect to parameter changes , we focus initially on spatially homogeneous variations in the four parameters highlighted with red frames in the schematic in Fig . 3 , i . e . , , and . Fig . 3 demonstrates that there are large regions of parameters for which the system resides in the background state ( black regions in Fig . 3 ) , or oscillatory state ( dark blue regions in Fig . 3 ) . Additionally , in some parameter regions the oscillatory state can be found to be bistable to the background state ( light blue regions in Fig . 3 ) . A consequence of this is that a system in this parameter region can exhibit either background or oscillatory dynamics under the same parameter conditions . The transition from background to oscillatory activity is dependent upon all four scanned parameters . Pairwise scans of additional model parameters can be found in Text S4 , which demonstrates that combinations of other parameters also give rise to background , oscillatory or bistable dynamics . From the dynamical systems perspective it is often assumed that the epileptic brain resides in a parameter setting close to the onset of oscillations [51] . Hence , we selected one standard parameter set for our model in line with this idea , as indicated in Fig . 3 ( a ) and ( b ) by a red dot . This standard parameter set serves as our model interictal state , or monostable background state . Dynamically , the interictal state is a node and excitability can be detect for a range of stimuli in this state ( data not shown ) . For an exemplary parameter change ( red arrow in Fig . 3 ( a ) ) we have also analysed the transitions in detail in a noise-free system . The monostable background state is the only stable fixed point in our system at . The onset of bistable high-amplitude oscillations occurs suddenly at . At the transition to monostable oscillations ( at about ) , the background node ceases to exist and the oscillatory state becomes the only stable state . When changing from the standard interictal parameter setting , similar transitions occur , only the background node remains stable and does not cease to exist . Having demonstrated the effect of global parameter changes on the mean-field dynamics of the model , we proceed to examine the different ways in which transitions to seizure activity can occur spatio-temporally . The parameter scans in the previous section imply that a slow parameter change that crosses from the background to the oscillatory region can induce a transition from background to seizure dynamics on the mean-field . An example of such a parameter ramp over time is indicated by the arrow in Fig . 3 ( a ) and in Fig . 4 ( a ) . This suggestion follows a traditional modelling approach of seizures induced by bifurcations ( see for example [55] , [56] , also c . f . [57] ) . In simulations of this scenario the onset of the abnormal rhythm is approximately simultaneous in all spatial locations , as the corresponding parameter is modified simultaneously in all units across the sheet . In our case , the transition occurs at about as a bifurcation from a node to an oscillatory state , where the onset of oscillation frequency and amplitude is sudden and discontinuous , and the node ceases to exist . Using such a transition , we sought to establish whether the model can produce focal onset dynamics . Typically cortical tissue is not globally homogeneous . We therefore consider the impact of a locally altered region of model cortex , which is realised by assuming a local parameter heterogeneity in the model . We specify a patch in the middle of the model cortical sheet that receives increased feed-forward excitation . This heterogeneity is not visually detectable in the interictal state ( see Fig . 4 ( c ) , first panel ) . However , in a simulation with a parameter ramp as shown in Fig . 4 ( a ) , the heterogeneous region displays an earlier response ( Fig . 4 ( c ) ) . Dynamically , the earlier ignition of activity in the heterogeneity is due to the introduced difference in feed-forward excitation , which lowers the threshold beyond which oscillatory dynamics can ensue . Next we explore the mechanisms leading to focal onset rhythmic activity when the whole model cortical sheet is in the bistable background state . A bistable state has been proposed to underlie situations in which the transition to abnormal brain activity is not caused directly by global parameter changes ( see e . g . [59] ) . It was postulated specifically as underlying the transition to epileptic seizures in the context of generalised [53] , [60] and focal seizures [17] , [28] , [61] . In order to explore this scenario we prepare the cortical sheet in a global parameter setting of bistability . If the sheet is initiated in the background state , it will remain in the background state in the absence of strong perturbations . To initiate oscillatory activity , the background state can be disturbed in two possible ways: either by a short , temporary stimulus or by a persistent stimulus . We shall explore both perturbations in the following . We prepared the cortical sheet in the bistable background state by decreasing the feed-forward inhibition compared to the interictal parameter setting . Our choice to decrease feed-forward inhibition is inspired by the suggestion that a failure of inhibitory restraint [54] contributes to seizure onset . Equally , a change in , or other parameters could have been used . In our model , the bistable background state ( dynamically also a node ) does not show any obviously different dynamics compared to the monostable background state . However , when perturbed locally by a pulse-stimulus , the whole cortical sheet can transit to the co-existing oscillatory state . In Fig . 5 ( c ) and ( d ) we demonstrate how this transition unfolds in terms of spatial-temporal dynamics . After the stimulation , a subset of the stimulated units transits to the bistable oscillatory regime , which subsequently recruits neighbouring units into the oscillatory state . The recruitment in this connectivity parameter setting progresses as a wave , similar to the observation by Schevon et al . ( 2012 ) [54] . The comparison between clinical data and the simulation is invited in Fig . 5 . In both cases the continuous progression of a wavefront of increased firing activity is observed . In the current connectivity setting , heterogeneities in the propagation dynamics can also be observed . This is due to the heterogeneously created remote projections , which can support the activation of tissue at some distance from the primary recruitment site . A purely local connectivity creates an even propagation front ( Text S7A , and Fig . S8 ( a ) ) , and a purely remote connectivity gives rise to stochastic patchy propagation ( Text S7A , and Fig . S8 ( b ) ) . A mixture of propagation behaviours between these two extremes can be observed when a connectivity scheme that combines both features is used ( as in Fig . 5 ( d ) ) . To demonstrate that these findings are repeatable and reliable despite the noise input to the system , we scanned the recruitment speed after a ( fixed ) pulse stimulus for different values of and in and around the bistable parameter setting . Averaged over 5 trials using different noise input , little variation in recruitment speed due to noise was found for a fixed parameter set . However , recruitment speed did vary with the parameter settings ( see Text S7B for details ) . The stimulus size was also found to influence the recruitment , and a minimal stimulus size was found to exist depending on the parameter setting ( Text S8A and Fig . S11 ) . This means that a critical number of units have to be stimulated to induce the transition of the sheet to the seizure state , when it is bistable . This finding is potentially important for the clinical determination of the spatial extent of pathological stimuli in a patient-specific context . A perturbation to the model sheet need not be externally generated , but can arise due to local , abnormal activity generated within the model . In Fig . 6 we demonstrate that the existence of an oscillating patch in the sheet can also trigger a transition into seizure dynamics . Fig . 6 ( d , e ) demonstrate that if the parameter setting of the surrounding sheet is monostable in the background state , the hyperactive microdomain remains isolated in its epileptic activity ( red trace in Fig . 6 ( d ) ) . This agrees with the clinical observation of spontaneous microseizures that remain spatially localised and do not recruit surrounding tissue . If , however , the rest of the system is in a bistable setting , a continued local perturbation by an oscillatory microdomain can start to recruit the surrounding units into the seizure state ( Fig . 6 ( f , g ) ) . The propagation pattern of recruitment is similar to the case of recruitment following a pulse stimulation to the bistable sheet . Depending on the connectivity settings , smooth propagating waves , patchy propagations , or a mixture of both can be observed . Text S7B demonstrates that an oscillatory microdomain can produce recruitment speeds of between and , using some example parameter changes in . This is within a range of propagation speeds reported experimentally ( 0 . 1–100 m/s [63] ) . In order to check the robustness of this onset mechanism , we tested the dependency of recruitment on both the parameter setting of the surrounding and the size of the pathological microdomain ( see Fig . S12 and Text S8A ) . We find that for a fixed bistable parameter setting , a minimum threshold exists for the number of units that are required to induce recruitment . When the parameter setting lies closer to the monostable oscillatory setting , fewer units are required for recruitment . This behaviour is stable with different noise inputs and microdomain positions in the model sheet . This finding is important for the clinical determination of pathological vs . neutral microdomains in a patient-specific context . After demonstrating focal seizure onset in a globally oscillatory and a globally bistable scenario , we now turn to the case of a globally monostable background . We shall investigate a system in the monostable background state except for one or multiple localised hyperactive microdomains . We examine the spatial conditions under which these hyperactive patches can recruit their surrounding , even though globally the oscillatory state neither exists exclusively ( class I ) nor coexists ( class II ) in the absence of these patches . We prepare the system in the monostable background state ( the standard interictal parameter set ) , except for some oscillatory microdomains . We begin by systematically assessing how the recruitment from these microdomains depends upon the total number of hyperactive units and the number of subclusters that microdomains are organised into . Here a subcluster is a contiguous patch of units , positioned randomly on the sheet . Fig . 7 ( a ) shows that despite the surrounding being in the monostable background state , partial or full recruitment can be registered in some configurations . E . g . Fig . 7 ( a ) demonstrates that when 2250 units ( 10% of the whole sheet ) are hyperactive , no recruitment is registered if all the units are organised into one compact patch ( red dot ) . However if this same number of hyperactive units are organised into 17 subclusters of equal size , randomly distributed over the model sheet , noticeable recruitment can be observed ( purple triangle ) . The recruitment behaviour additionally depends on the exact parameter of the surrounding . For example if the exogenous input parameter , , is set to a value closer to the global bistability ( , Fig . 7 ( a ) ) recruitment starts at a lower total number of hyperactive units and with a lower number of subclusters than when using , further away from the bistability ( Fig . 7 ( b ) ) . This recruitment behaviour is stable with regards to the noise input in our simulation . However , the exact values of the total number of hyperactive units and the number of clusters vary slightly with the position of the ( sub ) clusters ( see Fig . S13 , Text S8A ) . Fig . 7 ( d , f ) show example time series from two simulations using the same number of hyperactive units ( 2250 units , 10% of the whole sheet ) , but different numbers of sub-clusters . In the case of a single cluster , only a few units are recruited ( Fig . 7 ( f ) ) . In the case of many sub-clusters , the whole sheet is recruited ( Fig . 7 ( d ) ) . Recruitment can be observed to begin in areas of increased subcluster density ( for example right side of T = 1 . 6 s in Fig . 7 ( d ) ) . The “normal” monostable tissue between nearby subclusters is recruited first . In this way , the subclusters that lie in close proximity recruit the healthy tissue between them to form a bigger contiguous cluster of oscillatory activity ( T = 2 s in Fig . 7 ( d ) ) , and eventually recruit the whole cortical sheet ( T = 4 s , T = 10 s in Fig . 7 ( d ) ) . The recruitment of monostable surrounding tissue in the background state is not as intuitively understandable as for instance the case of recruitment of a bistable surround . The scans in Fig . 7 ( a , b ) show that the spatial arrangement of “recruiters” is important . We propose that the basic mechanism is based on the coherent oscillatory input to units in the background state , which can incite them to oscillate despite their configuration being monostable . The parameter change in the microdomains induces the microdomains to become intrinsically oscillatory . Hence , the recruitment from microdomains induced by this local parameter change is a bifurcation from a node to an oscillatory state . The onset of oscillations , while ramping , occurs with a sudden change in frequency and amplitude . We additionally address the effect of boundary conditions on this mechanism in Text S2 . We have shown that clusters of autonomous oscillations can induce recruitment of the whole system to the seizure state . In this section we investigate additionally whether a system-wide bistability can be induced by localised , bistable clusters of tissue ( i . e . a set of bistable microdomains ) . The reasoning is that if the network of microdomains is bistable , specific localised stimuli will be able to induce localised oscillatory behaviour in the patches , which in turn would lead to recruitment of the monostable surrounding as in class IIIa . For such a scenario , it is required to determine the conditions under which a local cluster of tissue is bistable . Hence we scan the size of a microdomain embedded in a monostable background surrounding versus an exemplary local parameter change ( ) and determine whether a microdomain patch is bistable by applying a single-pulse stimulus ( Fig . 8 ( a ) ) . An elevation in leads to bistability of the microdomain . Upon further increase of , the microdomain becomes monostable oscillatory . This bifurcation also occurs with a sudden change in amplitude and frequency . As the patches become smaller , has to be higher to reach bistability ( or the monostable oscillations ) in the microdomain . The dependency of the dynamic behaviour on the size of the microdomain can be understood if we consider that the oscillatory state in the system emerges from the coupling of individual units . Using information from the previous parameter scan , we set up a monostable sheet and distribute bistable microdomains within it ( Fig . 8 ( b ) ) . Such a system remains in the monostable background state in the absence of perturbations . Multiple single-pulse stimuli applied randomly at different locations can be used to activate some bistable patches ( Fig . 8 ( c ) ) . Some degree of coactivation ( i . e . an active patch subsequently activating a connected silent patch ) can also be observed . Once activated and in high enough density , the patches can cause recruitment of their non-oscillatory environment as shown in the previous section . Fig . 8 ( c ) shows a time course of multiple stimuli activating silent bistable patches , which ultimately results in full recruitment of the sheet .
In this study we used a novel spatio-temporal model of the dynamics of cortical minicolumns , coupled by multi-scale cortical connectivity , to categorise possible mechanisms of focal seizure onset . We showed that in this framework , apparently conflicting clinical observations regarding focal seizure onset can be understood and unified . We furthermore suggested how to test for the different onset categories , and made predictions regarding possible treatment methods for each category . The three mechanisms we identified by which a focal seizure onset can occur are: ( I ) A global parameter change which induces a global bifurcation of a piece of cortical tissue to the seizure state . ( II ) A global bistability combined with a local trigger leading to transition to the seizure state . ( III ) A globally monostable state with local parameter changes causing recruitment of the whole system . We expect that either mechanism may dominate the onset of focal seizures in different patients . The model employed herein uses the approach of discretised , coupled spatial units to reflect the activity of a piece of contiguous cortical tissue . Each unit in the current model is described by Wilson-Cowan equations , which embody the collective activity of local excitatory and inhibitory neural populations [40] . Compared to detailed neuronal models of cortical activity ( e . g . [67] ) , the Wilson-Cowan model is computationally less demanding and the number of parameters to analyse is manageable . However the parameters of the Wilson-Cowan model are more abstract in nature . Thus , if for example cellular mechanisms of focal seizure onset are to be investigated ( e . g . [68] ) , a detailed neuronal model is required . Similarly , if the detailed laminar and horizontal interaction between different types of excitatory and inhibitory populations is of interest , the populations in our model can be extended . However , in our current study , describing the dynamics of cortical minicolumns in terms of the lumped activity of generic excitatory and inhibitory neural populations allowed us to model a hierarchy of clinically relevant spatial scales by reducing the level of detail for the analysis . The classical Wilson-Cowan model has been used to reflect EEG/ECoG dynamics in the delta to beta range [41] , [69] . Similarly , we used it here to model seizure oscillations in this frequency range . Faster or slower dynamics are therefore not considered in our current approach , although it will be interesting in future studies to investigate the influence of these aspects , for example the addition of slower time scales . The incorporation of additional intrinsic long-term dynamics ( e . g . adaptation or learning ) can lead to the creation of additional types of dynamics , which could also be relevant for clinical question . If the time scale separation is sufficient ( i . e . intrinsic long-term dynamics are on the order of seconds or longer ) Fenichels theorem [70] indicates that our presented attractors would remain as manifolds in the full system with a slower time scale . Hence the slower time scale dynamics would modulate and orchestrate the transitions between the stable dynamics presented here . Indeed , the global parameter configuration ( monostable , bistable , and oscillatory ) used in our current model could be fluctuating over time according to some slow dynamics . It might be that the parameters of the cortex of patients as well as healthy subjects are constantly changing [8] , putting cortical tissue in different global configurations at different times . However , in an epileptic patient , either these global fluctuation are either too extreme leading to a global bifurcation into the seizure state ( class I ) , or would remain silent if not co-occuring with a local trigger ( class II ) , or do not affect seizure onset directly ( class III ) . In patients with stereotypical seizure onset ( i . e . the seizure onset is repeatedly from the same region with a similar electrographic pattern ) , the underlying long time-scale dynamics are either similar from seizure to seizure , or at least giving similar dynamical conditions . Hence the categories would apply to all seizures of the same stereotype ( in the same patient ) . Our classification is hence crucial to determine ( patient-specifically ) the exact role of the parameter fluctuation dynamics in seizure onset . Practically , a constant multi-scale monitoring of the cortical activity , as well as regular stimulation tests should be carried out to determine the global and local parameter configuration . In our model , we equated high amplitude oscillations with a pathological state in each mini column . This is mainly inspired by the observation that the seizure core contains highly active neurons with firing patterns phase locked to the oscillatory LFP [54] . We believe that in our case , firing activity might provide a better benchmark for comparison of clinical and simulation data than LFP , as the generators of the different components of focal seizure field potentials are largely unknown . Hence , following [54] , we identified high amplitude oscillations in firing as the seizure state and low level firing as the background state . Additionally , the approach of identifying oscillations with seizures and fixed points with background activity is well established in the modelling literature ( see for example [51] , [55] , [56] , [71] , [72] ) . It is in line with the long-standing suggestion of dynamic diseases [73] , [74] , where the disease state is identified as an oscillatory attractor and the background state as an non-oscillatory , primarily noise dominated state . Only very little clinical or theoretical understanding exists regarding the different waveform morphologies in focal seizures [75] , [76] and how seizure onset mechanisms influence them . Weiss et al . ( 2013 ) [77] point out that high frequency oscillations phase locked to low frequency oscillations at seizure onset could be an indicator for increased , structured firing in the underlying tissue and hence an indicator for the seizure core . Future studies should specifically investigate how focal seizure onset field potential morphologies arise , as well as how they relate to firing patterns . Potentially , the knowledge gained by studies of waveform morphology in purely temporal framework such as [41] , [71] , [78] could be of use . Each of the onset mechanisms we describe relies on a certain configuration of global parameters , where global is in reference to the scale of the model of about one square centimetre of cortex . However , in reality global parameter changes in the brain will vary from the whole-brain level to the scale of our current model , all of which can influence the global parameter configuration in our model . A range of physiological and pathological conditions could cause such variations . For example different phases of the sleep-wake cycle or hormonal variations [8] can change the excitability of brain . Pathological conditions include misregulation of excitation and inhibition [10] . If pathological parameters changes exist in a limited part of the cortex , then the focal seizure could be limited in its spatial extent . However , if abnormal dynamics entrain a large region , they could activate other whole-brain networks ( including subcortical networks ) leading to generalised abnormal activity ( secondary generalisation ) . In this context the model can also resolve the apparent contradictions in the experimental literature on the mechanisms of focal seizure onset . The contradiction of focal seizure onset being a result of global ( whole brain network changes ) or local ( abnormally behaving cortical columns ) mechanisms is no longer a contradiction in our model . We have shown that global as well as local mechanisms can interact and we have classified the interaction in three major categories . Hence , global changes can cause ( class I ) , or support ( class II ) , or modify ( class III ) seizure onset . Equally , local changes can trigger ( class II ) , cause ( class IIIa ) , or support ( class IIIb ) focal seizures . It is hence no surprise that clinical and experimental observations supporting both global as well as local mechanisms are found . Similarly , the contrasting observations from [11] and [54] can also be united: it might be that very near to the “focus” recruitment propagates as a wave over the local network . However , further away regions are probably recruited via remote or long-distance connections first and activation is primarily patchy . Hence , the conflicting recruitment dynamics described by Truccolo et al . ( 2011 ) and Schevon et al . ( 2012 ) is explained in our model by the propagation of activity via different networks . Interestingly , Schevon et al . ( 2012 ) [54] hypothesised that the ictal penumbra could restrain the propagation of epileptic activity due to an “inhibitory veto” . In our model of non-recruiting microdomains , we find that the restraint is not explicitly excessive inhibitory firing activity in the penumbra . Rather , the net synaptic input into each unit in the penumbra is not strong enough to entrain them to become oscillatory . A question that arises from our study is whether the categories we established can be generalised to any spatio-temporal system showing bifurcations or bistabilities between a non-oscillatory ( fixed point ) and an oscillatory state . We propose that the detailed transition dynamics will depend on the specific system . However , we postulate that the three categories are general features of spatio-temporal systems showing either a bifurcation or a bistability between fixed point and oscillation . This is mainly due to the observation of the three categories in other spatio-temporal models using different model formalisms as well as underlying connectivity . For instance [17] essentially show class IIa in their partial differential equation model . Class I has been shown in a coupled Amari-type model representing a whole-brain network [24] . [64] show a class IIIa transition in their rule-based model of microseizures and recruitment . To our knowledge , class IIIb has not been demonstrated explicitly so far . We emphasise that the dynamical classification only becomes useful in the context of a relevant model , and the interpretation becomes useful when it is applied to the clinical context , e . g . to search for the cause of the seizure and to devise potential treatment strategies . We have outlined major features and the expected observations of each class of onset mechanism in the Results section . A question that remains is how one would practically tell the classes and subclasses apart in a clinical setting . This question is crucial , as treatment will depend on the individual mechanism of seizure onset in a patient . We suggest that high resolution spatio-temporal recordings , similar to [79] , combined with local perturbation studies ( similar to [80] , but on different spatial scales ) might be the key to answer this question . In the context of local perturbations , we point out that although we only demonstrated the impact of pulse stimulation in our current study ( to essentially reset the activity of the excitatory population ) , practically the effect of different types of stimulation has to be assessed prior to its usage for the classification of the seizure onset . In this context , we recommend the development of patient-specific models to classify the dynamic seizure onset mechanism . This would involve incorporating the patient-specific connectivity of the affected cortical area ( e . g elucidated from high resolution track density imaging [81]–[83] ) , as well as online parameter fitting according to passive and active high-resolution spatio-temporal recordings . This could enable the use of closed loop counter-stimulation devices ( as demonstrated in Fig . S15 ) . Additionally , such patient-specific models can be employed to predict optimal treatment protocols , for example minimal cortical micro-incisions to stop the recruitment of tissue into full seizures ( see Fig . S16 and [84] ) . In the case of a global shift of parameters ( affecting larger brain regions ) causing or facilitating seizure initiation and recruitment , it is probably desirable to target the reason for the global shift directly rather than trying to suppress seizure onset locally . In fact class I onset demonstrates that although one particular cortical location appears to be the source of seizure initiation ( epileptogenic zone ) , the mechanism causing the seizure can be a global parameter shift in an extended tissue . The “epileptogenic zone” only reacts first due to its increased local threshold . Then , despite reducing or removing the local activity in the seizure onset zone , the seizure still starts , albeit from a different “most active” site . This concept of the existence of alternative foci has been proposed from clinical reasoning [1] , [3] to explain why some surgical resections of epileptogenic zones have little effect . Conceptually , we hence propose to distinguish between global or generalised causes of focal seizures , which induce the seizure by a global parameter shift - and local or focal causes of seizure , which can be facilitated by global bistability settings . The spatial extent of the cause of the seizure , however , can differ greatly from the spatial extent of the observed seizure onset . The traditional concepts of the epileptogenic zone and the seizure onset zone do not fully account for this . The understanding and treatment , of focal-onset seizures might benefit from further clinical and computational studies of seizure onset mechanisms on multiple spatial scales .
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According to the WHO fact sheet , epilepsy is a neurological disorder affecting about 50 million people worldwide . Even today 30% of epilepsy patients do not respond well to drug therapies . Neocortical focal epilepsy is a particular type of epilepsy in which drug treatments fail and surgical success rate is low . Hence , research is essential to improve the treatment of this type of epilepsy . Recent advances in brain recording methods have led to new observations regarding the nature of neocortical focal epilepsy . However , some of the observations appear to be contradictory . Here , we develop a computational modelling framework that can explain the different observations as different aspects of possible mechanisms that can all lead to seizure onset . Specifically , we classify three main conditions under which focal seizure onset can happen . This classification is clinically important , as our model predicts different treatment strategies for each class . We conclude that focal seizures are diverse , not only in their electrographic appearance and aetiology , but also in their onset mechanism . Combined multiscale recordings as well as stimulation studies are required to elucidate the onset mechanism in each patient . Our work provides the first classification of possible onset mechanism .
|
[
"Abstract",
"Introduction",
"Model",
"Results",
"Discussion"
] |
[
"epilepsy",
"medicine",
"and",
"health",
"sciences",
"computational",
"neuroscience",
"neurology",
"biology",
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"life",
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2014
|
Dynamic Mechanisms of Neocortical Focal Seizure Onset
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Proteins containing DUF59 domains have roles in iron-sulfur ( FeS ) cluster assembly and are widespread throughout Eukarya , Bacteria , and Archaea . However , the function ( s ) of this domain is unknown . Staphylococcus aureus SufT is composed solely of a DUF59 domain . We noted that sufT is often co-localized with sufBC , which encode for the Suf FeS cluster biosynthetic machinery . Phylogenetic analyses indicated that sufT was recruited to the suf operon , suggesting a role for SufT in FeS cluster assembly . A S . aureus ΔsufT mutant was defective in the assembly of FeS proteins . The DUF59 protein Rv1466 from Mycobacterium tuberculosis partially corrected the phenotypes of a ΔsufT mutant , consistent with a widespread role for DUF59 in FeS protein maturation . SufT was dispensable for FeS protein maturation during conditions that imposed a low cellular demand for FeS cluster assembly . In contrast , the role of SufT was maximal during conditions imposing a high demand for FeS cluster assembly . SufT was not involved in the repair of FeS clusters damaged by reactive oxygen species or in the physical protection of FeS clusters from oxidants . Nfu is a FeS cluster carrier and nfu displayed synergy with sufT . Furthermore , introduction of nfu upon a multicopy plasmid partially corrected the phenotypes of the ΔsufT mutant . Biofilm formation and exoprotein production are critical for S . aureus pathogenesis and vancomycin is a drug of last-resort to treat staphylococcal infections . Defective FeS protein maturation resulted in increased biofilm formation , decreased production of exoproteins , increased resistance to vancomycin , and the appearance of phenotypes consistent with vancomycin-intermediate resistant S . aureus . We propose that SufT , and by extension the DUF59 domain , is an accessory factor that functions in the maturation of FeS proteins . In S . aureus , the involvement of SufT is maximal during conditions of high demand for FeS proteins .
Iron ( Fe ) is an essential nutrient for nearly all organisms . Fe is acquired from the environment and is transported into cells using specific uptake systems . Studies have shown that ~80% of the intracellular Fe is located in inorganic cofactors , called iron-sulfur ( FeS ) clusters , and heme in a respiring microorganism [1] . The metabolisms of most organisms are highly reliant on FeS cluster chemistry and a failure to properly assemble FeS clusters in proteins can result in widespread metabolic disorders , metabolic paralysis , and cell death [2 , 3 , 4] . FeS proteins function in diverse metabolic processes including environmental sensing[5] , carbon transformations [6] , DNA repair and replication [7 , 8] , RNA metabolism [9] , protein synthesis [10] , nucleotide , vitamin , and cofactor synthesis [11 , 12 , 13] , and cellular respiration [14 , 15 , 16] . FeS clusters are typically found in proteins as [Fe2S2] or [Fe4S4] clusters , but the use of complex FeS clusters has evolved for processes such as dinitrogen [17] , carbon monoxide [18] , and hydrogen metabolism [19] . Iron and sulfur ( S ) ions are often toxic to cells resulting in the evolution of tightly controlled mechanisms to synthesize FeS clusters from their monoatomic precursors [20 , 21] . Three FeS cluster biosynthetic systems ( Nif , Suf , and Isc ) have been described in Bacteria and Archaea for the synthesis of [Fe2S2] and [Fe4S4] clusters [22 , 23 , 24] . Bioinformatic analyses suggest that the Suf system is the most prevalent machinery in Bacteria and Archaea and perhaps the most ancient [25] . The Suf , Nif , and Isc systems utilize a common strategy to synthesize FeS clusters . First , sulfur is mobilized from free cysteine ( typically ) , using a cysteine desulfurase enzyme and subsequently transferred to either a sulfur carrier molecule ( SufU or SufE ) or directly to the synthesis machinery [24 , 26 , 27] . Monoatomic iron and sulfur , along with electrons , are combined upon a molecular scaffolding protein ( SufBD in S . aureus ) to form an FeS cluster [28] . The FeS cluster can be transferred directly from the scaffold to a target apo-protein or it can be transferred to a carrier molecule that subsequently traffics the cluster to a target apo-protein and facilitates maturation of the holo-protein [29] . Nfu and SufA serve as FeS cluster carriers in Staphylococcus aureus [4 , 30] . Nfu is necessary for virulence in models of infection [4 , 31] Most studies on bacterial FeS cluster assembly have been conducted using Escherichia coli and Azotobacter vinelandii . E . coli encodes for both the Suf and Isc systems [22] whereas A . vinelandii encodes for the Isc and Nif systems [32] . In contrast , few studies have been conducted on FeS cluster assembly in gram-positive bacteria such as Bacillus subtilis or S . aureus , which encode for only the Suf system [4 , 27] . Recent findings suggest that SufCDSUB are essential for S . aureus viability , confirming that Suf is the sole FeS cluster biosynthetic machinery used under laboratory growth conditions [4 , 33 , 34] . Dioxygen can accept electrons from cellular factors resulting in the spontaneous generation of reactive oxygen species ( ROS ) such as hydrogen peroxide ( H2O2 ) and superoxide [35 , 36 , 37] . FeS clusters are among the primary cellular targets of H2O2 and superoxide [38 , 39] . ROS readily oxidize solvent exposed [Fe4S4]2+ cofactors of enzymes such as aconitase ( AcnA ) [38 , 39] . Oxidation results in conversion to an inactive [Fe3S4]1+ cluster that can be repaired back to the active [Fe4S4]2+ state using Fe2+ and an electron [40] . Studies have implicated roles for cysteine desulfurase ( IscS ) and the putative Fe donors CyaY , YtfE , and YggX in the repair of oxidized clusters [40 , 41 , 42] . Cells also employ mechanisms to physically protect FeS clusters . The Shethna protein shields the FeS cofactor of dinitrogen reductase from dioxygen exposure [43] . Alternatively , protein domains can be situated in a manner that prevents oxidants from interacting with the FeS cluster . The pyruvate:ferredoxin oxidoreductase ( PFOR ) from Desulfovibrio africanus was found to have greater stability in the presence of dioxygen , relative to alternate PFOR enzymes , due to the presence of a domain that prevents the interaction of oxidants with its [Fe4S4]2+ cluster [44] . We have identified an open reading frame ( ORF ) in S . aureus that is often associated with the suf operon in a number of bacterial and archaeal genomes . The ORF ( SAUSA300_0875 ) encodes for a protein composed solely of a DUF59 domain and is annotated as SufT since it is often found in operons with a cysteine desulfurase ( i . e . SufS ) [45] . In eukaryotic cells , the CIA2 ( also identified as Fam96a/b or AE7 ) FeS cluster assembly factor ( s ) contain a DUF59 domain [46 , 47] . CIA2a and CIA2b act downstream of the cytosolic iron-sulfur assembly ( CIA ) machinery and are required for the maturation of FeS cluster proteins . A DUF59 domain is also present in the Arabidopsis thaliana chloroplast FeS cluster carrier , HCF101 , which is required for photosystem I maturation [48] . S . aureus is a leading cause of human infectious disease related morbidity and mortality worldwide . S . aureus forms surface associated communities referred to as biofilms that are critical for S . aureus pathogenesis and biofilm associated cells serve as the etiologic agents of recurrent staphylococcal infections ( reviewed here [49] ) . S . aureus also secretes a variety of toxins and enzymes into its extracellular milleu that are critical for biofilm formation , host colonization , nutrient acquisition and survival in the human host ( reviewed here [50] ) . About 60% of the secretome consists of peptide toxins ( phenol soluble modulins ( PSM's ) , which have multiple key roles in pathogenesis [51 , 52] . Since the 1990s the proportion of infections caused by community-associated methicillin resistant S . aureus ( CA-MRSA ) has been steadily increasing and has now reached near epidemic levels [53] . Vancomycin is a glycopeptide antibiotic that has traditionally been regarded as a last-resort drug for the treatment of MRSA infections [54] . Strains have recently emerged that display intermediate ( vancomycin intermediate-resistant S . aureus; VISA ) or high ( vancomycin resistant S . aureus; VRSA ) levels of resistance towards vancomycin [54 , 55] . Among the characteristics of VISA strains are decreased activity of peptidoglycan hydrolases and alterations in their cell wall that results in increased resistance to the lytic enzyme lysostaphin [55] . S . aureus provides an excellent model to assess the role of the DUF59 domain ( SufT ) in cellular physiology . In this report we present phylogenetic analyses indicating a widespread distribution for SufT and conservation of SufT homologs in bacterial and archaeal taxa that utilize the Suf system . These analyses also suggest that sufT was recruited to the neighborhood of sufBC over evolutionary time and for the most part retained . The bioinformatic analyses led us to hypothesize that SufT has a role in the maturation of FeS proteins . Results demonstrate an involvement of SufT in the maturation of FeS proteins during conditions imposing a high demand for FeS proteins . Moreover , epistasis studies show that the nfu and sufT mutations display synergy and the introduction of nfu in multicopy partially corrects the phenotypes of a sufT mutant . Deficiencies in the maturation of FeS proteins also result in increased biofilm formation , decreased exoprotein production , and the appearance of phenotypes consistent with vancomycin-intermediate resistant S . aureus ( VISA ) . We propose that SufT functions as an auxiliary factor for the maturation of FeS proteins with maximum usage during conditions of high FeS cofactor demand .
Of the 1669 complete genome sequences available as of October 2011 and compiled as part of our previously published work on the evolution of Suf [25] , 1092 ( 65 . 4% of total ) encoded for SufBC . Among these genomes , 761 ( 69 . 7% of total ) encoded for SufT . Of the 1669 genomes , 68 genomes contained sufT , but not sufBC . Five genomes contained sufT , but not sufB , iscU , or nifU , which encode for FeS cluster scaffolding molecules . These genomes were all from lactobacilli and the sufT homologues are in apparent operons with the genes encoding for either anaerobic ribonucleoside-triphosphate activating enzyme or serine dehydratase , which are FeS cluster-requiring enzymes [11 , 56] . Among the 761 genomes that encoded for sufT and sufBC , 374 of the sufT homologs were localized with sufBC ( suf operon associated ) and 387 sufT homologs were not associated with sufBC ( non-suf operon associated ) . Maximum likelihood phylogenetic reconstructions of SufT ( unrooted ) and SufBC ( rooted ) , followed by overlays of suf-operon associated and non-suf operon associated sufT , indicate that sufT has been recruited to and lost from the suf operon multiple times during its evolutionary history ( Fig 1 ) . However , the overall trend appears to be retainment once sufT was recruited to the suf operon . Mapping of the association of sufT with the suf operon on the SufBC tree indicates that sufT was not associated with the suf operon early during the evolution of taxa that used the Suf FeS cluster biosynthetic system and that it was recruited to the operon recently in its evolutionary history . Each SufT homolog identified contained a conserved cysteine residue , which was previously shown to be hyper-reactive [57] , but described FeS cluster-binding motifs were not recognized . Of the total ( n = 761 ) identified SufT homologs , the predominant structure contained only the DUF59 domain ( S1 architecture; ex . S . aureus SufT ) , but 198 encoded for additional N- and C-terminal motifs represented by nine primary modular structures ( Fig 2A ) . The most prevalent modular structure was the S2 architecture ( n = 88 ) , with a N-terminal motif that did not display homology to previously described domains . SufT within the S5 architecture ( n = 5 ) contained a N-terminal domain with homology to U-type FeS cluster scaffolds while SufT within the S7 architecture ( n = 3 ) harbored a N-terminal domain with homology to Rieske iron-oxygenase ferredoxins . Finally , SufT within the S9 architecture ( n = 1 ) contained a N-terminal domain with homology to serine acetyltransferases ( CysE ) . Characterization of the C-terminal motifs also revealed variation that was represented in four unique modular structures . These were characterized as SufT with C-terminal domains that have homology to PaaJ or acetyl-CoA acetyltransferase domains ( S3 architecture , n = 75 ) , P-loop NTPase domains ( S4 architecture n = 20 ) , DUF1858 domains ( S6 architecture , n = 4 ) and co-enzyme pyrroloquinoline quinone synthesis protein D ( PqqD ) domains ( S8 architecture , n = 2 ) . The S2-S9 architectures were mapped on the phylogenetic reconstruction of core DUF59 ( N- and C-terminal motifs were pruned from alignment block ) in order to determine if the modules are randomly distributed over the tree or if they are phylogenetically clustered . The overall pattern of clustering of the modular structures on the tree ( Fig 2B ) indicates that once these modules were fused to an ancestor of a given DUF59 containing protein , they were largely retained . This suggests that the N- and C-terminal motifs , and presumably their functionalities , are under strong selective pressure . We created and characterized a S . aureus ΔsufT mutant to test whether SufT has a role in the maturation of FeS proteins . A S . aureus ΔacnA strain is defective in utilizing glutamate as a source of carbon ( S1A Fig ) [58 , 59] . Nfu has a role in the maturation of AcnA in S . aureus [4] . The Δnfu and ΔsufT strains displayed growth defects in chemically defined media supplemented with glutamate as a carbon source ( hereafter 20AA glutamate medium ) ( Fig 3A ) , but the defect of the ΔsufT strain was less severe than that of the Δnfu strain . The WT , Δnfu , and ΔsufT strains had similar growth profiles in defined medium containing glucose as a carbon source ( hereafter 20 AA glucose medium ) ( S1B Fig ) . AcnA activity was assessed in the WT , ΔsufT , and Δnfu strains across growth . AcnA activity was decreased in strains lacking Nfu or SufT ( Fig 3B ) . The decreased AcnA activity in the ΔsufT strain could arise due to one of four scenarios: 1 ) decreased transcription of acnA , 2 ) decreased abundance of AcnA , 3 ) decreased occupancy of the [Fe4S4] cluster upon AcnA due to the decreased transcription of genes encoding FeS cluster biogenesis factors , or 4 ) decreased cluster occupancy upon AcnA due to the absence of SufT . Transcriptional activity of acnA was increased in the ΔsufT strain ( S2 Fig ) . This suggested that decreased AcnA activity in the ΔsufT strain was not the result of altered acnA transcription ( S2 Fig ) . We constructed acnA::TN strains containing a plasmid with a acnA_FLAG allele under the transcriptional control of a xylose inducible promoter ( pacnA ) . Introduction of pacnA allows for the control of acnA transcription and the simultaneous determination of AcnA_FLAG abundance . The acnA::TN ΔsufT strain was genetically complemented by re-introduction of the sufT allele at a secondary chromosomal location ( sufT+ ) . AcnA activity and AcnA abundance was assessed in the acnA::TN , acnA::TN ΔsufT , and acnA::TN ΔsufT sufT+ strains containing pacnA . AcnA activity was ~2-fold lower in the acnA::TN ΔsufT strain compared to the acnA::TN when activity was normalized to AcnA abundance in the same cell-free lysates ( Fig 3C ) . This phenotype was genetically complemented . Suf is encoded by the sufCDSUB operon in S . aureus . The transcriptional activity of sufC was increased ( ~2-fold ) in the Δnfu strain and mildly , but consistently , increased in the ΔsufT strain ( Fig 3D ) . Similar results were obtained in exponential and stationary growth . From Fig 3 we concluded that the absence of SufT results in decreased occupancy of the [Fe4S4] cofactor upon AcnA . Synthesis of the branched chain amino acids ( BCAA ) leucine and isoleucine requires the FeS cluster containing dehydratase enzymes isopropylmalate isomerase ( LeuCD ) and dihydroxyacid dehydratase ( IlvD ) , respectively [60 , 61] . Strains lacking either SufT or Nfu displayed growth defects in defined medium lacking leucine ( Leu ) and isoleucine ( Ile ) ( hereafter 18AA glucose medium ) ( Fig 4A ) , but displayed a growth profile similar to WT in 20AA glucose medium ( S1B Fig ) . We constructed leuC::TN , leuC::TN ΔsufT , leuC::TN ΔsufT sufT+ , ilvD::TN , ilvD::TN ΔsufT , and the ilvD::TN ΔsufT sufT+ strains carrying plasmids with either leuCD or ilvD under the transcriptional control of a xylose inducible promoter ( pleuCD and pilvD ) . The activities of LeuCD and IlvD were decreased in strains lacking SufT and these defects were restored by genetic complementation ( Fig 4B and 4C ) . We concluded that SufT is utilized in the maturation of multiple FeS cluster requiring enzymes . Staphylococcus aureus is a facultative anaerobe and can respire upon dioxygen or nitrate as terminal electron acceptors or grow fermentatively [62] . The acnA::TN and acnA::TN ΔsufT strains containing pacnA were cultured aerobically , as well as anaerobically in the presence or absence of nitrate before determining AcnA activity . The ΔsufT mutant had lower AcnA activity during respiratory growth , but AcnA activity was restored during fermentative growth ( Fig 5A ) . Microaerobic conditions also mitigated the growth defect of both the Δnfu and ΔsufT strains in 18AA glucose medium ( S3 Fig ) . Fermentative growth imposes a decreased demand for FeS clusters [63] . By inference , fermentative growth should result in decreased transcription of genes encoding for FeS assembly factors . Consistent with this prediction , the transcriptional activities of sufT , nfu , and sufC decreased when aerobically cultured cells were shifted to an anaerobic ( fermentative ) environment ( Fig 5B ) . We examined whether SufT functions to protect the AcnA FeS cluster via physical exclusion of dioxygen . Cell-free lysates were generated from the acnA::TN and acnA::TN ΔsufT strains containing pacnA . AcnA activity was assessed at periodic intervals before and after exposure of lysates to dioxygen . Dioxygen exposure resulted in decreased AcnA activity in both the parent and ΔsufT mutant ( Fig 5C ) , but the rate of decrease was statistically indistinguishable between the strains . Fermentatively cultured cells exposed to dioxygen ( reaeration ) increased sufC transcription suggesting that the resumption of respiratory processes results in an increased demand for FeS clusters ( Fig 6A and [4] ) . The transcription of sufT was also increased ( ~2 . 5-fold ) upon reaeration ( Fig 6A ) . The role of SufT in the maturation of AcnA upon reaeration was assessed . The acnA::TN and acnA::TN ΔsufT strains containing pacnA were cultured fermentatively before one set of the cultures was exposed to dioxygen while the other set was incubated anaerobically ( as previously described [40] ) . AcnA activity increased by ~30% in the parental strain upon dioxygen introduction ( Fig 6B ) . In contrast , AcnA activity decreased by ~20% in the ΔsufT mutant . The use of protein synthesis inhibitors allowed for the conclusion that the increased AcnA activity in the parental strain upon reaeration was due to de novo protein synthesis . These findings led to the conclusion that SufT has a role in FeS cluster assembly in cells attempting to resume respiratory processes , and thereby facilitates the adaptation of cells to shifts in dioxygen tensions . Reactive univalent species can damage or destroy solvent exposed FeS clusters [4 , 38 , 39] . We found that the ΔsufT , and sodA::TN ( encoding for the dominant aerobic superoxide dismutase [64] ) strains displayed decreased growth in the presence of paraquat , a redox cycling molecule that leads to increased accumulation of intracellular ROS ( Fig 7A ) . However , the phenotype of the ΔsufT mutant was less severe than that of the sodA::TN strain . The acnA::TN and acnA::TN ΔsufT strains containing pacnA were cultured , challenged with paraquat , and AcnA activity was determined . Challenging cells with paraquat resulted in ~15% and ~45% decrease in AcnA activity in the parent and ΔsufT mutant , respectively ( Fig 7B ) . The alkylhydroperoxidase system ( Ahp ) functions as an intracellular H2O2 scavenger and a S . aureus strain lacking Ahp accumulates intracellular ROS [4 , 65] . AcnA activity was assessed in the WT , ΔsufT , ahp::TN , and ahp::TN ΔsufT strains . AcnA activity was decreased ~25–30% in both the ahp and sufT strains and by ~75% in the ahp sufT double mutant strain ( Fig 7C ) . Four explanations could underlie the decreased AcnA activity observed in a ΔsufT strain upon ROS toxification: 1 ) the ΔsufT strain has decreased activities of ROS scavenging enzymes , 2 ) SufT is necessary for the repair of FeS clusters inactivated by ROS oxidation , 3 ) SufT is involved in physically shielding and/or excluding ROS from the enzyme active site and preventing damage , or 4 ) there is an increased need for SufT in FeS cluster assembly . The activities of the ROS scavenging enzymes catalase ( Kat ) and superoxide dismutase ( Sod ) were similar in the WT and ΔsufT strains across growth ( Fig 7D , S4 Fig ) . The acnA::TN and acnA::TN ΔsufT strains containing pacnA also displayed similar levels of Sod activity , both before and after paraquat treatment ( S5 Fig ) . We examined whether SufT is capable of physically shielding FeS clusters from univalent oxidants [43 , 44] . Cell-free lysates from the acnA::TN and acnA::TN ΔsufT strains containing pacnA were exposed to varying concentrations of H2O2 and AcnA activity was determined one minute post treatment . AcnA activity decreased with increasing H2O2 concentrations , but the decrease in AcnA activity was similar in the parent and ΔsufT mutant ( Fig 7E ) . Brief exposure to H2O2 can convert the active [Fe4S4]2+ cluster in AcnA into the inactive [Fe3S4]1+ cluster . This can be repaired to the [Fe4S4]2+ state by Fe2+ and an electron [40] . Cell-free lysates from the acnA::TN and acnA::TN ΔsufT strains containing pacnA were exposed to H2O2 . One-minute post challenge , the stress was terminated and reactivation of AcnA activity by factors in the lysate was monitored over-time . The rate of AcnA reactivation was similar in the parent and ΔsufT mutant ( Fig 7F ) . From Fig 7 we concluded that SufT is involved in the de novo assembly of FeS clusters in cells experiencing ROS stress . The phenotypic abnormalities of the ΔsufT mutant were exacerbated during respiration , during resumption of respiration in fermenting cells , and upon ROS challenge ( i . e . conditions imposing a high demand for FeS assembly ) . The transcription of core genes required for FeS assembly increased upon challenge with ROS or resumption of respiration [4] . We tested the hypothesis that SufT is required for FeS cluster assembly during conditions imposing a high demand for FeS clusters . Growth was monitored in either 20AA glutamate medium , or defined medium containing glutamate as a carbon source and lacking leucine ( Leu ) and isoleucine ( Ile ) ( hereafter 18AA glutamate medium ) . Growth in 18AA glutamate medium would impose a simultaneous requirement for the AcnA , LeuCD , and IlvD enzymes , and by inference , exert an increased requirement for FeS clusters . The ΔsufT strain displayed a growth defect in 20AA glutamate medium ( similar to Fig 3A; however the magnitude appears lower here due to the scale ) and this defect was exacerbated upon culture in 18AA glutamate medium ( Fig 8A ) . The acnA::TN and acnA::TN ΔsufT strains containing pacnA were cultured in the presence or absence of varying concentrations of xylose followed by assessing AcnA activity . The difference in AcnA activity between the parent and ΔsufT mutant increased in synchrony with increasing inducer concentrations ( Fig 8B and 8C ) . We next monitored sufT transcriptional activity with respect to the demand for FeS clusters using the acnA::TN strain carrying pacnA , as well as the sufT transcriptional reporter . The transcriptional activity of sufT increased in synchrony with increasing inducer concentrations ( Fig 8D ) . Mycobacterium tuberculosis contains a DUF59 containing protein ( Rv1466 ) that is part of the suf operon and is essential for viability ( Fig 1C and [66] ) . We examined whether Rv1466 could compensate for the loss of SufT in S . aureus . Rv1466 has a ~20 amino acid N-terminal extension when compared to the S . aureus SufT . Codon-optimized rv1466 and a truncated version of rv1466 ( trunc_rv1466 ) were introduced upon a multi-copy plasmid into the S . aureus ΔsufT strain and phenotypes were examined . The presence of trunc_rv1466 , but not rv1466 , rescued the growth defect of the ΔsufT strain in 18AA glutamate medium ( Fig 9A ) . The presence of trunc_rv1466 , but not rv1466 , displayed a dominant effect and inhibited growth of the ΔsufT strain in 20AA glucose medium ( Fig 9B ) . Epistatic relationships between sufT , nfu , and sufA were investigated by phenotypically examining mutant strains lacking one , two , or all three maturation factors . The ΔsufA strain did not display a defect in AcnA activity , relative to the WT strain , and the ΔsufA ΔsufT double mutant phenocopied the ΔsufT strain ( Fig 10A ) . The phenotypic effects of the ΔsufA and Δnfu mutations displayed an additive effect . AcnA activity in the Δnfu mutant was ~65% of WT while the activity in the ΔsufA ΔsufT double mutant was ~50% . AcnA activity was near the limit of detection in the Δnfu ΔsufT double mutant ( ~2% ) . The Δnfu ΔsufT ΔsufA triple mutant had AcnA activity similar to the Δnfu ΔsufT strain . AcnA activity in the acnA::TN Δnfu ΔsufT strain containing pacnA was also nearly undetectable relative to its isogenic parental strains ( Fig 10B ) . This suggested that the low AcnA activity in the Δnfu ΔsufT strain was not solely the outcome of decreased acnA transcription . Growth was examined in media that impose varying demands for FeS proteins ( 20AA glucose , 20AA glutamate , or 18AA glutamate media ) . The ΔsufA strain did not display a growth deficiency in any of the media examined ( Fig 10C–10E ) . The ΔsufA ΔsufT double mutant phenocopied the ΔsufT strain in 20AA glucose and 20AA glutamate medium , but the effects of the mutations were additive in 18AA glutamate medium . The Δnfu ΔsufA double mutant phenocopied the Δnfu strain in 20AA glucose and 20AA glutamate media , but the effect of the mutations were additive in 18AA glutamate medium . The phenotypes of the Δnfu and ΔsufT mutations displayed synergism . The Δnfu ΔsufT double mutant displayed a severe growth defect in each media examined . The Δnfu ΔsufT ΔsufA triple mutant strain largely phenocopied the Δnfu ΔsufT strain in each media . The Δnfu ΔsufT double mutant also displayed severe growth defects in complex medium . Growth of S . aureus in tryptic soy broth ( TSB ) results in the consumption of glucose , the release of fermentative byproducts such as acetate , and acidification of the medium [67 , 68] followed by the uptake of the fermentative byproducts resulting in alkalization of the growth medium . Therefore , the pH and acetate profile of the spent medium correlates with the cells ability to uptake and utilize fermentation products [67 , 68 , 69] . We monitored optical densities , pH of the spent medium , and acetate concentrations in the spent medium over time in cultures of the WT , ΔacnA , Δnfu , ΔsufT , and Δnfu ΔsufT strains . The Δnfu ΔsufT double mutant and ΔacnA strains displayed pronounced differences during post-exponential growth reaching lower final optical densities ( S6A Fig ) . The pH of the medium from the Δnfu ΔsufT and ΔacnA mutants did not re-alkalinize ( S6B Fig ) nor was acetate utilized ( S6C Fig ) . The interactions amongst sufT , nfu , and sufA were further examined by introducing each gene upon a multi-copy plasmid ( psufT , pnfu and psufA , respectively ) and assessing whether they impart phenotypic suppression to the ΔsufT or Δnfu strains . The ΔsufA strain did not have decreased AcnA activity , and therefore , suppression was not examined in this strain . The presence of psufA appeared to increase AcnA activity mildly in both the WT and ΔsufT strains , but a statistically significant phenotypic rescue was not observed ( S7A Fig ) . AcnA activity decreased in the Δnfu strain carrying psufA . AcnA activity was increased in the ΔsufT strain carrying pnfu ( increase of ~250% ) , while the presence of pnfu had little effect on AcnA activity in the WT ( Fig 11A ) . The presence of psufT slightly decreased AcnA activity in the WT , while it did not alter AcnA activity in the Δnfu strain ( S7B Fig ) . Growth profiles of the WT and ΔsufT strains carrying empty vector or pnfu were examined in 20AA glutamate medium . The presence of pnfu partially mitigated the growth defect of the ΔsufT strain in 20AA glutamate medium ( S8 Fig ) . The phenotypes of the ΔsufT strain were mitigated during fermentative growth , which imposes a low demand for FeS clusters . We reasoned that Nfu is utilized to fulfill the demand for FeS cluster assembly in the ΔsufT strain during fermentative growth . After fermentative culture the acnA::TN Δnfu ΔsufT strain containing pacnA displayed levels of AcnA activity that were near the limit of detection ( ~2% ) , whereas the acnA::TN ΔsufT and acnA::TN Δnfu strains had AcnA activity similar to the parent ( Fig 11B ) . Microaerobic growth in 18 AA glucose medium was also examined . The Δnfu and ΔsufT strains displayed growth profiles that did not significantly deviate from that of the WT ( Fig 11C ) . However , the Δnfu ΔsufT double mutant displayed a large growth defect . From Figs 10 and 11 , S7 and S8 Figs , we concluded that 1 ) the phenotypic effects of the nfu and sufT mutations are synergistic , 2 ) overproduction of nfu partially alleviates the phenotypes of the ΔsufT strain , and 3 ) either Nfu or SufT is sufficient for AcnA maturation during fermentative growth . Biofilm formation and exoprotein production were assessed in strains lacking FeS cluster assembly factors . Agr is the dominant activator for transcription of exoproteins and toxins , as well as the phenol soluble modulins ( PSMs ) . Therefore , an Δagr strain was included as a positive control [51] . A strain lacking AcnA has been proposed to have increased Agr activity [70] . Since a Δnfu ΔsufT strain phenocopied the acnA::TN mutant , the acnA::TN strain was also examined . Exoproteins were extracted from the spent medium supernatant and analyzed using SDS-PAGE . S . aureus encodes for eight PSMs that are small peptides comprising ~60% of the total exoproteome and are visualized on SDS-PAGE as one band [51] . The Δnfu ΔsufT , Δnfu ΔsufT ΔsufA , and the Δagr strains were deficient in exoprotein production ( Fig 12A ) . For ease of comparative analyses , only the band corresponding to PSMs is displayed . Static growth of WT in TSB does not induce biofilm formation , and therefore , biofilm formation was examined in biofilm inducing medium ( Fig 12B and 12C , [71] ) . Biofilm formation was also assessed in strains lacking Agr and SigB , which negatively and positively influence biofilm formation , respectively [72 , 73] . Strains deficient in the maturation of FeS proteins displayed varying degrees of biofilm formation . The Δnfu ΔsufT double mutant displayed the largest increase in biofilm formation ( ~4 . 5 fold ) . The acnA::TN strain formed biofilms at a similar extent as the WT ( Fig 12B and 12C ) . We examined vancomycin sensitivities of strains lacking FeS cluster assembly factors . The Δnfu ΔsufT double mutant displayed a large increase in resistance towards vancomycin during growth ( Fig 13A ) . In growth inhibition curves we found that the Δnfu ΔsufT strain was not completely resistant towards vancomycin , but rather , it displayed an inhibition response more characteristic of vancomycin-intermediate resistant Staphylococcus aureus ( VISA ) ( S9 Fig and [74] ) . Vancomycin resistant strains display alterations in their cell walls resulting in increased resistance towards lysis by lysostaphin [55 , 74] . The Δnfu ΔsufT double mutant displayed the greatest resistance towards lysis by lysostaphin ( Fig 13B ) . Decreased activity of peptidoglycan hydrolases is a hallmark of VISA strains [55 , 74] . Peptidoglycan hydrolase activity was monitored using zymographic analysis upon heat-killed WT cells as a substrate . The Δnfu ΔsufT double mutant displayed the largest alterations in the activities of peptidoglycan hydrolases ( Fig 13C ) .
Staphylococcus aureus SufT is composed solely of a DUF59 domain . Alternate proteins containing DUF59 domains participate in FeS cluster assembly , but the function ( s ) of the DUF59 domain itself has not been described [46 , 47 , 48] . The goals of this study were to determine if SufT has a role in FeS cluster assembly , and if so , begin to dissect its in vivo functional role . Phylogenetic analyses found that sufT was recruited to the same chromosomal location as sufBC , and once recruited , it was largely retained . These findings suggested that sufT was recruited to the operon to refine the functionality of Suf-mediated FeS cluster assembly . Amongst the genomes analyzed , only five organisms encoded for SufT , but not the FeS cluster scaffolding proteins SufB , IscU , or NifU . The five organisms identified were lactobacilli and within these genomes the SufT homolog was located within apparent operons that encode known FeS cluster requiring proteins . The informatics and phylogenetic findings strongly suggested a role for SufT in FeS cluster assembly . The S . aureus ΔsufT strain displayed physiological abnormalities consistent with SufT having a role in the maturation of FeS proteins . Further , the phenotypes of the S . aureus ΔsufT strain closely resembled those of a strain lacking the FeS cluster carrier Nfu [4] . Aside from a role in de novo FeS cluster assembly , alternate possibilities for the observed deficiencies manifest in the ΔsufT strain were considered . The ΔsufT strain did not have altered H2O2 or superoxide scavenging activities . SufT was not required for the physical exclusion of H2O2 from the AcnA active site or the repair of the H2O2 damaged FeS cluster upon AcnA . These findings suggested that SufT likely functions in the de novo assembly of FeS clusters upon apo-proteins . Genes encoding for proteins with functional overlap often display synergistic ( superadditive ) phenotypic effects when the gene products are absent or non-functional [75] . The phenotypes associated with nfu and sufT were synergistic . This was most evident during fermentative growth where there is a lower demand for FeS clusters . The phenotypes of the Δnfu and ΔsufT strains were nearly indistinguishable from the WT strain , but the Δnfu ΔsufT double mutant displayed a large growth defect and exhibited AcnA activity near the limit of detection . Introduction of nfu in multicopy to the ΔsufT strain led to partial mitigation of the phenotypes of this strain . Taken together , these findings led to the conclusion that both SufT and Nfu function as non-essential , accessory factors in the maturation of FeS proteins . Lending further support to this conclusion , subsequent to our informatics analyses , the genome of Oligotropha carboxidovorans was sequenced and found to encode for a protein consisting of a fusion of the N-terminus of Nfu and SufT ( Locus tag: OCA5_c02770 ) . SufT , Nfu , and SufA are auxiliary FeS cluster maturation factors leading to the question of why S . aureus encodes for three such factors . The simplest explanations are that there is a degree of specificity for each auxiliary factor with respect to their target apo-proteins or that they have different functions . Vinella et al . have recently proposed an expanded model , which visualizes a dynamic cellular network of proteins that varies with growth stage or growth condition allowing for rapid calibration to alterations in the cellular demand for FeS protein maturation [76] . During such a scenario , certain auxiliary proteins and pathways would be preferred during normal growth and alternate auxiliary proteins and pathways during stress conditions . The findings presented herein are consistent with the model proposed by Vinella et al . [76] . During routine aerobic growth , Nfu was the dominant auxiliary factor required for the maturation of AcnA . However , upon the overproduction of AcnA , the need for SufT for AcnA maturation was increased . The cellular need for SufT was also increased when cells were resuming respiration , toxified with ROS , or grown in 18AA glutamate medium; three conditions that impose a high demand for de novo FeS cluster assembly . The transcriptional activity of sufT also increased as the cellular demand for FeS clusters increased . These findings lend strong support to a model wherein SufT is a dominant factor involved in the maturation of FeS proteins in cells experiencing a high demand for FeS clusters . The epistasis experiments further strengthen the idea that certain accessory proteins are preferentially utilized when confronted with a high demand for FeS clusters . SufA was dispensable for growth under all conditions tested . However , SufA dependent phenotypes were manifest in strains lacking either Nfu or SufT and simultaneously cultured upon a medium imposing a high demand for FeS proteins . Therefore , we propose that SufA facilitates FeS protein maturation in S . aureus under conditions imposing a very high demand for FeS clusters . It is tempting to speculate that cells encode for multiple accessory maturation factors to respond to a gradation of demand for FeS cluster assembly , however , this awaits further experimentation . It is currently unclear what genetic or biochemical elements dictate the increased usage of SufT or SufA upon increased FeS cofactor demand . Possible explanations include different functionalities , increased stability of a particular factor under stress conditions , or an increased rate of FeS cluster synthesis or FeS protein maturation under select cellular conditions . A similar scenario has been described to exist between the Suf and Isc FeS cluster biosynthetic machineries . In Escherichia coli , Suf is preferred under ROS stress and Fe limiting conditions , whereas Isc is the preferred FeS assembly system during conditions imposed by routine laboratory cultivation [77 , 78] . What is the role of SufT in FeS cluster assembly ? The genetic findings presented make it tempting to speculate that SufT functions in the carriage of FeS clusters , but further biochemical analyses will be necessary to make this conclusion . It also worth noting that the SufT homologues analyzed in Fig 1 contain only one strictly conserved cysteine residue . With the exception of monothiol glutaredoxins , described FeS cluster carriers contain two or more cysteines utilized in FeS cluster ligation [79] . Biofilm formation and exoprotein production are critical in the infectious lifecycle of S . aureus [49 , 50] . We previously found that a strain lacking Nfu is attenuated for virulence in models of infection [4] . In this report we found that a strain that was crippled in its ability to maturate FeS proteins displayed significantly increased biofilm formation and decreased exoprotein production . Vancomycin is a last resort drug in the treatment of CA-MRSA infections and the genetic and molecular mechanisms underlying resistance to vancomycin are an active area of research [54] . Strains defective in FeS protein maturation also displayed an intermediate resistance to vancomycin and multiple phenotypes associated with VISA strains . The Δnfu ΔsufT strain phenocopied a ΔacnA strain in growth experiments , but it did not phenocopy this strain in phenotypes involved in virulence . S . aureus encodes for the FeS cluster utilizing two-component regulatory system ( TCRS ) AirSR [5] . AirSR alters the transcription of genes encoding for peptidoglycan hydrolases , as well as those required for biofilm formation [5 , 80] . AirR directly binds to the promoter region of Agr [80] . AirSR is also implicated in vancomycin resistance and a strain lacking AirSR displays VISA like phenotypes [80] . Therefore , the accumulation of apo-AirSR in the Δnfu ΔsufT strain may underlie the virulence phenotypes witnessed . An alternate explanation is that the altered Agr activity in these strains results in altered virulence phenotypes . Apart from its roles in toxin production and biofilm formation , Agr has also been implicated in modulating vancomycin resistance in S . aureus [51 , 81 , 82] . Regardless of the mechanism ( s ) underlying the phenotypes presented , these findings highlight the importance of efficient FeS cluster assembly for multiple phenotypes critical for pathogenesis and antibiotic resistance . In summary , we have identified a role for SufT , and by extension DUF59 , in the maturation of FeS proteins . We propose a model wherein SufT is an auxiliary FeS protein maturation factor whose usage is selectively increased during growth conditions necessitating increased FeS cluster assembly in S . aureus . An increased demand for FeS clusters may have been an evolutionary driving force to recruit sufT to the suf operon thereby increasing the efficiency and control of de novo FeS cluster assembly .
Restriction enzymes , quick DNA ligase kit , deoxynucleoside triphosphates , and Phusion DNA polymerase were purchased from New England Biolabs ( Ipswich , MA ) . The plasmid mini-prep kit , gel extraction kit and RNA protect were purchased from Qiagen ( Hilden , Germany ) . Lysostaphin was purchased from Ambi products ( Lawrence , NY ) . Oligonucleotides were purchased from Integrated DNA Technologies ( Coralville , IA ) and sequences are listed in S1 Table ( oligonucleotides used in this study ) . Trizol ( Life Technologies ) , High-Capacity cDNA Reverse Transcription Kits ( Life Technologies ) , and DNase I ( Ambion ) was purchased from Thermo Fisher Scientific ( Waltham , MA ) . Tryptic Soy Broth ( TSB ) was purchased from MP Biomedicals ( Santa Ana , CA ) . An acetic acid quantification kit was purchased from R-BioPharma ( Darmstadt , Germany ) . Unless specified all chemicals were purchased from Sigma-Aldrich ( St . Louis , MO ) and were of the highest purity available . Unless otherwise stated , the S . aureus strains used in this study ( listed in Table 1 ) were constructed in the S . aureus community-associated USA300 strain LAC that was cured of the native plasmid pUSA03 , which confers erythromycin resistance [83] . The USA300 LAC genome differs from USA300_FPR3757 only by a few single nucleotide polymorphisms [84 , 85] . Unless specifically mentioned , S . aureus cells were cultured as follows: 1 ) aerobic growth at a flask/tube headspace to culture medium volume ratio ( hereafter HV ratio ) of 10; 2 ) anaerobic growth at a flask/tube headspace to culture medium volume ratio of 0 , as described earlier [4]; 3 ) in 96-well microtiter plates containing 200 μL total volume ( detailed procedure below ) . Liquid cultures were grown at 37°C with shaking at 200 rpm unless otherwise indicated . Difco BioTek agar was added ( 15 g L-1 ) for solid medium . When selecting for plasmids , antibiotics were added at the final following concentrations: 150 μg mL-1 ampicillin ( Amp ) ; 30 μg mL-1 chloramphenicol ( Cm ) ; 10 μg mL-1 erythromycin ( Erm ) ; 3 μg mL-1 tetracycline ( Tet ) ; 125 μg mL-1 kanamycin ( Kan ) ; 150 ng mL-1 anhydrotetracycline ( Atet ) . For routine plasmid maintenance , liquid media were supplemented with 10 μg mL-1 or 3 . 3 μg mL-1 of chloramphenicol or erythromycin , respectively . Escherichia coli DH5α was used as a cloning host for plasmid constructions . All clones were passaged through RN4220 and transductions were conducted using phage 80α [86] . All S . aureus mutant strains and plasmids were verified using PCR or by sequencing PCR products or plasmids . All DNA sequencing was performed by Genewiz ( South Plainfield , NJ ) . Unless otherwise stated , JMB1100 chromosomal DNA was used as a template for PCR reactions . To create the ΔsufT deletion strain ( JMB1146 ) , approximately 500 base pairs upstream and downstream of sufT gene ( SAUSA300_0875 ) were amplified using PCR with primer pairs 0875up5EcoRI and 0875up3NheI; 0875dwn5MluI and 0875 dwn3BamHI ( S1 Table ) . Amplicons were gel purified and fused using PCR and the 0875up5EcoRI and 0875 dwn3BamHI primers . The resulting amplicon was gel purified , and digested with BamHI and SalI , followed by a ligation into similarly digested pJB38 resulting in pJB38_ΔsufT . The plasmid pJB38_ΔsufT was isolated and subsequently transformed into RN4220 before transducing into JMB1100 . A single colony was inoculated into 5 mL of TSB-Cm and cultured overnight at 42°C followed by plating 25 μL on TSA-Cm to select for colonies containing a single recombination event . Single colonies were inoculated into 5 mL of TSB medium and were grown overnight , followed by a dilution of 1:25 , 000 before plating 100 μL onto TSA containing Atet to select against plasmid containing cells . Colonies were screened for Cm sensitivity and for the ΔsufT mutation using PCR . The sufT::tetM strain was created by digesting the pJB38_ sufTΔ with MluI and NheI and inserting the tetM gene between the upstream and downstream regions of sufT . The DNA encoding for Tet resistance ( tetM ) was amplified using PCR with Strain JMB1432 as a template and the G+tetnheI and G+tetmluI primers before digesting and ligating into similarly digested pJB38_ΔsufT . The resulting plasmid ( pJB38_ΔsufT::tetM ) was passaged though E . coli , before it was transformed into RN4220 . The ΔsufT::tetM mutant was constructed as described above . Plasmids for genetic complementation , transcriptional analyses , and insertion of epitope tags to allow protein detection by western blots were constructed by subcloning digested PCR products into similarly digested vectors or by using yeast homologous recombination cloning ( YRC ) as previously described [87 , 88] . The pLL39_sufT and pCM28_sufT plasmids were created using the 0875_5BamHI and the 0875_3SalI primer pair . The pCM11_sufT was created using the 875gfpKpnI and 875gfpHindIII primer pair . The pCM11_acnA was made using the AcnApHindIII and AcnApKpnI primer pair . The Mycobacterium tuberculosis rv1466 was codon optimized and synthesized by Integrated DNA technologies ( IDT; Coralville , IA ) and cloned into pCM28 using the native S . aureus sufT promoter using YRC . The full-length construct was constructed using amplicons generated using the following primer pairs: pCM28YCC and Ycc875p3; ycc875p5 and 875pMT3; 875pMT5 and 875pCM28 3 . The truncated version was created using the same primers except MT875trunk5 and MT875trunk3 replaced ycc875p5 and Ycc875p3 , respectively . Growth was assessed in 200 μL cultures grown at 37°C in 96-well plates using a BioTek 808E Visible absorption spectrophotometer . Culture optical density was monitored at 630 nm . The staphylococcal-defined medium has been described previously [4] . Strains cultured overnight in TSB were inoculated into minimal medium or TSB to a final optical density ( OD ) of 0 . 025 ( A600 ) units . For assessing nutritional requirements , cultures were harvested and treated as above , except that the cell pellet was washed twice to prevent carryover of rich medium components . For aerobic growth the shake speed was set to medium . For microaerobic growth the plate was incubated statically . The four growth medium formulations utilized for nutritional analyses were: 1 ) 20AA glucose medium , containing the 20 canonical amino acids and 14 mM glucose as a source of carbon; 2 ) 18AA glucose medium , containing 18 canonical amino acids and lacking leucine and isoleucine and 14 mM glucose as a source of carbon; 3 ) 20AA glutamate medium , containing the 20 canonical amino acids and 44 mM glutamate as a source of carbon , and 4 ) 18AA glutamate medium , containing 18 canonical amino acids and lacking leucine and isoleucine and 44 mM glutamate as a source of carbon . To examine vancomycin sensitivity , cultures were inoculated into TSB in the presence or absence of varying concentrations of vancomyin ( 0 . 025–1 . 5 μg/mL ) . Growth inhibition was assessed after 4 hours of growth . Paraquat sensitivity assays were conducted upon solid tryptic soy broth agar ( TSA ) plates containing 0 or 30 mM of paraquat . Overnight cultures ( ~18 hours of growth ) were serial diluted in 1X phosphate buffered saline and 10 μL of each dilution was placed on plates of the solid medium . The plates were incubated at 37°C for 15 hours before the growth was assessed . Strains cultured overnight in TSB-Erm medium were diluted into fresh TSB-Erm medium to a final OD of 0 . 1 ( A600 ) and cultured , with shaking , at a HV ratio of 10 . At periodic intervals culture density and fluorescence were assessed as described previously [4] . Fluorescence data were normalized with respect to a strain not carrying a GFP-based transcriptional reporter to normalize for background fluorescence values . The resulting data were normalized to the culture OD . Finally for ease of comparative analyses the data were normalized relative to the wild-type ( WT ) strain , or as specified in the figure legend . Anaerobic culture conditions were achieved as described earlier [4 , 89] . Cells were cultured to exponential growth , aerobically , as described above . The cultures were then split and one set of cells was cultured at a HV ratio of zero in capped microcentrifuge tubes and anaerobiosis was verified by the addition of 0 . 001% resazurin to control tubes [4 , 89] . mRNA abundances of genes were examined from a previously described cDNA library [4] . Strains were cultured overnight in TSB and cells were harvested by centrifugation . Cell pellets were washed twice with 1X phosphate buffered saline and resuspended in lysis buffer ( recipe above ) in the presence of 5 μg/mL of lysostaphin . The lysostaphin mediated decrease in optical densities ( A600 ) was recorded periodically . Protein concentration was determined using a copper/bicinchonic acid based colorimetric assay modified for a 96-well plate ( 47 ) . Bovine serum albumin ( 2 mg/mL ) was used as a standard . Western blot analyses were conducted as described previously [4 , 88] . Strains cultured overnight in TSB ( ~18 hours ) were diluted into fresh TSB to a final OD of 0 . 1 ( A600 ) . Periodically , aliquots of the cultures were removed , optical density was determined , and the cells and culture media were partitioned by centrifugation at 14 , 000 rpm for 1 minute . Two mL of either the culture supernatant or sterile TSB , which served to provide a pH reading for the point of inoculation , were combined with 8 mL of distilled and deionized water and the pH was determined using a Fisher Scientific Accumet AB15 pH mV Meter . The concentration of acetic acid in spent media was determined using the R-Biopharm Enzymatic BioAnalysis kit following the manufacturer's suggested protocol . Biofilm formation was examined as described elsewhere , with minor changes [71 , 97] . Briefly , overnight cultures were diluted into biofilm media ( TSB supplemented with 3% NaCl and 0 . 5% glucose ) , added to the wells of a 96-well microtiter plate and incubated statically at 37°C for 22 hours . Prior to harvesting the biofilms , the optical density ( A590 ) of the cultures was determined . The plate was subsequently washed with water , biofilms were heat fixed at 60°C , and the plates and contents were allowed to cool to room temperature . The biofilms were stained with 0 . 1% crystal violet , washed with water , destained with 33% acetic acid and the absorbance of the resulting solution was recorded at 570 nm and standardized to an acetic acid blank and subsequently to the optical density of the cells upon harvest . Finally the data were normalized with respect to the WT strain to obtain relative biofilm formation . Spent medium supernatants were obtained from overnight cultures , filter sterilized with a 0 . 22 μm ( pore-size ) syringe filters , and standardized to culture optical densities ( A600 ) . Zymographic analyses of bacteriolytic proteins were conducted using standard methods described elsewhere [98] and samples were separated upon a 12% SDS gel incorporated with 0 . 3% ( vol/vol ) heat killed USA300_LAC cells [98] . To determine exoprotein profiles , the spent media supernatant was concentrated using standard trichloroacetic acid precipitation . The resultant protein pellets were resuspended in laemelli buffer and equal volumes were separated upon a 12% SDS gel . The taxonomic distribution of Suf was determined via BLASTp analyses of publically available genome sequences in October of 2011 as part of a previous study [25] . This distribution of Suf was characterized using the KEGG gene viewer [99] , with manual verification using BLASTp or using sequence alignments . 1094 genomes out of a total of 1667 genome sequences ( 65 . 6% of total ) encoded for SufBC . Genomes that encoded for SufBC were then screened for the presence of SufT using BLASTp . sufT was considered to be associated with the suf operon if they were within four open reading frames from sufBC and appeared to be transcribed from a common promoter . SufT sequences were compiled and aligned with ClustalW specifying default parameters [100] . The aligned sequences were manually truncated to the minimal SufT sequence or positions 1 to 99 of SufT from Thermoplasma acidophilum ( Kegg ID: Ta0200 ) . Phyml was used to reconstruct the evolutionary history of the SufT alignment block specifying the Blosum62 substitution model and gamma distributed rate variation [101] . The topology of the tree was evaluated using Chi2-based likelihood ratio tests . The phylogenetic reconstruction was projected with the Interactive Tree Of Life ( Itol ) web program [102] . The N- and C-terminal sequences that were pruned from the alignment block were subjected to BLASTp against the Conserved Domain Database ( CDD ) using an evalue of 0 . 01 [103] . Identified motifs in both N- and C- terminal motifs were compartmentalized into modular structures based on the presence of unique sequence motifs . These N- and C-terminal motifs were mapped onto the SufT phylogenetic tree using the Itol program . Furthermore , SufT was mapped onto a concatenated SufBC phylogenetic tree using the Itol program . The concatenated SufBC tree was constructed as previously described [25] .
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Iron-sulfur ( FeS ) clusters are inorganic cofactors that are used for diverse cellular processes including cellular respiration , DNA replication and repair , antibiotic resistance , and dinitrogen fixation . A failure to properly assemble FeS clusters in proteins results in widespread metabolic disorders , metabolic paralysis , and oftentimes cell death . Therefore , the biosynthesis of FeS clusters is essential for nearly all organisms . Proteins containing DUF59 domains are widespread in Eukarya , Bacteria , and Archaea . Proteins containing DUF59 domains have roles in FeS cluster assembly , but the function ( s ) of the DUF59 domain is unknown . Moreover , the function ( s ) of proteins containing DUF59 domains are largely unknown . Staphylococcus aureus SufT is composed solely of a DUF59 domain , which provides a unique opportunity to examine the role ( s ) of this domain in cellular physiology . In this report we show SufT to be an accessory factor utilized in FeS cluster assembly during conditions imposing a high-demand for FeS proteins . We also show that deficiencies in the maturation of FeS proteins result in alterations in the ability of S . aureus , an epidemic human pathogen , to form biofilms , produce exoproteins , and resist antibiotic stress .
|
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2016
|
The DUF59 Containing Protein SufT Is Involved in the Maturation of Iron-Sulfur (FeS) Proteins during Conditions of High FeS Cofactor Demand in Staphylococcus aureus
|
In recent years , there has been growing awareness of the significant burden of Chagas disease in the United States ( US ) . However , epidemiological data on both prevalence and access to care for this disease are limited . The objective of this study is to provide an updated national estimate of Chagas disease prevalence , the first state-level estimates of cases of T . cruzi infection in the US and to analyze these estimates in the context of data on confirmed cases of infection in the US blood supply . In this study , we calculated estimates of the state and national prevalence of Chagas disease . The number of residents originally from Chagas disease endemic countries were computed using data on Foreign-Born Hispanic populations from the American Community Survey , along with recent prevalence estimates for Chagas disease in Latin America from the World Health Organization that were published in 2006 and updated in 2015 . We then describe the distribution of estimated cases in each state in relation to the number of infections identified in the donated blood supply per data from the AABB ( formerly American Association of Blood Banks ) . The results of this analysis offer an updated national estimate of 238 , 091 cases of T . cruzi infection in the United States as of 2012 , using the same method as was used by Bern and Montgomery to estimate cases in 2005 . This estimate indicates that there are 62 , 070 cases less than the most recent prior estimate , though it does not include undocumented immigrants who may account for as many as 109 , 000 additional cases . The state level results show that four states ( California , Texas , Florida and New York ) have over 10 , 000 cases and an additional seven states have over 5 , 000 cases . Moreover , since 2007 , the AABB has reported 1 , 908 confirmed cases of T . cruzi infection identified through screening of blood donations . This study demonstrates a substantial burden of Chagas disease in the US , with state variation that reflects the distribution of at risk Latin American immigrant populations . The study lends important new insight into the distribution of this disease in the US and highlights the need for further research quantifying prevalence and incidence to guide interventions for control of Chagas disease across the US .
Chagas disease , caused by infection with the parasite Trypanosoma cruzi , affects an estimated 8–10 million people globally . Among those infected with the parasite , approximately 30% will develop chronic Chagas cardiac disease , including serious arrhythmias and heart failure . This infection can also be transmitted congenitally , from infected mother to child . Though Chagas disease has historically been considered a condition of the rural poor in Latin America , in recent years there has been growing recognition of the burden of Chagas disease in the United States ( US ) and Europe due to human migration from Latin America . [1–3] Moreover , both autochthonous transmission in the southern US and congenital transmission have been documented , although the numbers of cases are small and the risk of transmission is undefined . [4 , 5] In the United States , there is no national level surveillance for Chagas disease; the most recent study to estimate the prevalence of this disease in the US , published in 2009 , concluded that approximately 300 , 167 individuals with Chagas disease were living in the United States in 2005 . [1] Since these estimates were published , a more recent American Community Survey ( ACS ) was released , allowing for an updated estimation of the numbers of T . cruzi infections at both the state and national level . In addition , screening of the blood supply was initiated in 2007 and surveillance data on cases identified in the donated blood supply since that time offer additional insight into the geographic distribution of Chagas disease in the US . The objective of this study is to provide an updated national estimate and the first state-level estimates of cases of T . cruzi infection in the US as of 2012–2013 and to analyze these estimates in the context of data on confirmed cases of infection in the US blood supply during the period from 2007–2013 .
In this study , we calculated estimates of the state and national prevalence of Chagas disease . We did not estimate possible US-acquired infections , either congenital or vector-borne . State-level estimates of the number of residents originally from Chagas disease endemic countries were computed using data on Foreign-Born Hispanic populations by state from the ACS; state totals were summed to provide a national estimate . This was compared to the national level estimate generated using data on Foreign-Born Hispanic populations for the entire nation from the ACS . To estimate the number of cases of Chagas disease we used recent prevalence estimates for the Latin American countries of origin from the World Health Organization ( WHO ) . [6–8] The WHO has provided country-based estimates of prevalence of T . cruzi infection in 2006 and updated these estimates in 2015 to reflect the impact of national vector control programs in the intervening period . State-Level Foreign-Born Hispanic Population Estimates by single Country of Origin group were computed using the 2012 ACS data for a five-year period ( 2008–2012 ) . The prevalence by state was estimated using Hispanic Country of Origin groups for each state as estimated by the ACS . For each single Country of Origin group , an expected number of cases were computed by multiplying the estimated state population by the proportion who had migrated prior to 2010 by the respective prevalence in 2006 in the Country of Origin and adding to that the estimated population that had migrated after 2010 multiplied by the respective prevalence reported by WHO in 2015 . This method is represented in the following equation: Estimated Cases of Chagas disease in State B from Country A Origin Group = ( Population that migrated from Country A to State B prior to 2010 X National prevalence of T . cruzi infection in Country B per WHO 2006 ) + ( Population that migrated from Country A to State B after 2010 X National prevalence of T . cruzi infection in Country B per WHO 2015 ) The estimated number of cases for each state was calculated as the sum of the expected number of cases for each of the single Hispanic Country of Origin groups in that state . The state totals were then added to provide a national prevalence estimate . For comparability , we also calculated the estimated number of cases at the national level in 2012 by taking the sum of the estimated number of infections for each single Country of Origin group at the national level . This method is the same as that which was used in Bern and Montgomery ( see Table 1 ) . [1] Finally , we describe the distribution of estimated cases in each state in relation to the number of infections identified in the donated blood supply per de-identified data from the AABB ( formerly American Association of Blood Banks ) . AABB aggregates voluntary reports of donor Chagas disease testing from most but not all U . S . blood collection agencies . These data include all confirmed cases reported to them from January 2007 to September 2013 . These data do not include cases diagnosed via mechanisms other than blood donation , such as community-based screening efforts or other clinical settings . [9] This study was approved by the Boston University Medical Center Institutional Review Board ( IRB ) ( Protocol H-32356 ) .
The results of this analysis offer an updated national estimate of 238 , 091 cases of T . cruzi infection in the United States as of 2012 using the same method as was used by Bern and Montgomery to estimate the number of cases in 2005 ( Table 1 ) . [1] This represents a decrease of 62 , 070 cases from this most recent prior estimate . By comparison , summing the number of cases estimated by state offers a slightly lower national estimate of 238 , 072 cases . The state level results show four states with over 10 , 000 cases and an additional seven states with over 5 , 000 cases ( Fig 1 ) . Moreover , since 2007 , the AABB has reported 1 , 908 confirmed cases of T . cruzi infection identified through screening of blood donations .
This study offers the first state-level analysis of the burden of Chagas disease in the US and provides objective data to inform future policies on screening and clinical care provision for this disease . This updated national estimate of 238 , 091 cases of T . cruzi infection represents an approximately 20% decrease in the number of infections estimated for the US in 2005 , most likely due to a decrease in the estimated population of foreign-born Latin Americans based on US Census population surveys conducted after 2005 . The state level estimates show a geographically focal burden with highest estimated numbers of cases in California , Texas , Florida and New York . Data on cases identified through blood donor screening is largely but not completely congruent with the state level estimates . This difference may reflect variation in community blood donation preferences related to country of origin , since blood donations are voluntary , or possibly other cultural or social differences . [10] There are several limitations to this study . First , we focused our estimation of the cases of Chagas disease to documented foreign born immigrants from endemic countries in Latin America only . Estimates of undocumented immigrants in the United States are no longer available from U . S . Department of Homeland Security , the source of population totals by country of origin used in the previously published estimate . Recent data from the Pew Research Center suggest that in 2012 there were approximately 11 . 2 million undocumented immigrants in the United States . Moreover , among the leading countries of origin for the undocumented population were several countries with substantial prevalence of Chagas disease , Mexico , Guatemala and El Salvador . [11] [1] . Based on these 2012 Pew Research Center estimates of undocumented Latin American immigrants in the United States and WHO country Chagas disease prevalence , an additional 88 , 000–109 , 000 people with Chagas disease may be living in the United States , bringing the estimated total number of cases to between 326 , 000 and 347 , 000 . Because of gaps in data available , we cannot provide state level estimates of Chagas disease among undocumented Latin American immigrants . A second limitation of this study was the inability to obtain reliable data to estimate both autochthonous of T . cruzi transmission and congenital transmission of T . cruzi infection among populations of Latin American origin . A third limitation of this study was that we assumed the prevalence of Chagas disease in the foreign born US immigrant population was equal to that of the country of origin . Due to a lack of data , we were unable to account for age , sex or year of immigration . Finally , for states whose population in any of the Hispanic origin groups was too small to be estimated , they were assumed to have zero members in that group and consequently no expected infections . If anything , this would result in an underestimation of the true number of cases . These findings have important implications for health policy regarding Chagas disease in the US . The estimate from this study is similar to the previous report and suggests a continuing burden of Chagas disease in the US . This research is a critical first step in addressing Chagas disease in the United States . Other important research gaps in our understanding of the epidemiology of this disease in the US include: ( 1 ) representative studies to generate an evidence-based prevalence of disease nationwide; ( 2 ) studies to estimate the risk for acquiring T . cruzi infection via congenital or autochthonous transmission in the US; and ( 3 ) a national level definition of the contribution of T . cruzi infections to cardiac disease . Defining the Chagas disease burden in the US is necessary to inform efforts to ensure access to appropriate care and treatment among the populations at risk . In conclusion , this study demonstrates a sustained substantial burden of Chagas disease in the US and offers the first state level prevalence estimates for Chagas disease . We also show state variation in burden , reflecting the distribution of at risk Latin American immigrant populations . The study lends important new insight into the distribution of this disease in the US and highlights the need for further research quantifying prevalence and incidence to guide interventions for control of Chagas disease across the US .
|
Chagas disease is a parasitic infection that primarily affects poor populations in Latin America . However , awareness of this disease in the United States has increased in recent years . In this study , we utilize data from the American Community Survey and the World Health Organization to estimate the number of cases of Chagas disease in the United States . We find that there are an estimated 238 , 000 cases across the United States , along with four states that each has over 10 , 000 cases ( California , Texas , New York and Florida ) . We also analyze data from the United States blood donation which shows that about 1 , 900 cases have been identified through blood donation . We conclude that there is still a substantial burden of Chagas disease in the US , though the burden is focused in certain geographic regions . We also highlight the need for further research to better quantify prevalence and incidence in order to guide interventions to diagnose and treat patients with Chagas disease across the US .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
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2016
|
Estimating the Burden of Chagas Disease in the United States
|
Type I interferon ( IFN-I ) plays a critical role in the homeostasis of hematopoietic stem cells and influences neutrophil influx to the site of inflammation . IFN-I receptor knockout ( Ifnar1−/− ) mice develop significant defects in the infiltration of Ly6Chi monocytes in the lung after influenza infection ( A/PR/8/34 , H1N1 ) . Ly6Chi monocytes of wild-type ( WT ) mice are the main producers of MCP-1 while the alternatively generated Ly6Cint monocytes of Ifnar1−/− mice mainly produce KC for neutrophil influx . As a consequence , Ifnar1−/− mice recruit more neutrophils after influenza infection than do WT mice . Treatment of IFNAR1 blocking antibody on the WT bone marrow ( BM ) cells in vitro failed to differentiate into Ly6Chi monocytes . By using BM chimeric mice ( WT BM into Ifnar1−/− and vice versa ) , we confirmed that IFN-I signaling in hematopoietic cells is required for the generation of Ly6Chi monocytes . Of note , WT BM reconstituted Ifnar1−/− chimeric mice with increased numbers of Ly6Chi monocytes survived longer than influenza-infected Ifnar1−/− mice . In contrast , WT mice that received Ifnar1−/− BM cells with alternative Ly6Cint monocytes and increased numbers of neutrophils exhibited higher mortality rates than WT mice given WT BM cells . Collectively , these data suggest that IFN-I contributes to resistance of influenza infection by control of monocytes and neutrophils in the lung .
Type I interferons ( IFN-I ) are produced by different cell types including alveolar macrophages ( AM ) , plasmacytoid dendritic cells , and epithelial cells following virus infection in the lung [1] , [2] . These IFN-I cytokines engage a unique heterodimeric IFN-α receptor ( IFNAR ) to induce various antiviral effectors [3] . IFN-I-related antiviral effectors , including protein kinase PKR [4] , 2′-5′-oligo A synthetase [5] , and Mx-GTPase [6] , control influenza virus infection by their own or in cooperation with various other signaling pathways [7] , [8] . Even though influenza NS1 protein provides antagonistic properties against IFN-I-inducible antiviral proteins , which help virus to circumvent host barriers [9] , increased susceptibility in Ifnar1−/− mice indicates that IFN-I signaling still plays a significant role in protecting the host after influenza infection in vivo [10] , [11] . Monocytes emigrate from bone marrow ( BM ) thorough CCR2 receptor-mediated signaling and then monocyte-derived cells mediate inflammatory responses against influenza infection [12] , [13] . Because of the important function of these monocytes , IFN-I signaling-mediated monocyte differentiation should be examined to better understand the regulation of leukocyte differentiation at large . In accordance , one recent study showed that IFN-I signaling triggers hematopoietic stem cell ( HSC ) proliferation [14] . Similarly , mice lacking IFN regulatory factor-2 , a suppressor of IFN-I signaling , fail to maintain quiescent HSC [15] . These studies of the effect of IFN-I on regulation of cell homeostasis may explain different cell constitutions of Ifnar1−/− mice . However , specific cell populations directly affected by IFN-I during hematopoiesis and their contributions toward unique cell composition in peripheral tissues are not yet described . Thus , the role of IFN-I on the regulation of overall monocyte differentiation and infiltration into inflamed tissue needs to be analyzed in depth . Although neutrophils are universally accepted as important in bacterial infection resistance [16] , their role in viral infection remains controversial . Tate and colleagues reported that mice undergo more pronounced disease when neutrophils are absent [17] , [18] . However , depletion of neutrophils not only results in increased viral burden but also in decreased lung inflammation , indicating that neutrophils contribute to control virus dissemination but may augment overall pathogenesis [19] . Clinically , excessive neutrophil recruitment , especially after highly pathogenic avian H5N1 or 1918 pandemic influenza infection , seems to play a detrimental role in acute lung injury [20] , [21] . Thus , neutrophil infiltration seems to be closely related to tissue damage following infection as well as to inflammation , and the feed-back regulation of neutrophil recruitment to the site of infection needs to be tightly regulated . In the current study , we adopted the influenza infection model and found that Ifnar1−/− mice undergo more acute and severe inflammation than B6 wild-type ( WT ) mice . IFN-I was directly involved in Ly6Chi monocyte differentiation from its precursor and these Ly6Chi monocytes exclusively provided MCP-1 in the lung after influenza infection . Further , Ifnar1−/− mice with defects in monocyte maturation produced excess KC chemokine and developed high mortality and severe neutrophilia when compared with WT mice . Our results suggest that IFN-I is required to resist influenza infection by orchestrating the leukocyte population in the lung and chemokines produced by those cells .
Because many previous studies indicate a crucial role for IFN-I in host defense against influenza infection , we looked for crucial regulatory factors that are mainly regulated by IFN-I after influenza infection using Ifnar1−/− mice of B6 background . First we challenged Ifnar1−/− and WT mice with a lethal dose ( 1×105 pfu ) of PR8 virus . The Ifnar1−/− mice started to die 5 days post infection ( dpi ) and all were dead within 8 dpi while approximately 50% of WT mice survived ( Figure 1A ) . When we decreased the challenge dose of PR8 virus ( 2×104 pfu ) , virus infection killed Ifnar1−/− mice from 5 dpi and no mice survived at 13 dpi while 80% of WT B6 mice survived ( Figure 1A ) . WT mice started to regain body weight 8 or 9 days after infection while Ifnar1−/− mice continued to lose weight until they died ( Figure 1A ) . Since the contribution of IFN-I in viral clearance is controversial [22] , [23] , we next addressed viral titer in the lung at 2 and 5 dpi with influenza PR8 virus ( 1×105 pfu ) . Intriguingly , Ifnar1−/− mice showed higher viral titer in the lung than WT mice at 2 dpi but not at 5 dpi ( Figure 1B ) . However , total protein levels were significantly higher in the bronchoalveolar lavage fluid ( BALF ) of Ifnar1−/− mice at 5 dpi than in WT mice ( Figure 1C ) . Of note , significant levels of IL-6 , TNF-α and IP-10 were determined at 3 dpi in the BALF of Ifnar1−/− mice and high levels IFN-γ and IL-6 at 5 dpi when compared with levels in WT mice ( Figure 1D ) . Lung histopathology of WT and Ifnar1−/− mice after H&E staining revealed more edema , alveolar hemorrhage , alveolar wall thickness , and neutrophil infiltration in Ifnar1−/− mice than in WT mice at 5 dpi ( Figure 1E and 1F ) . Staining specifically for myeloperoxidase ( MPO ) , most abundantly present in the granules of neutrophils , confirmed increased numbers of MPO+ neutrophils in the lung of Ifnar1−/− mice after influenza infection ( Figure 1F ) . As Ifnar1−/− mice of B6 background exhibited severe pathology in terms of hyper secretion of pro-inflammatory cytokines and neutrophilia in lung after infection with influenza virus , we examined profiles of infiltrated cell populations in BALF in a time-dependent manner . From 1 dpi with influenza virus , predominant numbers of neutrophils ( Ly6CintLy6G+ ) were infiltrated into the lung of WT and Ifnar1−/− mice ( Figure 2A ) . Of note , the proportion ( Figure 2A ) and absolute numbers ( Figure 2B ) of infiltrated neutrophils in BALF were much higher in Ifnar1−/− mice than in WT mice . Furthermore , the numbers of neutrophils in WT mice peaked at 3 dpi and decreased at 5 dpi while neutrophils in Ifnar1−/− mice increased until 5 dpi , when mice began to die ( Figure 2B ) . Meanwhile , monocytes ( Ly6C+Ly6G− ) were gradually infiltrated into the lung of WT mice in a time-dependent manner after influenza infection ( Figure 2A and 2B ) . However , fewer monocytes were recruited into the lung of Ifnar1−/− mice than in WT mice ( Figure 2A and 2B ) and they were Ly6Cint rather than Ly6Chi ( Figure 2C ) . Since Ly6Chi monocytes have similar phenotype to myeloid-derived suppressor cells , which expand during cancer , inflammation , and infection [24] , we tested their ability to suppress CD4+ T cells . However , we did not find any evidence of CD4+ T cell suppression by Ly6Chi monocytes ( Figure S1A ) . We next examined expression levels of co-stimulatory molecules , such as CD40 , CD80 , CD86 , and MHCII , on surfaces of the respective cell populations , but overall these markers were not significantly different between cells isolated from WT and Ifnar1−/− mice ( Figure S1B ) . We further analyzed surface expression of various markers on monocytes of infected WT and Ifnar1−/− mice ( Figure S1C ) . Monocytes in the lung of infected WT and Ifnar1−/− mice expressed macrophage-related markers and they were negative for markers specific for dendritic cells , lymphocytes , and natural killer cells [25] . Because MyD88 signaling cooperates with IFN-I in Ly6Chi monocyte recruitment in a Listeria monocytogenes infection model [26] , we tested whether Toll-like receptor ( TLR ) or RIG-I-like receptor ( RLR ) signaling is involved in Ly6Chi monocyte regulation in our model . However , mice have defects in TLR ( Myd88−/−Trif−/− ) or RLR ( Ips-1−/− ) signaling normally generated Ly6Chi monocytes ( Figure S2A ) . Although IPS-1 is involved in IFN-I expression , deletion of IPS-1 can be compensated by MyD88 signaling after influenza infection [27] . Indeed , Ips-1−/− mice produced IFN-α comparable to Ips-1+/+ mice ( Figure S2B ) . Overall , direct engagement of IFN-I signaling through IFNAR but not TLR or RLR signaling seems to play a crucial role in Ly6Chi monocyte infiltration into the lung for host defense after influenza infection . To assess the innate immune cells of WT and Ifnar1−/− mice in more detail , we next examined their morphologies ( Figure 2D ) . Both AM and neutrophils in the lung seemed identical in WT and Ifnar1−/− mice except that some neutrophils from Ifnar1−/− mice exhibited larger size and had a more diffused nucleus , but monocytes were clearly different in the lung of WT and Ifnar1−/− mice after influenza infection . Interestingly , Ly6Chi monocytes morphologically resemble the foamy macrophages previously found in the lung of Mycobacterium bovis bacillus Calmette-Guérin-infected mice [28] . To confirm this , we stained Ly6Cpos monocytes with Nile red , which mainly stains lipid body , and found that only Ly6Cpos monocytes isolated from the lung of WT mice were positively stained ( Figure 2E ) . Next we generated BM chimeric mice ( WT BM into Ifnar1−/− mice and vice versa ) to confirm whether the defect in IFN-I signaling in the hematopoietic cell lineage can trigger the alteration of monocyte phenotypes in the lung of influenza-infected mice . As a result , Ly6Chi monocytes were generated in Ifnar1−/− recipient mice reconstituted with WT BM cells but were not detected in WT recipient mice that received Ifnar1−/− BM cells ( Figure 2F ) . These suggest that IFN-I signaling in the hematopoietic cell lineage plays an indispensable role for differentiation of Ly6Chi monocytes against influenza infection . We next measured the chemokines responsible for leukocyte migration against influenza infection in the BALF at different time points . Of note , expression levels of MCP-1 and KC , the main chemokines for CCR2- and CXCR2-dependent cell recruitment , respectively [29] , [30] , were very different in the BALF of WT and Ifnar1−/− mice ( Figure 3A ) . The Ifnar1−/− mice had significantly lower levels of MCP-1 than the WT mice but had predominant levels of KC at 3 and 5 dpi ( Figure 3A ) . In addition , MIP-2 , another well-known molecule for neutrophil recruitment [31] , was significantly higher in Ifnar1−/− mice at 3 dpi ( Figure 3A ) . Both Ly6Chi and Ly6Cint monocytes obtained from the lung of WT and Ifnar1−/− mice expressed CCR2 but not CXCR2 ( Figure 3B ) , suggesting the critical role of MCP-1 for monocyte recruitment into influenza-infected lung . To directly compare chemokine expression by individual cell populations , we sorted Ly6Cpos monocytes from influenza-infected lung of WT and Ifnar1−/− mice and then cultured them in vitro . Although both monocytes and AMs isolated from WT mice produced MCP-1 ( Figure 3C ) , recovered Ly6Chi monocytes were quantitatively overwhelming over AMs ( 2 . 6×105 vs . 3 . 1×104 in BALF per mouse at 5 dpi ) ( Figure 2B ) . These findings suggest that Ly6Chi monocytes are the main producer of MCP-1 among leukocytes in WT mice after influenza infection . In contrast , in Ifnar1−/− mice , KC was highly produced by both Ly6Cint monocytes and AMs , but Ly6Cint monocytes secreted significantly less MCP-1 than WT Ly6Chi monocytes ( Figure 3C ) . It is noteworthy that types of chemokines expressed in monocytes from WT and Ifnar1−/− mice were in complete contrast; gene expression profiles analyzed by gene chip experiments support these results ( Figure 3D ) . Collectively , these data suggest that IFN-I is crucial for determining monocyte characteristics that dramatically influence chemokine production . For macroscopic comparison between monocytes recruited in the lung of influenza-infected WT and Ifnar1−/− mice , we performed gene chip analysis ( Figure 4 ) . As expected , IFN-I-regulated genes ( e . g . , Mx , Oas , Irf , etc . ) were significantly decreased in monocytes isolated from Ifnar1−/− mice . Of the genes elevated in Ly6Cint monocytes isolated from Ifnar1−/− mice , S100a8/S100a9 and Trem1 are associated with inflammatory responses in various diseases [32] , [33] . In contrast , negative regulators of inflammation , Trim21 and Trim30 [34] , [35] , were up-regulated in Ly6Chi monocytes isolated from WT mice when compared to Ly6Cint monocytes from Ifnar1−/− mice . These inflammation-biased gene expressions by Ly6Cint monocytes may explain the higher susceptibility of Ifnar1−/− mice to influenza infection . Ly6Chi monocytes from WT mice were also superior in expressing genes involved in lipid metabolism ( e . g . , Apoe , Apoc2 , etc . ) . Interestingly , the 1918 pandemic influenza virus was found to block lipid metabolism as part of its evasion strategy against antiviral responses [36] . Furthermore , influenza infection causes prominent inflammation in Apoe−/− mice [37] , indicating that a defect in lipid metabolism in Ifnar1−/− mice might contribute to worsen inflammation . Collectively , we confirmed that monocytes from WT and Ifnar1−/− mice have significantly different characteristics and that lack of IFN-I signaling changes gene expression bias to augment inflammation . Since the different gene expression patterns in Ly6Cpos monocytes of WT and Ifnar1−/− mice after influenza infection can be ascribable to altered monocyte differentiation from their precursors , we next analyzed BM where hematopoiesis occurs and provides common monocyte precursors . The proportion of Ly6Chi monocytes was significantly lower ( 6 . 9±1 . 9 vs . 1 . 0±0 . 3% ) in the BM of Ifnar1−/− mice than in WT mice , while the proportion of Ly6Cint monocytes was comparable to WT mice ( 17 . 2±0 . 6 vs . 16 . 3±1 . 5% ) at 5 dpi ( Figure 5A ) . However , there were no significant differences in cell morphology between WT and Ifnar1−/− mice ( data not shown ) , and Ly6Cpos monocytes of WT mice did not show lipid bodies unlike Ly6Chi monocytes in the lung post influenza infection ( Figure 5B ) . Because we found higher levels of Ly6C expression in BM monocytes from WT than in Ifnar1−/− mice at 5 dpi ( Figure 5C ) , we further assessed whether IFN-I signaling can directly affect differentiation of naïve BM cells in vitro . When BM cells were stimulated with WT BALF collected at 5 dpi or directly infected with influenza virus , WT BM cells were able to differentiate into Ly6Chi monocytes but Ifnar1−/− BM could not ( Figure 5D ) . To confirm that this maturation defect of Ly6Chi monocytes from Ifnar1−/− BM is due to lack of IFN-I signaling , we co-cultured PR8-infected WT BM cells with or without anti-IFNAR1 blocking antibody . When treated with anti-IFNAR1 antibody , WT BM cells failed to differentiate into Ly6Chi monocytes ( Figure 5E ) . Importantly , these Ly6Chi monocytes derived from WT BM stimulated by influenza virus dominantly produced MCP-1 when compared to Ly6Cint monocytes derived from WT or Ifnar1−/− BM , while Ifnar1−/− Ly6Cint monocytes produced robust KC instead ( Figure 5F ) . From this observation , we suggest that absence of Ly6Chi cells in lung of Ifnar1−/− mice results from failure in differentiation of their precursors due to lack of IFN-I signaling . To clarify the origin and roles of monocytes in depth in the absence of IFN-I signaling , we used BM chimeric mice . Ifnar1−/− recipient mice reconstituted with WT BM cells ( W→K ) have significantly more Ly6Chi monocytes than K→K ( Ifnar1−/− donor and Ifnar1−/− recipient ) mice ( Figure 6A ) . Concomitantly , the MCP-1 level in the BALF was correlated with Ly6Chi monocytes ( i . e . , W→W and W→K ) after influenza infection ( Figure 6B ) . The fact that K→W mice elicited no higher level of MCP-1 than found in the BALF of K→K mice after influenza infection suggests that radio-resistant WT parenchymal cells do not play a major role in MCP-1 production ( Figure 6B ) . In addition , Ly6Chi monocytes derived from W→W and W→K mice produce MCP-1 efficiently following in vitro culture ( Figure 6C ) , clearly indicating that Ly6Chi monocytes derived from WT BM mice exclusively produce MCP-1 in response to influenza infection . To the contrary , although we saw a distinct correlation between KC production with IFN-I deficiency in monocytes ( Figure 6C ) , W→K chimera mice still had significantly augmented KC in BALF even with high levels of MCP-1 and Ly6Chi monocytes ( Figure 6B ) . Thus it seems likely that the complete regulation of KC also needs the IFN-I-dependent signaling pathway in cells other than monocytes or there may be another regulator for KC in the absence of IFN-I signaling . Finally , we sought to find the relationships between the defect in Ly6Chi monocyte generation and the susceptibility of chimeric mice against influenza infection . The loss of intrinsic Ly6Chi monocytes in K→W mice resulted in the increased susceptibility of mice against influenza infection as compared with W→W mice ( Figure 6D ) . In addition , the W→K chimera mice showed less susceptibility to influenza infection than did K→K mice with the restoration of Ly6Chi monocytes ( Figure 6D ) . To see whether neutrophils can augment inflammation and tissue damage , we isolated neutrophils from infected Ifnar1−/− mice and transferred them to naïve WT mice ( Figure S3A ) . Then effect of neutrophil transfer on the lung inflammation was observed without additional infection or any treatment . Histologically , recipient WT mice had destruction in epithelial layers and showed inflammatory lesions while PBS-treated mice did not at 2 days after transfer ( Figure S3B ) . Inflammatory cytokines in the BALF were also induced after neutrophil transfer , indicating activated neutrophil itself can cause inflammation ( Figure S3C ) . Thus , we suggest that excess neutrophils can worsen disease even if there are good reasons for recruitment after virus infection . Even though parenchymal cells appear to maintain host defense , our data clearly show the importance of IFN-I signaling in hematopoietic cells in protection against influenza infection .
Despite numerous previous studies focused on the direct antiviral nature of IFN-I , the data we present here suggest that IFN-I-dependent generation of Ly6Chi monocytes after influenza infection might be critical for the attenuation of neutrophil infiltration and hence prevent severe tissue damage caused by uncontrolled inflammation ( Figure 7 ) . Ly6Chi monocytes in the lung were previously reported as TNF/iNOS-producing dendritic cells [38] or inflammatory monocytes [12] , [39] . In our observations , however , Ly6Chi monocytes were closest in morphology to the highly vacuolated foamy macrophages , which were found in granulomas in the lung of Mycobacterium bovis-infected mice [28] . Previously , it was proposed that there is no up-regulation of MCP-1 in the absence of IFN-I signaling; as a consequence , without up-regulation there will be fewer Ly6Chi monocytes in various tissues [26] , [40] , [41] . We show here that only mice reconstituted with WT BM cells ( W→W and W→K ) , but not with Ifnar1−/− BM cells ( K→K and K→W ) , generated Ly6Chi monocytes and produced high amounts of MCP-1 in the BALF against influenza infection . Further , BM cells from Ifnar1−/− mice were unable to differentiate into MCP-1-producing Ly6Chi monocytes by in vitro stimulation , indicating that IFN-I signaling on hematopoietic cells is required for differentiating Ly6Chi monocytes . Our findings are the first to delineate a notion that MCP-1 is mainly produced by Ly6Chi monocytes , which are regulated by IFN-I against influenza infection . Additionally , it seems plausible that AMs and other cells ( e . g . , pulmonary epithelial cells ) contribute to produce MCP-1 for initial monocyte recruitment before Ly6Chi monocytes are heavily recruited after virus infection . MCP-1 is a chemokine that has great importance in CCR2-mediated monocyte recruitment after lung inflammation [42] . Although other chemokines such as MCP-2 , MCP-3 , and MCP-4 also contribute to attract CCR2-expressing monocytes after influenza infection , overexpression of MCP-1 results in elevated monocyte recruitment [12] . In this regard , MCP-1-deficient mice show significantly reduced macrophage infiltration [43] . Moreover , MCP-1 is capable of activating AM [44] and of blocking MCP-1-augmented damage on epithelial cells after influenza infection [45] . Thus , Ly6Chi monocytes might play an important role for host defense against influenza infection by mediating MCP-1 . Unlike MCP-1 whose level was dramatically decreased in Ifnar1−/− mice , KC was significantly increased after influenza infection . We observed that W→K and K→W chimeric mice produced intermediate KC when compared to W→W and K→K mice , suggesting that KC can be produced by both radio-sensitive and -resistant cells . AMs , which produced KC in vitro , seemed especially important producers as did radio-resistant cells ( e . g . , epithelial cells ) as proposed by others [46] . Since KC with MCP-1 is reversely correlated in BALF and culture supernatant with monocytes from the lung of WT and Ifnar1−/− mice , further studies are needed to elucidate a putative role of IFN-I-dependent Ly6Chi monocytes on neutrophil regulation as well as possible feedback regulation of KC by MCP-1 production . Previously it was proposed that Ly6Cint monocytes were converted from Ly6Chi monocytes after migration to the site of inflammation [41] . Those activated forms of Ly6Cint monocytes were characterized by high CX3CR1 but no CCR2 expression [47] . In our study , however , Ly6Cint monocytes generated in influenza-infected Ifnar1−/− mice expressed high levels of CCR2 . Thus , it seems likely that Ly6Cint monocytes in Ifnar1−/− mice could be an alternative to Ly6Chi monocytes generated under IFN-I-deficient conditions rather than a different cell subset of Ly6C expressing monocytes . It has been reported that the spleen also stores monocytes and these cells deploy to inflammatory sites to regulate inflammation [25] . To address this issue , we compared characteristics of monocytes in the spleen before and after influenza infection ( Figure S4A ) . Interestingly , Ly6C expression level in the monocytes was much lower in the spleen of Ifnar1−/− mice than in WT mice in the steady-state condition ( Figure S4B ) . The number of Ly6Chi monocytes stored in the spleen was also decreased in Ifnar1−/− mice compared to WT mice , whereas the number of neutrophils was comparable before influenza infection ( Figure S4C ) . We speculate that the reduced numbers of WT and Ifnar1−/− monocytes after influenza infection in the spleen may indicate the spleen is a reservoir that provides monocytes to the lung . After finding that IFN-I signaling is involved in Ly6Chi generation , we next sought to clarify the relationships between the defect in Ly6Chi monocytes and susceptibility to influenza infection . Viral titer in lethally infected lung culminated around 2 dpi and continuously decreased . Although Ifnar1−/− mice were intact in viral clearance and showed similar viral burden at 5 dpi compared to WT mice , they developed severe inflammation and consequently higher susceptibility . In previous studies , attenuated inflammation provided resistance to influenza infection even with increased viral burden [12] , [48] . These findings indicate that virus-induced inflammation could be more critical than viral burden itself in the course of influenza pathology . Thus , we suggest that severe and acute lung inflammation in Ifnar1−/− mice , especially with uncontrolled accumulation of neutrophils due to massive KC production , contributes to increased susceptibility of those mice to influenza infection . Accumulation of neutrophils is one of the most important events during acute respiratory distress syndrome [49] . Neutrophils are generally thought to aggravate lung injury after influenza infection [50] , especially in severe infections such as those we assessed in our study . To address the contribution of neutrophils to virus-induced airway hyperresponsiveness as proposed by others [51] , [52] , we used CXCR2 blocking Ab and CXCR2 antagonist SB225002 [29] , [53] . These materials partially dampened neutrophil responses when moderate or low doses of influenza were given , but they could not efficiently block massive influxes of neutrophils after lethal influenza infection in Ifnar1−/− mice ( data not shown ) . However , we found that neutrophils can augment inflammation and tissue damage ( Figure S3 ) ; also loss of Ly6Chi monocytes in K→W mice augmented neutrophil infiltration compared to W→W mice , indicating the balance between neutrophils and Ly6Chi monocytes are reversely correlated . Moreover , when chimeric mice were lethally challenged , susceptibility of these mice was directly proportional to neutrophil numbers . Our data suggest that uncontrolled neutrophils may aggravate the outcome of excess inflammation against virus infection . Even though neutrophils are thought to augment inflammation and make disease worse , we still must consider their protective role . We showed Ifnar1−/− mice had higher peak virus titer but were able to successfully control virus replication . Regarding previous report that neutrophils can limit virus replication [19] , it seems plausible that excessively recruited neutrophils may contribute to observed virus elimination in Ifnar1−/− mice . However , several lines of evidence lead us to speculate that destructive trait of neutrophils dominated over their positive role during severe and acute viral pneumonia . IFN-I is used to treat several diseases , including hepatitis B virus infection [54] , chronic hepatitis C virus infection [55] , and multiple sclerosis [56] . Since IFN-I , which is produced by virus infection , can migrate and affect BM to switch on the production of functional monocytes [57] , therapeutically administrated IFN-I can communicate with BM leukocytes . This possibility suggests that clinical uses of IFN-I should be investigated in terms of modified patient leukocyte profiles , especially in those receiving prolonged IFN-I therapy . Influenza virus NS1 protein antagonizes IFN-I responses , and influenza virus lacking the NS1 gene replicates inefficiently in tissue culture and normal egg culture conditions and shows attenuated phenotype in WT mice; however , it replicates far more efficiently in IFN-deficient Vero cells and pathogenic in Stat1−/− mice [58] . In the clinical context , it is important to note that the NS1 protein of highly pathogenic viruses , such as H5N1 avian influenza and the 1918 pandemic influenza virus , has stronger suppressive effects on IFN-I [59] , [60] . These viruses also are involved in more acute recruitment of neutrophils , severe lung injury , and aggressive inflammatory cytokine production ( so-called cytokine storm ) as found in Ifnar1−/− mice [20] , [61] , [62] . Thus our results in Ifnar1−/− mice merit further study to help understand the pathogenesis of highly pathogenic influenza virus . Our findings show the specific function of IFN-I on Ly6Chi monocyte differentiation and address the impact of this event in the lung after influenza infection . Further studies on regulation of neutrophils and Ly6Chi monocytes by potential pandemic virus may provide insight that will prove useful for development of novel therapeutic targets .
All animal experiments were approved by the Institutional Animal Care and Use Committee of the International Vaccine Institute ( Approval No: PN 1003 ) , and all experiments were carried out in strict accordance with the Guide for the Care and Use of Laboratory Animals , Institute of Laboratory Animal Resources Commission on Life Sciences National Research Council , USA . All experiments were performed under anesthesia with a mixture of ketamine ( 100 mg/kg ) and xylazine ( 20 mg/kg ) , and all efforts were made to minimize suffering . C57BL/6 ( B6 ) mice were purchased from Charles River Laboratories ( Orient Bio Inc . , Sungnam , Korea ) . Ifnar1−/− mice ( B6 background ) were purchased from B&K Universal Ltd . ( Hull , U . K . ) . To generate chimeric mice , naïve B6 and Ifnar1−/− recipient mice were lethally irradiated with 960 rad and donor BM cells ( 1×107 ) were reconstituted by intraperitoneal injection . Chimeric mice were maintained for at least 8 weeks and chimerism was assessed by IFNAR1 expression on Gr-1+ cells in serum . Mice were infected intranasally ( 20 µl ) with influenza A/PR/8/34 ( PR8 , H1N1 ) virus after anesthesia . To obtain BALF , tracheas were cannulated after exsanguination and lungs were washed with 1 ml of PBS . BALF samples were centrifuged ( 800×g , 5 min ) to isolate cells and supernatants were centrifuged again ( 13 , 000×g , 1 min ) to completely remove remaining cells . BM cells obtained from femurs and tibias and red blood cells were removed before analysis . In some experiments , cells were cultured in vitro ( 1×105 cells/well ) for 4 h in RPMI ( Gibco , Auckland , New Zealand ) supplemented with 10% FBS ( Gibco ) to measure chemokines . Cultured cells were removed from supernatants by centrifugation ( 2 , 300×g , 3 min ) and supernatants were used for further analysis . Total lung was removed and homogenized to prepare lung extracts in 1 ml of PBS ( pH 7 . 4 ) . Confluent Madin-Darby canine kidney ( MDCK ) cells were washed with MEM ( Gibco ) once and treated with virus for 30 min at room temperature . After a wash with MEM , the plate was overlaid with MEM containing 1% low-melting-point agarose and 10 µg/ml of trypsin and incubated at 37°C for 3 days . We measured total protein in BALF samples by BCA Protein Assay Kit ( Pierce , Rockford , IL ) according to the manufacturer's instructions . The levels of MCP-1 , IL-6 , TNF-α , and IFN-γ were measured by Mouse Inflammatory Cytometric Bead Array Kit ( BD Biosciences , San Jose , CA ) . The levels of KC , MIP-2 , and IP-10 were measured by DuoSet Mouse ELISA Kit ( R&D Systems , Minneapolis , MN ) according to the manufacturer's instructions . Lungs were removed from naïve or infected B6 and Ifnar1−/− mice and washed using PBS before being fixed with 4% formaldehyde for 1 h at 4°C . The tissues were embedded in paraffin and stained with H&E . To detect MPO expression , tissues were dehydrated in sucrose solutions ( 10 , 20 , and 30% ) after fixation and embedded in OCT compound ( Sakura Finetec , Tokyo , Japan ) . Cryo sections ( 5 µm ) were fixed in ice-cold acetone and blocked with FcRII/III mAb ( 2 . 4G2; BD Pharmingen , San Jose , CA ) in PBS . Then , tissues were stained with FITC-conjugated anti-MPO ( 2D4; Abcam , Cambridge , MA ) for confocal microscopy . Histopathological score was assessed by a pathologist using a blind test . As previously described [63] , we used a scoring system of 20 points to evaluate the level of lung tissue destruction , epithelial cell layer damage , polymorphonuclear cell infiltration into the site , and alveolitis . Cells were collected from lung or BM and stained with the following antibodies: CD11c ( HL3 ) , CD11b ( M1/70 ) , Ly6C ( AL-21 ) , Ly6G ( 1A8 ) , all purchased from BD Pharmingen; F4/80 ( BM8 ) from eBioscience ( San Diego , CA ) ; and CXCR2 ( 242216 ) from R&D Systems; CCR2 ( MC-21 ) was obtained from Prof . Matthias Mack ( University of Regensburg , Germany ) . The cells were read by FACSCalibur ( BD Biosciences ) and data were analyzed by FlowJo 7 . 2 . 5 ( Tree Star , Ashland , OR ) . In some experiments , cells were sorted using FACSAria ( BD Biosciences ) . Cell populations in the lung were classified using these surface markers: AM ( CD11chiF4/80+ ) , DP ( Ly6C+Ly6G+ ) , neutrophils ( Ly6CintLy6G+ ) , Ly6Chi monocytes ( Ly6ChiLy6G− ) , and Ly6Cint monocytes ( Ly6CintLy6G− ) . For analysis of BM Ly6C/Ly6G-positive cells , CD11b+ cells gated out and further divided depending on their Ly6C and Ly6G expressions . To cytospin cells on Cytoslide ( Thermo Scientific , Asheville , NC ) , sorted cells were centrifuged at 1 , 000 rpm for 10 min using CytoSpin 4 Cytocentrifuge ( Thermo Scientific ) . Then cells were fixed and stained with H&E . For Nile red staining , stock solution ( Sigma , St . Louis , MO; 0 . 1 mg/ml in acetone ) was diluted 1∶5 , 000 in PBS and cells were stained for 30 min at 37°C . Samples were washed twice with Ca2+/Mg2+-free HBSS and cytospun . Then fixed specimens ( 3 . 7% formaldehyde ) were stained with DAPI and washed twice before mounting . Cells were prepared from BM of naïve WT and Ifnar1−/− mice . After being washed twice with RPMI , cells ( 1×107 ) were either infected with PR8 ( 5×106 pfu/3 ml ) virus or mock infected for 30 min at room temperature . Cells were washed in RPMI twice and cultured for 5 days in a 1∶1 mixture of PBS and RPMI containing 10% FBS in culture dishes ( Nunc , Roskilde , Denmark ) . In some groups , we used BALF from infected WT mice for stimulation . To inhibit IFN-I signaling , we used anti-IFNAR1 blocking antibody ( 100 ng/ml; MAR1-5A3; BioLegend , San Diego , CA ) . Monocytes were sorted from lung of WT and Ifnar1−/− mice at 5 dpi . RNA from each cell subset was extracted by RNA Isolation Kit ( Qiagen , Valencia , CA ) . cDNA microarray analysis was performed using a MouseRef-8 v2 Expression Beadchip Kit ( Illumina , Inc . , San Diego , CA ) . We used a paired two-sample t-test for analysis , except for survival data for which we used Kaplan-Meier analysis . *p<0 . 05 , **p<0 . 01 , and ***p<0 . 001 were considered significant .
|
Type I interferon ( IFN-I ) was originally reported as a molecule that interferes with influenza virus replication . Various IFN-I inducible antiviral proteins contribute to dampening virus replication and dissemination . Thus , loss of IFN-I signaling attenuates antiviral response and aggravates disease . Recent studies suggest the possible role of IFN-I in hematopoiesis , which subsequently might have an effect on the immune cell response at the site of infection . Indeed , IFN-I signaling-defective mice have been shown to develop aberrant cell populations . The aim of this current study was to clarify the mechanisms of IFN-I signaling in the regulation of monocytes and neutrophils . We show that IFN-I is directly involved in monocyte differentiation and that loss of IFN-I signaling allows mice to generate monocytes whose gene profile is significantly different . We found that monocytes are an important source of chemokines for further monocyte recruitment , but IFN-I-defective monocytes produce chemokines for neutrophil recruitment . As a result , mice lacking IFN-I signaling recruit more neutrophils and a reduced number of alternatively generated monocytes . Thus , our findings indicate that authentic monocyte differentiation , which requires IFN-I signaling , is critical in controlling neutrophils and protecting mice against influenza virus infection .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"respiratory",
"medicine/respiratory",
"infections",
"microbiology/immunity",
"to",
"infections",
"microbiology/innate",
"immunity",
"immunology/innate",
"immunity",
"infectious",
"diseases/viral",
"infections",
"virology/host",
"antiviral",
"responses"
] |
2011
|
Type I Interferon Signaling Regulates Ly6Chi Monocytes and Neutrophils during Acute Viral Pneumonia in Mice
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Eosinophil responses typify both allergic and parasitic helminth disease . In helminthic disease , the role of eosinophils can be both protective in immune responses and destructive in pathological responses . To investigate whether eosinophils are involved in both protection and pathology during filarial nematode infection , we explored the role of eosinophils and their granule proteins , eosinophil peroxidase ( EPO ) and major basic protein-1 ( MBP-1 ) , during infection with Brugia malayi microfilariae . Using eosinophil-deficient mice ( PHIL ) , we further clarify the role of eosinophils in clearance of microfilariae during primary , but not challenge infection in vivo . Deletion of EPO or MBP-1 alone was insufficient to abrogate parasite clearance suggesting that either these molecules are redundant or eosinophils act indirectly in parasite clearance via augmentation of other protective responses . Absence of eosinophils increased mast cell recruitment , but not other cell types , into the broncho-alveolar lavage fluid during challenge infection . In addition absence of eosinophils or EPO alone , augmented parasite-induced IgE responses , as measured by ELISA , demonstrating that eosinophils are involved in regulation of IgE . Whole body plethysmography indicated that nematode-induced changes in airway physiology were reduced in challenge infection in the absence of eosinophils and also during primary infection in the absence of EPO alone . However lack of eosinophils or MBP-1 actually increased goblet cell mucus production . We did not find any major differences in cytokine responses in the absence of eosinophils , EPO or MBP-1 . These results reveal that eosinophils actively participate in regulation of IgE and goblet cell mucus production via granule secretion during nematode-induced pathology and highlight their importance both as effector cells , as damage-inducing cells and as supervisory cells that shape both innate and adaptive immunity .
Eosinophilia is one of the principal features of parasitic helminth infection and is also associated with asthmatic disease and many gastro-intestinal disorders . However , for many years the relative role of eosinophils in protection against parasites and/or pathology has been contentious . Eosinophils are integral to the development of asthmatic pathology [1] , [2] , yet are commonly thought to be beneficial for protection against helminth infections [3] , [4] . It is now known that the role of eosinophils in protective immune responses differs depending upon the species of infecting helminth [3] . We and others , have previously shown that eosinophils are required for killing of the filarial nematode , Brugia sp . , during primary , but not challenge infection [5] , [6] . In related filarial infections , eosinophils are associated with clearance of parasites in challenge , but not primary infection [7] , [8] . Eosinophils also play a role in clearance of primary S . stercoralis infections [9] , [10] , and secondary N . brasiliensis and T . spiralis infections [11] , [12] . The protective function of eosinophils , has been hypothesised to be mediated by deposition of eosinophil granule contents upon the helminth surface . Indeed each of the eosinophil granule proteins , major basic protein-1 ( MBP-1 ) , eosinophil peroxidase ( EPO ) , eosinophil-derived neurotoxin ( EDN ) and eosinophil cationic protein ( ECP ) , can kill Brugia sp . microfilariae ( Mf ) in vitro [13] . During the eosinophil-dependent protective response in primary in vivo infection with either B . malayi Mf or S . stercoralis L3 , EPO is released into the bloodstream [5] , [9] and deficiency of EPO or MBP-1 can enhance the establishment of L . sigmodontis L3 in non-permissive mouse strains [14] . Despite their role in protection , eosinophils can also have detrimental effects on the host during helminth infection , either by prolonging parasite survival , increasing reproductive maturation , or by contributing to host pathology [15] , [16] , [17] . Development of airway hyper-responsiveness ( AHR ) in B . malayi infected mice has been shown to be dependent on IL-5 [17] . Likewise , AHR in Toxocara canis and Ascaris suum infections is associated with increased lung eosinophilia , IL-5 and IgE in mice [18] , [19] . Eosinophil granule proteins may be linked with the pulmonary pathologies in these models as they can induce the production of airway remodelling factors by bronchial epithelial cells [20] and during challenge infection with B . malayi Mf in mice , deposition of the eosinophil granule protein , MBP-1 was observed [17] . This study addresses the role of eosinophils in nematode infection . We dissect the role of the eosinophil as a double-edged sword in generating protection and in mediating pathology . We definitively confirm the importance of eosinophils in protection against B . malayi Mf by using eosinophil-deficient mice ( PHIL ) . Furthermore we investigate whether the eosinophil granule proteins , EPO and MBP-1 are necessary for protection against Mf . By using eosinophil-deficient and eosinophil granule deficient mice we revealed that eosinophils are important for protective immune responses , and equally , eosinophil granules were shown to be negative regulators of parasite-induced lung inflammatory and adaptive responses .
Groups of eosinophil-less ( PHIL ) mice and wild-type ( WT ) mice were either a ) left naïve and uninfected or b ) given a primary live challenge infection of 200K Mf i . v . , or c ) they were immunised with 3×200 µg doses of MfAg and challenged i . v . with 200K live Mf ( Figure 1 ) . Mice were killed at days 6 , 12 , and 21 post infection and Mf were counted at each time point . Previous immunohistochemical studies , of all tissues in the mouse that have resident eosinophil populations at baseline , revealed no MBP+ cells in PHIL mice [1] . In addition , a sensitive EPO-based ELISA assay revealed no evidence of EPO expression in PHIL mice [21] . The absence of eosinophils led to significantly longer Mf survival during primary infection ( P<0 . 01 ) , further clarifying our previous work in IL-5 and eotaxin-1 deficient mice [5] ( Figure 2A ) . In accord with our previous work [5] , these experiments also showed that eosinophils were not essential for rapid clearance of challenge Mf infections following repeated immunisations ( Figure 2A ) . Blood-borne microfilariae sequester primarily in the small vessels in the lungs of their host and in human filarial patients this can lead to a severe asthmatic syndrome known as Tropical Pulmonary Eosinophilia ( TPE ) . It has previously been demonstrated in mouse models , that Mf infections of Brugia sp . i . v . , leads to AHR , pulmonary inflammation and a cellular infiltrate into the broncho-alveolar lumen [5] , [22] . The type of cells recruited into the broncho-alveolar lavage fluid ( BALF ) was investigated in the naïve and infected PHIL mice . Eosinophils were recruited into the BALF of WT mice during the course of both primary and challenge infections of B . malayi Mf . Maximal eosinophil counts were obtained in BALF at day 21 post infection . Interestingly , in the absence of eosinophils there were no significant differences in the recruitment of other leucocyte cell types into the lung of either naïve mice or mice with primary infections . However , following immunisation plus challenge , there was a significant rise in mast cells in the BALF of mice at day 21 ( P<0 . 0001 ) ( Figure 2B–D ) . Mast cells were infrequently seen in the lungs of WT Brugia Mf-infected mice . Antibody responses to primary Mf infection in WT mice were characterised by IgM and each of the IgG isotypes as we have shown previously [23] . Mice lacking eosinophils had similar Mf-specific antibody responses during primary infection ( data not shown ) . Immunisation and Mf challenge resulted primarily in Mf-specific IgG1 responses in WT mice ( Figure 2E ) . However , mice lacking eosinophils , had both strong Mf-specific IgG1 responses , and high levels of IgE , which increased over 21 days post-challenge ( p<0 . 05 ) ( Figure 2F ) . This suggests that eosinophils may play a role in downregulation of IgE during helminth infection . When the recall response of splenocytes to Mf extract was investigated , IFN-γ was stimulated significantly in both primary and challenge infection regardless of the presence of eosinophils . This was in accord with our earlier observations [23] ( Figure 2G ) . However in the absence of eosinophils , the IFN-γ response was sustained until day 21 during primary infection , possibly due to stimulation by prolonged parasite survival ( p<0 . 001 ) ( Figure 2G ) . Post-challenge , both WT and eosinophil-less mice had high levels of IL-5 ( Figure 2H ) while levels of IL-13 were very low ( Figure 2I ) . Secretion of IL-10 by splenocytes from WT or PHIL mice during primary and challenge infection was very low and IL-4 was not detected in these experiments ( data not shown ) . Filarial-nematode induced airway dysfunction was examined by whole body plethysmography at day 6 , 12 and 21 post challenge . At d12 , but not day 21 , post-challenge , WT mice had significantly greater airway responsiveness to metacholine challenge than their eosinophil-less counterparts ( P<0 . 05 ) ( Figure 3A ) . However there were no significant differences in the plethysmography results of mice that were given live primary Mf challenge alone . Interestingly , the airway responses of all PHIL mice , and particularly the infected PHIL mice , were reduced compared to those of WT mice at day 21 post Mf-challenge , albeit these differences were not significant . Histological sections of lungs from eosinophil-less mice were examined to determine whether there were any other histological differences in the lungs of these mice relative to WT animals . Lungs from groups of naïve , primary and challenge infected WT and PHIL mice were examined for goblet cell hyperplasia , cellular infiltration and collagen remodelling . Interestingly , there were more goblet cells in PHIL mice at day 12 and day 21 post-challenge infection and this was significant at day 12 ( p<0 . 05 ) ( Figure 3 B–C ) . This suggests that eosinophils play a part in regulating goblet cell hyperplasia . In addition , by implication goblet cells appear not to be significantly involved in the mechanical changes in lung responses because higher numbers of goblet cells in PHIL mice are associated with lower responsiveness . Collagen remodelling in primary and challenge infections in WT mice did not appear to be significantly different in comparison to eosinophil-less mice ( Figure S1 ) . Both strains of mice showed similarly increased levels of cellular infiltration and deposition of extracellular matrix proteins following infection at each time point examined . Overall these results suggest that eosinophils do play a targeted role in nematode-induced lung pathology . In order to investigate whether the protective role of eosinophils seen in primary Mf infection and indeed the lung pathology associated with Mf infection is mediated by eosinophil granule proteins , we infected mice specifically lacking either eosinophil peroxidase ( EPO ) or major basic protein ( MBP ) and investigated their ability to clear Mf and their development of nematode-induced pathology . EPO−/− and wild-type mice were infected with B . malayi Mf alone ( primary infection ) or following immunisation with Mf antigen ( challenge infection ) . EPO was not necessary for clearance of Mf in either a primary or a challenge infection ( Figure 4A ) . Thus , although eosinophils are required for clearance of primary Mf infection , the mechanism of killing is not dependent upon degranulation of EPO . The absence of EPO did not significantly alter the recruitment of total leucocytes or non-eosinophilic cells into the lung . Eosinophil infiltration into the lungs was not altered during primary infection in EPO−/− mice , however during challenge infection EPO−/− mice had a reduced eosinophilia in comparison to WT mice at d10 p . i . ( p<0 . 0001 ) ( Figure 4B ) . No mast cells or basophils were observed in the lungs of either WT or EPO−/− mice . EPO may have an autocrine effect on eosinophil recruitment . Antigen-specific immunoglobulin isotypes were measured in EPO−/− and WT mice infected with B . malayi Mf 20 days pi . As previously shown by our group following Mf infection [23] , [24] , high levels of antigen-specific IgG1 , IgG2a , IgG2c , IgG3 and IgM were found . However there were no significant differences between the levels of these isotypes between infected EPO−/− and wild-type mice ( data not shown ) . Interestingly , there was a significantly higher level of IgE in challenge-infected EPO−/− ( but not MBP-1−/− ) mice in comparison to wild-type mice ( p<0 . 05 ) suggesting that EPO directly/indirectly regulates IgE and may account for the high IgE in eosinophil-less mice ( Figure 4C ) . Despite these differences in IgE levels between EPO−/− and WT mice , neither IL-4 not the other splenocyte cytokine responses tested ( IL-5 , IL-13 , IL-10 and IFN-γ ) differed significantly between the groups of mice ( data not shown ) . In order to investigate whether EPO is involved in nematode-induced lung dysfunction during primary or challenge infection , lung function was measured by whole body plethysmography at 5 , 10 and 20 days following challenge . WT mice displayed physiological changes in respiratory function during primary infection however , EPO−/− mice appeared to be protected from B . malayi-induced changes at each time point examined ( Figure 4D ) . There were no differences in Penh between EPO−/− and WT mice during challenge Mf infection . Interestingly absence of EPO also appeared to alter the breathing rate of mice during primary infection ( Figure 4E ) . No differences were observed in goblet cell number or in the level of deposition of collagen or fibrin in infected lungs in the absence of EPO ( data not shown ) . In order to investigate whether MBP-1 is necessary as an effector molecule in the clearance of B . malayi Mf and/or the molecule responsible for pathology during filarial infection , groups of MBP-1−/− and WT mice were infected with B . malayi Mf . Mf survival and filarial-related pathology was assessed at days 5 , 10 and 20 post infection . The absence of MBP-1 did not significantly alter Mf survival during primary infection ( Figure 5A ) . However some parameters of pathology were actually increased in the absence of MBP-1 . For example , total numbers of cells recruited into BALF were not significantly different between MBP-1−/− and WT mice infected with Mf , but MBP-1−/− mice had significantly more eosinophils in primary and challenge infection ( p<0 . 05 ) ( Figure 5B ) . This suggests that MBP-1 release may regulate the recruitment of eosinophils . The filarial antigen-specific antibodies IgG , IgM ( data not shown ) and total IgE responses did not differ between WT and MBP-1−/− mice ( Figure 4C ) suggesting that isotype-regulating cytokine responses did not differ in the absence of MBP-1 . Furthermore , in accord with this no significant differences were seen between cytokine responses ( IL-4 , IL-5 , IL-13 , IL-10 and IFN-γ ) from splenocyte restimulation or from BALF in MBP-1−/− and WT mice ( data not shown ) . In addition , while the absence of MBP-1 had little effect upon nematode-induced airway responses ( Figure 5C ) , goblet cells were greatly increased in number in the absence of MBP-1 suggesting that this eosinophil granule protein may modulate goblet cell hyperplasia ( Figure 5D ) . The level of collagen and fibrin deposition in infected lungs did not appear to differ between infected MBP-1−/− and WT mice ( data not shown ) .
In this study we have investigated the dual role of eosinophils as effector cells in parasite clearance and as mediators of pathology . During helminth infection , eosinophils have been widely reported to be involved in protection and they are also linked with helminth and allergy-associated pathology . Indeed , during filarial infection eosinophils have been reported in both of these roles [5] , [17] , [25] , [26] . Eosinophil-mediated killing of filarial nematodes in particular has been the subject of much attention [3] , [5] , [6] , [8] , [13] , [14] , [16] , [17] , [26] . Interestingly within the filarial nematodes , the efficacy of eosinophils as effector cells varies with both the host and nematode species concerned . In the present study , we have examined the role of eosinophils during infection with the human parasite and causative agent of human lymphatic filariasis , B . malayi . Using mice that specifically lack eosinophils [1] or the eosinophil granule proteins , EPO [27] and MBP-1 [28] , we definitively confirm and extend our previous results that eosinophils are needed for protection against primary , but not challenge infection of B . malayi Mf [5] . We also show that this protection is not solely dependent upon release of either EPO or MBP-1 . Additionally , we show that eosinophils contribute to nematode-induced lung pathology and impairment of lung function , and EPO and MBP-1 alone can regulate nematode-induced IgE responses and goblet cell mucus production respectively ( results are summarised in Table S1 ) . In accord with our Mf results , eosinophils are also necessary for killing primary , but not secondary , L3 infections of B . malayi [26] . Several studies have shown that eosinophilic granules can be directly toxic to Brugia sp . in vitro . For example , MBP-1 , EPO , eosinophil cationic protein ( ECP ) , and eosinophil-derived neurotoxin ( EDN ) can all kill Mf , and EPO is particularly potent in this regard [13] . However , our present in vivo studies showed that absence of EPO or MBP-1 alone is not sufficient to abrogate Mf clearance . Similarly , survival of B . pahangi L3 ip was not altered in either EPO−/− or MBP-1−/− mice [26] . Our studies used B . malayi microfilariae alone in the absence of adult worm infection , which lead primarily to induction of type 1 responses , while secondary and challenge Mf infections are known to induce strong type 2 responses as is more usual in natural filarial infection [23] , [24] , [29] . To date , however , the literature using different Brugia sp . nematode stages is remarkably consistent and suggests that eosinophilic granules are either redundant in their ability to kill Mf and L3 of Brugia sp . or that eosinophil-dependent clearance of parasites during primary infection is independent of granule deposition and is a downstream function of eosinophil presence such as regulation of responses via eosinophil cytokine release . Studies using a rodent filarial nematode , Litosomoides sigmodontis , show differing results . While survival of a primary infection of L . sigmodontis is not altered in eosinophil-deficient mice; eosinophil-intact mice have enhanced nematode development , the established nematodes are significantly longer and the infections achieve patency more rapidly suggesting that eosinophil presence drives faster nematode development [16] . Another study showed that absence of either EPO or MBP-1 resulted in greater adult L . sigmodontis establishment following L3 infection , and longer female nematodes , although worms did not survive until patency [14] . This latter study suggests a role for eosinophils in protection against this rodent nematode , although their role may be indirect . Indeed , the absence of eosinophil granule proteins caused several downstream alterations in cytokine responses [14] . For example , thoracic cavity macrophages produced higher IL-10 in both infected MBP-1−/− and EPO−/− mice , while thoracic cavity and splenic T cells had reduced IL-4 production in both strains and reduced IL-5 production in EPO−/− mice [14] . Thus , there is a precedent in this rodent-filarial nematode model for eosinophil granule proteins exerting a role in protection indirectly by cytokine production , eosinophil-induction or activation of other effector cells [14] . Protective immunity against a related filarial worm , Strongyloides stercoralis , shows redundancy in the mechanisms mediated by neutrophils and eosinophils . In the primary immune response , larvae are killed either via an eosinophil MBP-1-dependent mechanism , or by a neutrophil myeloperoxidase ( MPO ) -dependent mechanism . Neutrophil MPO , but not eosinophils , are required for challenge protective immunity [30] . The importance of eosinophils was shown in antibody-treated WT mice while PHIL mice had developed a compensatory protective mechanism . The observation in our study that mast cells are significantly up-regulated in PHIL mice could provide one such compensatory mechanism . Redundancy of granulocyte killing mechanisms has yet to be investigated in models using human filarial nematodes , however , unlike S . stercoralis , we have previously shown that neutrophils are not necessary for killing a primary or challenge B . malayi Mf infection [31] . In both PHIL and EPO−/− mice , IgE levels were significantly higher than WT mice . This suggests that mouse eosinophils down-regulate induction of Mf-induced IgE . However the mechanism for this is not clear as mouse eosinophils lack FcR for IgE , including CD23 , the low affinity IgE receptor that negatively regulates IgE production . Filarial nematode-specific IgG and IgM responses in both PHIL and eosinophil granule-less mice were similar to those found in our previous work [23] , [24] . As IgE regulation was also dependent on EPO release , it is possible that alterations in the cytokine milieu due to the absence of eosinophils or their granules may have led to dysregulation of IgE levels or indeed that EPO itself directly regulates IgE [14] . Eosinophils themselves are known to produce a variety of cytokines , including IL-4 , IL-3 , IL-6 , IL-13 , IL-10 , IL-25 , GM-CSF , TGFβ1 and TNFα [32] , [33] . Furthermore , eosinophils have been shown in several models to be important for recruitment and activation of effector Th2 cells . They are thought to drive type 2 responses principally by producing IL-4 and IL-13 very early in the immune response , presenting antigen to naïve CD4+ T cells and by secreting chemoattractants that further recruit Th2 effector cells . Indeed the absence of eosinophils in several mouse models of acute and chronic allergic inflammation is accompanied by attenuated Th2 immunity [32] , [33] . However , in our experiments with Mf infection we did not find significant changes in Type 2 cytokine responses . The IFN-γ was sustained in primary infection in the absence of eosinophils , which could suggest a rise in type 1 responses or it could reflect the fact that Mf , which induce IFN-γ , survive for longer in these mice . While we can not rule out the possibility that the sampling time-points for type 2 cytokines were sub-optimal , our measures of other Type 2-mediated parameters such as IgE production and goblet cell metaplasia , which reflect historical type 2 cytokine production , were actually enhanced in eosinophil absence . In addition downstream components of type 2 immunity , such as IgE and goblet cell metaplasia , were increased in the absence of eosinophils . Overall , our results suggest that the type 2 responses generated during Mf challenge infection are not entirely dependent upon eosinophil presence and could be compensated for by non-eosinophil cell sources such as mast cells , NK T cells , γδ T cells or basophils [24] . Indeed an increase in mast cells was seen in PHIL mice following challenge infection . Future work will dissect the need for eosinophil-derived IL-4 or IL-13 in differentiation and mobilisation of Th2 cells in different pathogen models . The implication from our previous work using the Brugia-Mf mouse model is that eosinophils while acting as effector cells in primary infection against Mf , may in fact mediate pathological damage during challenge and/or chronic infection [5] . In wild type mice we have shown significant eosinophil recruitment to the lung occurs during primary and challenge infection . The absence of MBP-1 alone in the above experiments increased eosinophil levels in naïve and infected mice , while eosinophil number was reduced in EPO−/− mice following challenge infection . However , evidence for the role of eosinophils and/or their granules in recruitment of cells to the site of infection/damage varies . For example , recruitment of cells into subcutaneous diffusion chambers following primary S . stercoralis infection of WT , EPO−/− and MBP-1−/− mice did not differ [30] , eosinophil recruitment to the peritoneal cavity following B . pahangi L3 infection of EPO−/− but not MBP-1−/− mice was reduced [26] while eosinophil recruitment to the thoracic cavity of L . sigmodontis infected EPO−/− mice increased [14] . Currently the precise role of eosinophil granule proteins in haemopoiesis , and/or their autocrine effects on bone marrow progenitors , differentiation of lineage-committed precursors , and the survival of mature metamyelocytes in circulation is the subject of investigation . Indeed , recent work has shown that mice lacking both MBP-1 and EPO have very few eosinophils and appear to regulate either eosinophil precursor haemopoiesis , survival and/or development by a mechanism targeting eosinophil progenitor survival [34] . Our study showed that the absence of eosinophils ameliorated nematode challenge-induced alterations in lung physiology . However , this was not explained by changes in collagen deposition and mucus production was greater in PHIL mice . In EPO−/− mice there was also a reduction in nematode-infection induced lung hyper-responsiveness and in the baseline respiratory rate , suggesting that release of EPO plays a role in nematode-induced lung pathology . However , although MBP-1 is deposited on lung epithelial tissue following challenge with live B . malayi Mf , our results suggested that eosinophilic release of MBP-1 is not involved in Mf-induced alterations in lung physiology [17] . Interestingly , the absence of MBP-1 resulted in increased mucus-producing goblet cells suggesting that MBP-1 itself , and not EPO , may affect goblet cell metaplasia . We conclude that goblet cell metaplasia is not involved in nematode challenge-induced respiratory changes . Further studies will be needed to pinpoint the exact cause of eosinophil-mediated nematode-induced changes in lung function . In an OVA-challenge mouse model of asthma , absence of neither EPO nor MBP-1 alters AHR [27] , [28] . Indeed , in the pathological condition in humans associated with filarial infection , tropical pulmonary eosinophilia ( TPE ) , levels of pathology appear to be most closely correlated with presence of the eosinophil granule protein , eosinophil-derived neurotoxin ( EDN ) [25] . This merits further investigation , however , currently mice deficient in this granule protein are not available . Studies of pulmonary cell recruitment in allergy models have shown a very different picture to that of nematode-induced pathology . PHIL mice used in an OVA-challenge allergy model , do not develop PAS+ lung goblet cells , unless they are adoptively transferred with T cells and eosinophils suggesting that eosinophils are needed for the recruitment of Th2 cells to the lung [1] , [35] . In a different eosinophil-less mouse , ΔDbl-GATA , OVA-challenge suggested that eosinophils are not involved in inflammation or AHR but are involved in lung remodelling responses such as collagen deposition and increases in airway smooth muscle [2] . Challenging a number of eosinophil-defective mice with Aspergillus allergen , Fulkerson et al . ( 2006 ) also reported that eosinophil absence is associated with a significant reduction in pulmonary Th2 gene expression and mucus production [36] . However , in a Nippostrongylus brasiliensis nematode-mouse model of lung inflammation , eosinophils are not required for pulmonary T cell recruitment , IgE production or worm expulsion during primary infection while they do play a limited role in activation or recruitment of CD4+ T cells to the lung following challenge [37] . Thus , data from these studies highlight the complexity of pulmonary immune responses and also highlight differences between the role of eosinophils in allergy and different nematode-induced pathology models . Overall our results reveal that eosinophils actively participate in protective immune responses against a nematode parasite . In addition , eosinophils and their granules are influential as negative regulators of specific parasite-induced lung inflammatory and adaptive responses . This study highlights the importance of eosinophils as effector cells , as damage-inducing cells and as supervisory cells that shape both innate and adaptive immunity .
Animal experiments were conducted in accordance with our project licence ( PPL 70/7243 ) , which was approved by the Home Office under the Animal Scientific Procedures Act ( 1986 ) . The project was approved by the local Ethical Review Committee at the Royal Veterinary College . PHIL mice ( deficient in eosinophils ) [1] , MBP-1−/− mice [28] and EPO−/− mice [27] on a C57Bl/6 background were obtained from Prof JJ Lee ( Mayo Clinic , Arizona ) and bred at the Royal Veterinary College . PHIL mice were generated using a cytocidal protein under the control of the EPO promoter , thus ablating EPO-expressing cells [1] . Wild type C57Bl/6 mice were purchased from Harlan UK at 6–8 weeks of age . All mice used in experiments were male , age-matched between groups and housed in individually-ventilated cages . B . malayi-infected gerbils ( Meriones unguiculatus ) were purchased from TRS Labs ( Georgia , USA ) and were housed in standard conditions . Mf were obtained by peritoneal lavage with RPMI 1640 and gerbil cells were removed by centrifugation over lymphocyte separation media ( Flow Labs , McLean , VA , USA ) [38] . Soluble Mf extract was prepared as described previously [39] . Groups of four to six individual , eight week old , male C57Bl/6 wild-type ( WT ) mice and/or gene-targeted mice were either left uninfected ( naïve ) , or they were injected with 2×105 B . malayi Mf i . v . ( primary ( 1° ) infection ) or they were immunised on three occasions with 200 µg soluble Mf extract s . c . prior to challenge with 200 , 000 B . malayi Mf i . v . ( challenge ( 2° ) infection ) ( Figure 1 ) . At days 5–6 , 10–12 and 20–21 post infection with Mf , immunological and pathological parameters of mice were measured in all groups of mice . Parasitaemia was monitored as previously described [39] . Whole body plethysmography was used to determine enhanced pause ( Penh ) . While there is controversy over the validity of Penh as a parameter for lung mechanics or resistance [40] , [41] , [42] , [43] , we use our measurements as an added indicator of physiological change during the infection process in conjunction with a number of other cellular histopathological measurements in the lung . A whole body plethysmograph ( EMMS , Bordon , Hants ) was used according to the manufacturer's instructions to determine Penh and breathing frequency in mice . Briefly , mice were placed in plethysmograph chambers and allowed to acclimatise for 20 min before a 4 min control period was recorded . Mice were exposed to varying concentrations of methacholine , up to a maximum of 100 mg/ml , and lung function was monitored for 5 min following each methacholine challenge . Mice were rested for 5 min between each challenge to allow lung function to return to baseline before the next challenge . A cannula was inserted into the trachea , and bronchoalveolar lavage was performed with 900 µl PBS . BALF was centrifuged at 13 , 000× g for 10 min , supernatant was removed and stored at −20°C . Cells were re-suspended , counted and cyto-centrifuged onto a microscope slide at 800× g for 5 min . Slides were air dried for 20 min , prior to fixation for 2 min in 50% methanol and 50% acetone . Fixed slides were stained with May-Gruenwald and Giemsa ( 10% solution in Giemsa buffer ) . Lungs were fixed in neutral buffered formalin , dehydrated in increasing concentrations of ethanol and processed in a Tissue Tek processor . Tissues were embedded in wax and 6 µm sections were cut . Sections were stained with haematoxylin and eosin , Periodic Acid Schiff or Martius Scarlet Blue . Mf-specific immunoglobulin levels were measured by ELISA [5] . Briefly 96-well plates were coated overnight at 4°C with 1 µg/ml soluble Mf extract ( MfAg ) in 50 µl carbonate buffer ( pH 9 . 6 ) . After blocking each well with 10% FCS in carbonate buffer , the plates were incubated with individual mouse sera diluted 1∶50 in PBS 0 . 5% Tween-20 . Antigen-specific antibodies were detected using horseradish peroxidase ( HRP ) -conjugated goat anti-IgM ( Southern Biotechnology Associates , Birmingham , AL , USA; SBA 1020-05 ) , anti-IgG1 ( SBA1070-05 ) , anti-IgG2a ( SBA 1080-05 ) , anti-IgG2b ( SBA 1090-05 ) or anti-IgG3 ( SBA 1100-05 ) . 3 , 3′ , 5 , 5′-Tetramethylbenzidine ( TMB ) ( Sigma ) was used as the substrate . Plates were read at 405 nm . Total IgE was measured by ELISA as previously described [44] . Spleen cells were cultured at 5×106 cells/ml in RPMI plus 5% FCS and 10 µg/ml MfAg , 5 µg/ml concanavalin A ( Con A ) or 1 µg/ml anti-CD3 as previously described [45] . Cells were incubated for 72 h at 37°C and supernatants were removed for cytokine analysis . The concentration of the cytokines , IL-4 , IL-5 , IL-10 and IFN-γ in the recovered supernatants was determined by sandwich ELISA . Purified and biotinylated monoclonal antibody pairs , 11B11 and BVD6-24G2 ( IL-4 ) , TRFK5 and TRFK4 ( IL-5 ) , JES5-2A5 and SXC-1 ( IL-10 ) , R46A2 and XMG1 . 2 ( IFN-γ ) were purchased from BD Pharmingen ( San Diego , CA , USA ) . Cytokine concentrations were measured against recombinant murine cytokine standards as previously described [45] . Briefly , each well was coated with capture antibody overnight at 4°C . Plates were washed and incubated with supernatant or recombinant cytokine standard for 2 h at 37°C . Following washing , biotinylated anti-mouse cytokine antibodies ( 2 µg/ml ) were incubated for 45 min at 37°C , wells were washed again and incubated with streptavidin-HRP ( R&D ) for 30 min at 37°C . In the IL-10 ELISA , extravidin-alkaline phosphatase ( AP ) ( Sigma ) was used at 1 µg/ml . Finally , plates were washed and developed either with TMB substrate for HRP-conjugated antibodies ( BD Pharmingen ) or for AP-conjugated antibody , p-nitrophenyl phosphate ( pNPP ) was used as a substrate . Plates were read at 450 nm for TMB and 405 nm for pNPP . IL-13 was measured using a Quantikine ELISA kit according to the manufacturer's instructions ( R&D Biosciences ) . All data are expressed as mean ± SE . One-way ANOVA with Bonferroni's post-hoc analysis was used for intergroup comparisons between infected PHIL , MBP-1−/− , EPO−/− and WT mice . P-values lower than 0 . 05 were considered statistically significant . Prism ( Graphpad Software Inc . ) statistical analysis software was used to determine significance .
|
Eosinophil recruitment is a classic characteristic of both allergic and parasitic helminth diseases . Elucidation of the role of eosinophils in these diseases is of pivotal importance for understanding the mechanisms of protection and the development of pathology . In the last few years , the part played by eosinophils in helminth-defence has been dissected using in vivo models and their importance in protection has been shown to be highly specific to the host-parasite combination . This study dissects the role of eosinophils during infection with the human lymphatic filarial parasite , Brugia malayi , which causes the major neglected tropical disease , lymphatic filariasis . In particular , we study the role of the eosinophil as a double–edged sword in generating both protection and pathology . We definitively confirm the importance of eosinophils in protection against B . malayi microfilariae and show that protection is not mediated by release of the eosinophil granule proteins , major basic protein or eosinophil peroxidase alone . Overall , we reveal that during an infection with B . malayi microfilariae , eosinophils are critical for primary protective responses . However , eosinophils contribute to nematode-induced lung dysfunction , while additionally , eosinophil granules are important negative regulators of parasite-induced lung inflammatory and some adaptive immune responses .
|
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"Results",
"Discussion",
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"Methods"
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"immunopathology",
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2014
|
Eosinophils Are Important for Protection, Immunoregulation and Pathology during Infection with Nematode Microfilariae
|
How did evolution generate the extraordinary diversity of vertebrates on land ? Zero species are known prior to ~380 million years ago , and more than 30 , 000 are present today . An expansionist model suggests this was achieved by large and unbounded increases , leading to substantially greater diversity in the present than at any time in the geological past . This model contrasts starkly with empirical support for constrained diversification in marine animals , suggesting different macroevolutionary processes on land and in the sea . We quantify patterns of vertebrate standing diversity on land during the Mesozoic–early Paleogene interval , applying sample-standardization to a global fossil dataset containing 27 , 260 occurrences of 4 , 898 non-marine tetrapod species . Our results show a highly stable pattern of Mesozoic tetrapod diversity at regional and local levels , underpinned by a weakly positive , but near-zero , long-term net diversification rate over 190 million years . Species diversity of non-flying terrestrial tetrapods less than doubled over this interval , despite the origins of exceptionally diverse extant groups within mammals , squamates , amphibians , and dinosaurs . Therefore , although speciose groups of modern tetrapods have Mesozoic origins , rates of Mesozoic diversification inferred from the fossil record are slow compared to those inferred from molecular phylogenies . If high speciation rates did occur in the Mesozoic , then they seem to have been balanced by extinctions among older clades . An apparent 4-fold expansion of species richness after the Cretaceous/Paleogene ( K/Pg ) boundary deserves further examination in light of potential taxonomic biases , but is consistent with the hypothesis that global environmental disturbances such as mass extinction events can rapidly adjust limits to diversity by restructuring ecosystems , and suggests that the gradualistic evolutionary diversification of tetrapods was punctuated by brief but dramatic episodes of radiation .
Tetrapods , the limbed vertebrates , include mammals , amphibians , and reptiles ( including birds ) , and today comprise more than 30 , 000 species . Alongside plants and insects , they are key components of the non-marine biota and play a diverse range of ecological roles . Patterns of tetrapod diversification from their Late Devonian origin to the present day are therefore central to understanding the evolution of terrestrial ecosystems . Almost all extant tetrapod species belong to just a few hyper-diverse groups , including neoavian birds , placental mammals , frogs , and squamates ( e . g . , [1] ) . Although both fossils and molecular clock analyses indicate Mesozoic origins for these hyper-diverse groups [2–7] , there is significant controversy over the timing of major increases in their species diversity . This controversy is embodied by disagreements about the divergence times of Placentalia and Neoaves , the clades that include most extant mammal and bird species . For example , fossils suggest that placental mammals were either absent , or so rare as to be undiscovered , prior to the end of the Cretaceous [8] , and phylogenomic studies of birds indicate that deep neoavian divergences were concentrated in the earliest part of the Cenozoic [9] . This evidence contradicts most other molecular clock estimates , which imply substantial origination of higher taxa within Placentalia [5] and substantial species diversification within Neoaves [6] before the Cenozoic . Nevertheless , the timings of deep divergences within extant tetrapod clades have generally been interpreted as supporting an “expansionist” mode of diversification , under which unbounded and essentially exponential diversification led to significant , near-continuous increases in species richness on land , especially since the late Mesozoic [2 , 10 , 11] . Patterns of fossil tetrapod diversity have also been interpreted as evidence of expansionist diversification on land [2 , 10–14] . This contrasts with strong evidence for constrained diversification in the fossil records of shallow marine animals [15–19] , planktonic Foraminifera [20 , 21] , North American mammals [22] , and mammalian subgroups [23–25] . These groups have rich , densely sampled fossil records that demonstrate the existence of diversity-dependent controls of diversity patterns . Under diversity dependence , speciation rates , extinction rates , or both vary with standing diversity such that net diversification rates approach zero or become negative when diversity is high [15 , 26–28] . This has the general effect of “flattening” diversity curves and generating long intervals of near-static diversity , but need not imply a permanently fixed upper limit to species richness ( e . g . , [28] ) . The population- and community-level processes causing diversity dependence on macroevolutionary scales are not well understood . However , it is possible that the availability of ecological opportunity regulates species richness within local communities via agonistic interspecies interactions such as competition over finite resources , thereby influencing global patterns of diversification through time [15 , 17 , 29] . The question of whether expansionist [2 , 30] or more constrained [15 , 16 , 18 , 31] patterns of diversification characterise the evolution of life on Earth is among the most contentious macroevolutionary questions [10 , 28 , 29 , 32 , 33] . Its answer has substantial implications for the origins and future of the enormous scope of extant biodiversity ( estimated at 2–8 million species [34] ) , and for assessing whether equilibrial processes inferred from the study of island biogeography are applicable to global spatial scales and geological time spans [26–28 , 32] . Animal diversity on land is especially high , comprising 75%–95% of multicellular species on Earth [35 , 36] , and evolved in significantly less time than did the lower diversity of marine animals [11] . This observation has been used to justify an “emerging consensus” that species diversification on land was essentially exponential , irrespective of the evidence for constrained diversification in the marine realm [10 , 11] . The expansionist paradigm implies that ecological constraints on diversification rates are either non-existent or unimportant in determining patterns of global diversification on geological timescales [2 , 32] . This could be possible if competitive ecological interactions among species are rare , or if their effects are typically weakened by evolved responses such as niche partitioning ( e . g . , [29 , 37] ) . Under an expansionist model , per-lineage net diversification rates in major clades of terrestrial animals have been positive and high on long timescales , commensurate with the attainment of high biodiversity in the present ( although other patterns , such as mass extinction events and adaptive radiations , may be evident on shorter timescales ) [2 , 13] . This model implies that substantial relative increases in species richness should occur during time intervals spanning hundreds of millions of years . Nevertheless , patterns of diversity across all tetrapods on land and their implications for macroevolutionary dynamics at larger scales have not been rigorously characterised on geological timescales , so this prediction has not been explicitly tested . So far , the fossil evidence for strongly expansionist diversification on land is based on iterations of a 30-year-old compendium of the geological ranges of non-marine tetrapod families [12 , 38] . This range-based , family-level approach has three shortcomings . Firstly , although range-based approaches have some utility in filling the unsampled gaps within fossil taxon ranges , counts of range-based data do not address core biases affecting fossil diversity counts , such as uneven sampling of specimens , environments , or geographic space through geological time , or the “Pull of the Recent” and related effects [19 , 39–41] . Range-based approaches are also prone to edge effects ( [42]; an issue that also applies to phylogenetic diversity estimates [43] ) . Secondly , the composition of taxonomic families is determined by an inconsistent set of subjective criteria including phenotypic distinctiveness , species diversity , and phylogenetic monophyly ( e . g . , [44] ) . We do not know the “perceptual algorithms” governing the delimitation of named clades such as families , or how their application varies among geological intervals or across the Tree of Life . However , we do know that they can profoundly bias studies of diversification [45] . Thirdly , and perhaps most importantly , the processes of evolutionary diversification act directly only on individual evolving lineages and so are most adequately represented at species level . We analysed non-marine ( terrestrial plus freshwater ) diversity patterns across Tetrapoda , applying sample standardization approaches [18 , 46] to species occurrence data from the Paleobiology Database ( http://paleobiodb . org ) , accessed via Fossilworks ( http://fossilworks . org ) on 22 January 2015 . These data result from a concerted effort to document the Mesozoic–early Paleogene ( Ypresian ) tetrapod fossil record , led by the authors of this paper , and representing an estimated 6 , 520 h of work by more than 70 contributors [47] . Flying taxa with Lagerstätten-dominated records ( birds , bats , pterosaurs ) that provide little robust information on species richness were excluded from our analyses , the implications of which are discussed below ( see Materials and Methods and Results and Discussion ) .
“Face-value” observed counts of genera and species occurring globally within time bins approximating 9 million years ( Myr ) ( S1 Table; S1 Appendix ) provide little support for exponential diversity increases during the Mesozoic . These counts resemble previously reported global tetrapod family counts [12 , 38] in several details , including the occurrence of Paleogene diversity levels that are several times greater than those of most Mesozoic intervals ( Fig 1 ) . Furthermore , within the Mesozoic , direct counts of families , genera , and species are all highest in the final two stages , the Campanian and Maastrichtian ( Fig 1 ) . Nevertheless , counts of genera and species show different long-term patterns than counts of families . General linear models assuming a negative binomial error distribution and ln ( ) link function were used to predict global family counts from geological age across the entire Mesozoic . We found a statistically significant , negative slope that is robust to the exclusion of influential data points ( Table 1 ) , indicating a long-term trend of increasing family counts through time . By contrast , statistically significant trends in genus and species counts are only supported if the first Triassic time bin ( Tr1; S1 Table ) , an influential data point with high leverage , is excluded ( Table 1 ) . Furthermore , the significance of this increase is largely due to the occurrence of high taxon counts in the final two time bins of the Cretaceous ( K7 and K8 ) , and the slope becomes marginally non-significant when these time bins are also excluded ( Table 1 ) . Notably , Late Triassic and latest Jurassic taxon counts also exceed those of most Cretaceous time bins . These observations indicate either that substantial species and genus diversification occurred only in the latest Mesozoic or that oversampling of latest Mesozoic terrestrial faunas has inflated face-value diversity counts for the Campanian and Maastrichtian . The latter possibility is more consistent with our further analytical results , described below . The observation that counts of lower-level taxa do not show a robust trend of Mesozoic increase ( Fig 1B and 1C; Table 1 ) refutes the proposition that species counts should reveal the hypothesised underlying exponential nature of diversification more prominently than do studies at higher taxonomic levels [30 , 49 , 50] . Pooled regional face-value genus and species counts ( Fig 2 ) also show no evidence for a Mesozoic trend of increase . General linear models predicting these counts using geological age across the Mesozoic have non-significant slopes ( Table 2 ) . Taxon counts for the first time bin ( Tr1 ) in Asia and Africa , and for the last two time bins ( K7 , K8; S1 Table ) in North America are identified as influential data points with high leverage . Significant negative slopes are obtained only when the influential Tr1 data points are excluded from the analysis on their own , and not when all influential data points are excluded together ( Table 2 ) . The absence of a trend of increasing regional taxon counts through the Mesozoic is consistent with the observation that most continental regions lack any Late Cretaceous increase ( Fig 2 ) , with the exception of North America , where Campanian and Maastrichtian deposits are disproportionately well sampled , with approximately six to eight times as many collections as are known from the most intensively sampled time intervals in regions outside of North America , or two to three times as many collections as the most highly sampled other North American intervals ( Fig 2C ) . Regional subsampling results indicate a protracted interval of only limited increases in standing diversity spanning the entire Mesozoic ( Fig 5A ) . Similar subsampled diversity estimates were obtained for widely separated time intervals such as the Maastrichtian ( 72 . 1–66 mega-annum [Ma] ) and Kimmeridgian–Tithonian ( 157–145 Ma ) of Europe , and the Kimmeridgian–Tithonian and Norian ( 228–208 Ma ) of North America ( Fig 5A and 5E–5G ) . The sporadic availability of data that is rich enough for rigorous diversity estimates makes it difficult to infer short-term patterns of change in standing diversity , although Cretaceous values seem generally higher than those of the Triassic and Jurassic . Nevertheless , we are able to estimate the resultant long-term net diversification rate using general linear models ( Table 4 ) , acknowledging that this represents a simplification of potentially more complex short-term patterns . The general linear model using geological time to explain subsampled diversity , pooled across geographic regions for the entire Mesozoic , demonstrates only a very weak , but significant slope ( Fig 5A; Table 4; p = 0 . 02 ) , indicating a trivial net diversification rate of 0 . 00335 ln ( species ) /Myr ( ±2 standard errors yields 0 . 00089–0 . 00581 ln ( species ) /Myr ) . This implies an expected increase in species richness of 0 . 637 ln ( species ) , or 89% over c . 190 Myr ( ±2 standard errors yields 18%–202%; and when within-region geographic spread is considered to be a bias the net diversification rate is reduced to 0 . 00148 ln ( species ) /Myr , predicting a diversity increase of 32%; Table 4 ) . This expected value is equivalent to less than one net speciation per lineage , and comparable to three standard deviations of the regression residuals ( s . d . = 0 . 28 ) . Therefore , short-term diversity fluctuations and statistical counting error have a similar magnitude to our estimate of the long-term expansion of diversity through the Mesozoic . The failure of short-term and inter-regional diversity variations to sum to a greater long-term change would be direct evidence of diversity dependence if we could demonstrate that the proportion of these short-term variations attributable to counting error was low [22 , 57] . Our estimated long-term diversification rate of 0 . 00335 ln ( species ) /Myr is particularly striking in context of the increase in tetrapod diversity that must have occurred during the c . 130 million years prior to our study interval , from the Late Devonian origin of tetrapods to the early Mesozoic , which entailed substantially more than a doubling of diversity ( e . g . , [38 , 58 , 59] ) . This can be demonstrated by approximation , assuming that Late Permian diversity was comparable to Early Triassic diversity , which is estimated from the general linear model of subsampled diversity on time as 2 . 62 ln ( subsampled species ) . The transition from ln ( 1 ) to ln ( 2 . 62 ) over 130 million years implies a Paleozoic long-term net diversification rate of 0 . 0202 ln ( species ) /Myr , which would generate more than a 40-fold increase in diversity over 190 Myr of Mesozoic time . This estimate is conservative: it could only increase if Late Permian diversity was higher than that of the Early Triassic , as is possible due to the occurrence of the Permian/Triassic boundary mass extinction event ( e . g . , [60] ) . The overall pattern therefore seems to be one of substantial reductions in the long-term net diversification rate of tetrapods during the Paleozoic and Mesozoic , representing the first 87% of their evolutionary history . Furthermore , equation A25 of reference [61] ( see Materials and Methods: Raup Equation ) gives the expected diversity of a clade after a specified time under specified birth and death rates , conditioned on the observation that the clade survived until that time had elapsed . We assumed that the tetrapod crown group originated 100 Myr earlier in the Late Carboniferous , and then specified a net diversification rate of 0 . 00335 ln ( species ) /Myr ( conservatively assuming the higher diversification rate implied by a direct bias model ) , and per-lineage death rates of 0 . 10 , 0 . 15 , 0 . 20 , 0 . 25 , and 0 . 30 ln ( extinctions ) /Myr ( centred on values estimated for Cenozoic North American mammals [22] ) . This gives expected Early Triassic regional diversities of 13 . 3 , 19 . 2 , 25 . 2 , 31 . 1 , and 37 . 0 species . We do not know the actual ( rather than observed or subsampled ) regional diversities of any studied intervals . However , these expected values of Late Palaeozoic regional diversity obtained under the estimated Mesozoic net diversification rate are lower than the face-value regional species counts of Tr1 in Asia ( 121 species ) , Africa ( 96 species ) , and Europe ( 41 species ) , and of Tr2 in South America ( 34 species ) . The diversity counts for these relatively well-sampled regions are not corrected for the possible existence of multiple chronofaunas that could cause counts to exceed the standing diversity at any single time horizon , and they immediately follow the Permian/Triassic extinction event rather than representing Late Permian diversity . An abrupt and substantial increase in regional subsampled diversity is apparent in the earliest Paleogene , following the end-Cretaceous mass extinction 66 Mya ( Fig 5A ) . This increment cannot be explained by bias due to paleogeographic sample spread , which does not change over the boundary ( Fig 4C ) . It results entirely from an increase in mammalian species diversity ( Fig 5B ) [62 , 63] , which is disproportionately large compared to the loss of dinosaur diversity ( Fig 5D ) . The diversity of non-dinosaurian , non-mammalian tetrapods ( “herps”; Fig 5C ) does not change substantially over the Cretaceous/Paleogene ( K/Pg ) boundary on the temporal resolution of our study , although a major , short-term K/Pg turnover certainly occurred among herps , including squamates [64] . Nevertheless , our subsampling results tentatively suggest a doubling of herp diversity around the Jurassic/Cretaceous boundary ( Fig 5; S1 Fig; S1 Appendix ) , consistent with patterns of subsampled fossil turtle diversity [65] . One possibility is that a taxonomic restructuring of terrestrial ecosystems at the K/Pg boundary rapidly established a new dinosaurian/mammalian diversity equilibrium that substantially exceeded the Mesozoic baseline . Such rapid equilibration could only be possible under strong diversity-dependence of diversification rates ( e . g . , [28 , 46] ) . However , mammals , which have increased proportional representation in Cenozoic ecosystems , possess complex teeth , allowing more precise taxonomic identifications from highly fragmentary material than can be diagnosed in fossils of the other highly diverse extant clades ( lissamphibians , squamates , and birds ) , and potentially also Mesozoic dinosaurs . The relatively greater ability to diagnose mammalian species based on fragmentary fossil remains compared to non-mammalian tetrapods is evident in our results: the ratio of mammalian species to species of lissamphibians plus squamates on Earth today is about 1:3 , but our subsampled diversity estimates from fossil data yield a ratio of >5:1 in the Paleocene . This suggests that an increase in the proportion of mammalian species within the total terrestrial tetrapod fauna should result in an increase in apparent species diversity in the fossil record , even in the absence of any change in actual tetrapod diversity . It is therefore possible that this apparent Paleocene increase in diversity at least partly reflects a change in the nature of terrestrial vertebrate taxonomy , and is not necessarily a genuine evolutionary phenomenon . Patterns of tetrapod alpha diversity , measured as counts of taxa found within individual fossil localities , are consistent with slow Mesozoic diversification among non-flying tetrapods . Specimens that are taxonomically determinate at the species level are present in 4 , 357 Mesozoic localities . Of these , just a handful of localities yield substantially greater species counts than most others , including the Late Triassic Placerias Quarry of North America ( e . g . , [66] ) , Late Jurassic Como Bluff Quarry 9 of North America ( e . g . , [67] ) and Guimarota Mine of Portugal [68] , and the Late Cretaceous Lull 2 Quarry and Bushy Tailed Blowout [69] of North America ( Fig 6A ) . The rare and sporadic occurrence of maximally-diverse fossil localities presents a challenge concerning our ability to resolve patterns of local diversity in the fossil record . Nevertheless , the diversities of these maximally diverse localities increases approximately 3-fold through the Mesozoic , or 2-fold if specifically indeterminate occurrences , which can represent the occurrences of distinct clades and are therefore relevant to diversity counts , are included ( Fig 6B ) . These values are comparable to the diversity increase estimated from regional subsampled diversities . It is notable that the maximal within-locality counts generally occur within those intervals containing the greatest numbers of localities such as the Norian ( Triassic 4 ) , Kimmeridgian–Tithonian ( Jurassic 6 ) , Campanian and Maastrichtian ( Cretaceous 7 and 8 ) , suggesting that the intensity of fossil collection activities plays a role in determining the apparent diversity of local communities sampled in the fossil record . Notably , almost all the localities exhibiting high species richness have been intensively bulk sampled for microvertebrate remains—the highest maximal species richness occurs in the latest Cretaceous ( Campanian and Maastrichtian ) North American localities , which have been intensively bulk sampled ( Fig 6D ) . At present , within-locality taxon counts do not suggest any increase in diversity during the early Cenozoic . The results of our alpha diversity analyses should be treated as “first pass” estimates that should be investigated in more detail by future studies , because ( 1 ) we did not apply subsampling methods , ( 2 ) we did not consider potential environmental or paleogeographical differences between these localities that might affect diversity counts , ( 3 ) we did not study the specimens known from these localities to determine the minimum taxon count based on unreported or undiagnosed material , and ( 4 ) we did not quantify biases resulting from the likely increased ability of taxonomists to identify fragmentary specimens belonging to extant clades—a bias that could cause relative underestimation of alpha diversity in the Triassic , preceding the origins of most tetrapod crown groups . The Mesozoic–early Cenozoic has previously been regarded as an episode of unbounded diversification , culminating in substantially increased tetrapod diversity on land [2 , 10–13] . In contrast to this paradigm , our analyses indicate less than a doubling of tetrapod diversity through the Mesozoic , and imply a near-zero long-term net diversification rate . Substantial increases in regional tetrapod diversity were absent from the entire Mesozoic , both for directly counted and subsampled fossil species ( Figs 1C and 5 ) . Furthermore , a possible dramatic expansion of tetrapod diversity occurred in the early Paleogene . Our conclusions are strongest if differences in paleogeographic sample spread among regions and intervals are considered to be a bias , in which case Mesozoic regional tetrapod diversity is estimated as being almost static on long time scales ( Table 4 ) . Furthermore , first-pass maximal alpha diversity estimates also indicate slow diversification rates ( Fig 6 ) , demonstrating that similar patterns occur at local and regional geographic scales . This is consistent with , though not conclusive for , the hypothesis that ecological constraints within local communities could slow diversity increases and thereby regulate diversity at larger scales [28 , 70] . Our results do not exclude the possibility that an increase in the number of distinct biogeographic regions due to continental fragmentation during the Cretaceous resulted in a greater increase in global diversity than that seen in regional diversities . Our estimated long-term net diversification rates of 0 . 00335 or 0 . 00148 ln ( species ) /Myr are 1–2 orders of magnitude less than those reported from studies of extant tetrapods ( e . g . , [1 , 6] ) . This difference is unlikely to be explained by underestimation of absolute biodiversity resulting from the incompleteness of the fossil record: estimated diversification rates rely only on accurate inference of relative , not absolute , changes in diversity through time; although we cannot altogether rule out any contribution of fossil record biases ( e . g . , the possibility that preservational biases could mask an increase in the diversity of small-bodied taxa ) . Regardless of fossil record biases , a difference between net diversification rates estimated from fossils and those from extant taxa might be expected , because even the richest phylogenies of living taxa lack information on the contributions of entirely extinct clades to diversity dynamics [23 , 71 , 72] . Specifically , the contributions of extinct clades and stem groups to total extinction rates cannot easily be estimated from extant-only datasets , and the proportion of entirely extinct clades is likely to increase systematically further back in time from the present . This should cause over-estimation of net diversification rates within inclusive and ancient clades such as Tetrapoda based on the study of living taxa alone . The discrepancy between Mesozoic diversification rates inferred from fossils and diversification rates inferred from living tetrapod phylogenies could also be explained if Cenozoic diversification rates ( which are the primary object of inference from living tetrapod phylogenies ) substantially exceeded those of the Mesozoic . Another explanation is plausible if tetrapod subclades show waxing/waning dynamics , as documented in invertebrate genera and mammalian families [23 , 73 , 74] . If the dynamics of subclades were asynchronous , whether this were due to diversity dependent interactions [25 , 75 , 76] , variable environmental tolerances [77] , or stochasticity , then the large diversity increases resulting from the waxing phases leading to speciose modern groups could be balanced on long timescales by the waning dynamics of groups that are extinct or depauperate today . This must have occurred in Cenozoic mammals , which show static and diversity-dependent diversification on large scales [22] , which apparently results from a zero-sum game among smaller clades that individually exhibit waxing/waning dynamics [23 , 25] . Near-stasis in Mesozoic tetrapod diversification could be explained by any of three prominent hypotheses: ( 1 ) diversity-dependence of diversification rates , or “equilibrial” models , under which speciation and extinction rates become balanced at equilibrial diversity [15 , 26 , 27]; ( 2 ) the possibility of relatively stable long-term environments during the Mesozoic , which could lead to nearly static diversity under Vrba’s Turnover Pulse hypothesis [77]; or ( 3 ) a “damped exponential” model , in which unconstrained diversification is held in check by frequent , stochastic downwards perturbations [2 , 37 , 50] . Determining which of these alternatives , if any , provides a good explanation of the pattern requires further work , and we discuss each of them below .
Mesozoic–Ypresian tetrapods were downloaded from the Paleobiology Database ( http://paleobiodb . org ) , accessed via Fossilworks ( http://fossilworks . org ) on 22 January 2015 . These data represent an estimated 6 , 520 hours of work , of which 88% was done , or authorised by , the first five authors of the present manuscript . The major contributors , in order of effort , are M . T . Carrano , J . Alroy , R . J . Butler , P . D . Mannion , R . B . J . Benson , A . M . Rees , W . Kiessling , M . E . Clapham , F . T . Fursich , M . Aberhan , and M . D . Uhen [47] . Our work included extensive checking of the completeness of the data , which we believe is essentially an accurate documentation of the literature on Mesozoic–Ypresian tetrapod taxonomy and occurrences . The data were processed by removing ootaxa , ichnotaxa , and marine taxa using a list of the names of genera , families , and higher taxa . Together , the remaining data comprise 27 , 260 global tetrapod occurrences of 4 , 898 species in 3 , 323 genera , spanning almost 205 million years . All data are available at DRYAD ( http://doi . org/10 . 5061/dryad . 9fr76 ) [48] . Equal-coverage , or shareholder quorum subsampling ( SQS ) , tracks coverage of each subsampling pool represented by the species that have been drawn , thereby subsampling more intensively when underlying richness is higher [18 , 46] . The substantial advantage of SQS over other subsampling methods , such as classical rarefaction , is that it is robust to the tendency of those methods to flatten out diversity curves . A total of 10 , 000 subsampling trials were run in each iteration . “Coverage” is the sum of the proportional frequencies of the species sampled ( i . e . , if one species constitutes 23% of occurrences within an interval , then it contributes a proportional frequency of 0 . 23 when it is sampled during subsampling draws ) , and coverage of observed data is modified to estimate the coverage of the real taxon distribution for each sample pool . This is achieved by multiplying coverage of the observed data by Good’s u: the proportion of occurrences representing non-singleton taxa [18 , 46] , which is a measure of sample completeness . Each interval can therefore only be subsampled to a maximum quorum level equal to Good’s u for that interval , meaning fewer intervals/regions can be subsampled at higher quorum levels . For example , Fig 5G shows that the Carnian of North American has a relatively low proportion of non-singleton occurrences ( <0 . 5 ) and could only be subsampled to a maximum quorum of 0 . 4 , whereas the Maastrichtian of North America has been more completely sampled , and could be subsampled to a maximum quorum of 0 . 7 . Results based on a minimum quorum level of 0 . 4 are shown in Fig 5A–5D , and other empirical analyses suggest that this level is sufficient to recover relative patterns of standing diversity [46] . Indeed , similar results were obtained using different quorum levels ( Fig 5E–5G ) and for genera ( S1 Fig ) . Singleton taxa were defined based on occurrences within collections rather than publications ( [92] contra [18 , 46] ) . Entire fossil collections , containing lists of species occurrences , were drawn . Previously , exclusion of either the most common taxon or the most diverse collection from each subsampling pool was proposed as a solution to Lagerstätten effects [18 , 46] . Instead of doing this , we excluded the three groups with Lagerstätten-dominated records: birds , bats , and pterosaurs [93–95] . The fossil records of these groups are dominated by a different taphonomic regime than those of other tetrapod groups , and do not provide sufficient information for meaningful subsampled diversity estimation . Furthermore , the well-known Early Cretaceous Jehol Biota Lagerstätten of China [96] has thus far yielded a high reported proportion of singleton occurrences , and therefore did not achieve a sufficient quorum to be included in our analyses . Because poorly studied spatiotemporal regions could appear well sampled for stochastic reasons , returning spuriously low subsampled diversity estimates , time bins with fewer than 20 publications were excluded from our analyses . Publications , rather than occurrences , were used as a criterion to ensure that a minimum level of taxonomic scrutiny had been applied to the fossils within each spatiotemporal region . Whenever a collection corresponding to a new publication was drawn , subsequent collections were drawn from that publication only until all or three collections from that publication had been sampled [92] . We used general linear models to estimate the coefficients of relationships between richness measures ( face-value taxon counts and subsampled diversity estimates; both globally and regionally ) and candidate controlling variables such as time , geographic spread and regional paleolatitudinal centroids . Models were fit using the glm ( ) function of the stats package of R version 3 . 1 . 0 [97] for Gaussian error models and the glm . nb ( ) function of the MASS package version 7 . 3 . 33 [98] . A negative binomial distribution was used for comparisons of face-value count data , which are over-dispersed , integer-valued , and bounded at zero . Gaussian distributions were used for subsampled diversity estimates , which are continuous-valued and do not approach zero . Because diversity is generated by the process of lineage diversification , with higher absolute total rates when more lineages are present , ln ( ) link functions were used in all analyses . The appropriateness of these distributions was confirmed by inspection of diagnostic plots using the glm . diag . plots ( ) function of the boot package version 1 . 3–16 [99] , and by comparing their AICc values to those of other distributions . The explanatory power of each model was estimated in comparison to an intercept-only null model using the generalised coefficient of determination [100] . Equation A25 of reference [61] is m’t = ( λe ( λ-μ ) t—μ ) / ( λ—μ ) , where λ is the speciation rate per lineage million years , μ is the extinction rate per lineage million years , t is the time in million years from some arbitrary starting point , and m’t is the expected paraclade diversity at time t , conditioned on the fact that the paraclade survives at least until time t . We calculated minimum spanning tree lengths for each of our time bins for comparison with counted and subsampled genus and species diversities , as shown in Fig 3 . A custom script in R version 3 . 0 . 2 [97] implemented the following protocol: ( 1 ) A matrix of great circle distances between pairs of fossil locality paleocoordinates was constructed for each interval . ( 2 ) This was transformed to a 3xN table containing distances between pairs of localities in rows as “locality 1 , ” “locality 2 , ” and “distance . ” ( 3 ) The columns of the table were ordered from shortest to longest distance . ( 4 ) The shortest distance was added to a running total , and the locality name of locality 2 was replaced with the name of locality 1 in all instances in the table . ( 5 ) Step 4 was repeated until all locality names were equal . Log10-transformed richness measures were compared to measures of geographic spread using correlation tests and not general linear models because our objective was to determine the significance and strengths of relationships among variables , not to determine coefficients [100] .
|
Vertebrates invaded the land more than 360 million years ago . Since then , they diversified to more than 30 , 000 tetrapod species today , including birds , mammals , squamates , and amphibians . The fossil record provides our best window onto diversification across such long spans of time , but is unevenly sampled . Previous studies counted observed families of fossil tetrapods and supported an expansionist model , entailing large and unbounded diversity increases through time . We applied methods that correct for differences in sampling through time and space to a comprehensive species-level database of Mesozoic to early Cenozoic fossil tetrapods . We find strong evidence that tetrapod diversity increased during the Mesozoic , but that the long-term net rate of diversification was low; species richness only doubled or tripled over 190 million years . This is enigmatic because today’s high biodiversity could not have been realised at such a slow rate . Diversification rates must have been much higher during other intervals , or rapid diversification might have been concentrated during brief episodes such as the earliest Cenozoic . Patterns of diversification on geological timescales and their relationships to hypothesised drivers such as ecological opportunity and environmental volatility must receive renewed scrutiny if we are to understand how land vertebrates and other animals attained the high biodiversity seen today .
|
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2016
|
Near-Stasis in the Long-Term Diversification of Mesozoic Tetrapods
|
Adjunctive vitamin D treatment for pulmonary tuberculosis enhances resolution of inflammation but has modest effects on bacterial clearance . Sodium 4-phenylbutyrate ( PBA ) is in clinical use for a range of conditions and has been shown to synergise with vitamin D metabolites to upregulate cathelicidin antimicrobial peptide ( CAMP ) expression . We investigated whether clinically attainable plasma concentrations of PBA ( 0 . 4-4mM ) directly affect Mycobacterium tuberculosis ( Mtb ) growth and human macrophage and PBMC response to infection . We also tested the ability of PBA to enhance the immunomodulatory actions of the vitamin D metabolite 25 ( OH ) D3 during infection and synergistically inhibit intracellular Mtb growth . PBA inhibited Mtb growth in broth with an MIC99 of 1mM , which was reduced to 0 . 25mM by lowering pH . During human macrophage infection , PBA treatment restricted Mtb uptake , phagocytic receptor expression and intracellular growth in a dose-dependent manner . PBA independently regulated CCL chemokine secretion and induced expression of the antimicrobial LTF ( lactoferrin ) , the anti-inflammatory PROC ( protein C ) and multiple genes within the NLRP3 inflammasome pathway . PBA co-treatment with 25 ( OH ) D3 synergistically modulated expression of numerous vitamin D-response genes , including CAMP , CYP24A1 , CXCL10 and IL-37 . This synergistic effect was dependent on MAPK signalling , while the effect of PBA on LTF , PROC and NLRP3 was MAPK-independent . During PBA and 25 ( OH ) D3 co-treatment of human macrophages , in the absence of exogenous proteinase 3 ( PR3 ) to activate cathelicidin , Mtb growth restriction was dominated by the effect of PBA , while the addition of PR3 enhanced growth restriction by 25 ( OH ) D3 and PBA co-treatment . This suggests that PBA augments vitamin D–mediated cathelicidin-dependent Mtb growth restriction by human macrophages and independently induces antimicrobial and anti-inflammatory action . Therefore through both host-directed and bacterial-directed mechanisms PBA and vitamin D may prove an effective combinatorial adjunct therapy for tuberculosis to both resolve immunopathology and enhance bacterial clearance .
Tuberculosis ( TB ) is the world’s leading bacterial cause of death , with 9 million new cases and 1 . 5 million deaths in 2013 [1] . Despite the availability of antimicrobials , effective treatment regimes require long-term administration of multiple drugs , which can lead to toxicity , problems with adherence and the development of drug resistance . Current drug regimes , while reducing bacterial load , do not directly resolve the immunopathological inflammatory responses associated with morbidity [2 , 3] . Immunomodulatory agents that augment antimicrobial activity and accelerate resolution of pulmonary inflammation could be used as adjuncts to antimicrobial therapy to improve treatment outcomes [4] . The active metabolite of vitamin D3 , 1α25-dihydroxy-vitamin D3 [1α , 25 ( OH ) 2D3] inhibits intracellular growth of Mycobacterium tuberculosis ( Mtb ) in peripheral blood mononuclear cells ( PBMC ) and monocytes ( MN ) and suppresses pro-inflammatory cytokine secretion in vitro [5 , 6] . LL-37 ( cathelicidin ) has been identified as a mediator of vitamin D-dependent antimicrobial activity [7] . Pro-LL-37 ( hCAP-18 ) is transcribed from the cathelicidin antimicrobial peptide ( CAMP; OMIM 600474 ) gene that contains 3 vitamin D response elements ( VDRE ) in its promoter and requires enzymatic digestion by human neutrophil proteinase 3 ( PR3; OMIM 177020 ) to produce mature LL-37 ( OMIM 600474 ) , an antimicrobial that directly inhibits Mtb growth [5 , 8] . The presence of VDRE in the CAMP promoter is an evolutionary adaptation , having only been identified in chimpanzee and human DNA sequences [9]; as such the antimicrobial action of vitamin D mediated by CAMP is limited to higher-order primates . LL-37 also regulates autophagy-mediated killing via induction of autophagy related proteins Beclin-1 ( OMIM 604378 ) and Atg5 ( OMIM 604261 ) [10] and has chemotactic activity for polymorphonuclear leukocytes [11] . 1α , 25 ( OH ) 2D3 is synthesised from the major circulating form of vitamin D3 , 25-hydroxy-vitamin D3 [25 ( OH ) D3] , by the vitamin D 1α-hydroxylase enzyme ( CYP27B1; OMIM 609506 ) the expression of which is upregulated in leukocytes following ligation of toll-like receptors with Mtb ligands [12] . Vitamin D deficiency ( defined as a circulating concentration <50nM 25 ( OH ) D ) is associated with susceptibility to tuberculosis [13 , 14] . We have shown that administration of adjunctive vitamin D accelerates resolution of inflammatory responses during tuberculosis treatment [15] , and that this is associated with a trend towards accelerated sputum culture conversion [16] . The limited in vivo effect of vitamin D supplementation on Mtb growth despite significant inhibition in vitro may be due to the fact that the majority of in vitro experiments have used supra-physiological doses of 1α , 25 ( OH ) 2D3 ( 100nM vs . the circulating concentration of 100pM ) . Physiological doses ( 100nM ) of 25 ( OH ) D3 have only shown a significant effect on Mtb growth in vitro during co-stimulation with other molecules , such as IFNγ ( OMIM 147570 ) or IL-15 ( OMIM 600554 ) , to prolong transcription of CYP27B1 [6 , 17 , 18] . Sodium 4-phenylbutyrate ( PBA ) is an aromatic short chain fatty acid which has been in clinical use for more that 30 years . As a pro-drug for sodium phenylacetate it is used to control nitrogen disposal in urea disorders , as a chemical chaperone it is used to alleviate endoplasmic reticulum stress in type-2 diabetes , and as a histone deacetylase inhibitor ( HDACi ) it is used to treat various leukaemias and cancers via modulating chromatin structure and inducing cellular differentiation and apoptosis [19–24] . PBA has also been shown to have direct anti-fungal activity against Candida albicans and Cryptococcus neoformans [25] . Moreover , PBA synergises with the 1α , 25 ( OH ) 2D3 induction of CAMP expression , in a variety of cell lines [26 , 27] . This synergistic induction of CAMP expression was also recently confirmed ex vivo in PBMC from healthy control participants in a dose-finding study who received oral PBA and vitamin D3 for 8 days [28] , also confirming the systemic effect of this treatment on immune cell function . On the basis of this synergistic CAMP induction , there is an on-going phase 2 trial of PBA and vitamin D3 adjunct therapy for TB [29] . However , the direct effect of PBA on Mtb and human immune cells is unknown . Data are also lacking on the question of whether this combined co-treatment has additional synergistic effects on macrophage function during Mtb-infection and if it can synergistically inhibit intracellular Mtb growth via CAMP induction . Thus , we investigated each of these questions , determining the effects of clinically attainable plasma concentrations of PBA ( 0 . 4-4mM ) , alone and in combination with the physiologically relevant vitamin D metabolite 25 ( OH ) D3 , at optimal concentration for immune function .
To test whether PBA has direct anti-mycobacterial effects , Mtb was grown on both solid agar medium and in broth in the presence of a 10-fold serial dilution ( 0 . 4-4mM ) of PBA . Plating a low density culture on solid medium containing 4mM PBA completely restricted growth , while the presence of 0 . 4mM PBA allowed 70% growth and 0 . 04mM allowed 84% growth , compared to vehicle control ( 0mM , 0 . 4% water ) ( Fig 1A ) . When PBA was added to exponentially growing rolling broth cultures , 4mM PBA significantly inhibited an increase in OD600 from day 2 onwards ( P<0 . 0001 ) and this was maintained until day 14 at which stage cultures were pelleted , washed and resuspended in PBA-free medium ( Fig 1B ) . Growth inhibition by 4mM PBA was maintained in PBA-free medium for a further 8 days and growth never reached control levels ( P<0 . 0001 ) ( Fig 1B ) . Reductions in OD600 were confirmed by colony forming unit ( CFU ) analysis , with 4mM PBA reducing CFU to 6% of control after 6 days of treatment ( Fig 1C ) . The retardation of growth following removal of 4mM PBA from the medium suggests that PBA is bacteriostatic rather than bactericidal and it may modify bacterial replication . Phenotypically when exponential phase cultures were grown in the presence of 4mM PBA , even in the presence of a detergent , the cultures formed a cord like conglomerate that was easily dispersed by swirling . This suggested that there may be a cell wall modification by PBA , resulting in the bacteria having an adherent phenotype . Confocal microscopy of GFP-expressing H37Rv confirmed that PBA-treated bacilli are often found bound along the entire shaft of neighbouring bacilli , forming lined-up clusters ( S1A Fig ) , unlike untreated cultures which had single bacilli or overlapping bacilli when in clusters ( S1B Fig ) . Non GFP-expressing bacteria were also found within these PBA clusters of lined-up bacilli indicating that they were not metabolically active ( S1A Fig ) . As a weak acid , PBA activity may be modulated by the pH of the growth medium . We confirmed that there was no significant effect of PBA on pH of growth medium from infected and uninfected cultures ( S2 Fig ) . We then tested a 2-fold serial pH reduction over a range of PBA concentrations ( 0 . 25–4mM ) using the Alamar Blue reduction checkerboard assay . The minimum inhibitory concentration of PBA which stopped 99% of Mtb growth ( MIC99 ) without HCl was 1mM and this was reduced to 0 . 5mM in the presence of 2 . 5mM HCl and to 0 . 25mM in the presence of 5mM HCl , with 10mM HCl completely inhibiting Mtb growth irrespective of PBA ( Fig 1D ) . At these HCl concentrations growth medium pH reduced from 6 . 61 to 6 . 37 , 6 . 06 and 4 . 82 , respectively ( Fig 1D ) . Mtb containing phagosomes have a pH range of 6 . 5 to 6 . 2 and become acidified to pH 5 . 8 to 5 . 6 when infected with attenuated Mtb , and can reach a pH of 5 . 0 to 4 . 5 upon phagolysosome fusion [30 , 31] . Thus , PBA may have enhanced activity against Mtb as phagosomes become increasingly acidified . Once we had determined PBA had a direct effect on Mtb , we next investigated the effect of PBA on the human cellular response to Mtb . Initially , a 2-fold serial dilution of PBA ( 0 . 5-8mM ) was added to PBMC at the time of Mtb infection . Uptake was significantly inhibited 4 hrs post-infection by 8mM PBA ( to 54% of control , P = 0 . 0009 ) and intracellular growth was inhibited after 96 hrs in PBMC , in a dose-dependent manner: there with a trend for decreased growth with 4mM PBA ( 71% of control ) and a significant inhibition of growth with 8mM PBA ( 45% of control , P = 0 . 01 ) ( Fig 2A ) . The inhibition of Mtb uptake when PBA was added at the time of infection suggests PBA may directly affect phagocytic function . To confirm this , monocyte-derived macrophages ( MDM ) were pre-treated for 48 hrs with PBA , washed and then infected with Mtb in PBA-free medium . Pre-treatment significantly inhibited MDM uptake of Mtb to 64% of control ( P = 0 . 02 , Fig 2B ) . To determine if this was associated with inhibited expression of phagocytic receptors , macrophages were analysed for surface expression of CD35 ( CR1 , OMIM 120620 ) , CD11b ( CR3 , OMIM 120980 ) , CD11c ( CR4 , OMIM 151510 ) , and CD16 ( FcγR , OMIM 146740 ) by flow cytometry . PBA pre-treatment did not affect the number of MDM expressing each receptor but it did significantly inhibit median fluorescent index of CD11b and CD11c ( Fig 2C ) . The effect of PBA on Mtb intracellular growth in MDM was subsequently assessed by treating MDM with PBA 4 hrs post-infection , to ensure comparable infection of cells pre-treatment . There was a trend for decreased intracellular growth with 4mM PBA ( 74% ) after 72 hrs of infection , while 2-4mM PBA restricted growth after 7 days ( to 55% and 37% of control , respectively , P<0 . 012 , Fig 2D ) . To elucidate whether Mtb growth inhibition was mediated via a cellular-dependent mechanism or if it was due to the direct effect of PBA on Mtb growth , Mtb was cultured for 4 days in the presence/absence of 4mM PBA , extensively washed to remove PBA and then MDM were infected for 4 hrs and cultured in medium without PBA for 7 days . CFU analysis indicated that there was no significant effect of PBA pre-treatment of Mtb on infection dose or 4 hrs uptake ( S3 Fig ) and only a small non-significant reduction in Mtb growth after 7 days ( Fig 2E ) . Conversely when MDM from the same donors were infected with untreated Mtb and MDM were treated post-infection with 4mM PBA Mtb growth was significantly inhibited ( P = 0 . 018 , Fig 2E ) . This suggests that PBA also has a direct effect on cell-mediated growth restriction and that the ability of PBA to restrict the growth of intra-phagosomal bacteria is more likely mediated via a host-directed mechanism than via the direct action of PBA . To investigate how PBA regulates macrophage response to Mtb infection , MDM gene expression was analysed using real-time reverse transcription-polymerase chain reaction ( RT-PCR ) arrays designed for a panel of 28 immune response and vitamin D-associated genes . Initially , to gain a broad sense of the transcriptional regulation by PBA , expression results from infected and uninfected MDM at 24 and 72 hrs post-infection ( 20 and 68 hrs post-treatment ) were combined for analysis , applying a t-test for general linear models ( GLM ) with statistical adjustment for donor variation and time of treatment . Principal component analysis ( PCA ) conducted using the 12 genes identified to be significantly regulated by PBA , irrespective of time and infection status , showed that PBA had the greatest effect on gene expression in infected cells ( Fig 3A ) . We then applied a similar GLM analysis to data from only infected cells , and found PBA had a greater effect on gene expression after 72 hrs ( Fig 3B ) . Gene expression data were then analysed separating samples from each time point and infection status , to identify all genes regulated by PBA under the different conditions ( Table 1 ) . PBA only affected uninfected cells after 72 hrs: seven genes were upregulated , six of which were also upregulated during Mtb infection ( CAMP , CXCL8 ( OMIM 146930 ) , IL1B ( OMIM 147720 ) , IL12A ( OMIM 161560 ) , PROC ( OMIM 176860 ) , VDR ( OMIM 601769 ) ) and one gene was inhibited ( CYP27B1 ) . During Mtb infection , 18 genes were regulated by PBA: 10 genes were induced at 24 hrs and 12 genes at 72hrs , with eight genes induced at both time points ( CASP1 ( OMIM 147678 ) , CXCL8 , IL1B , IL36G ( OMIM 605542 ) , IL37 ( OMIM 605510 ) , LTF ( OMIM 150210 ) , PROC , TREM1 ( OMIM 605085 ) ) , while four genes were inhibited and only at 24 hrs ( ARG1 ( OMIM 608313 ) , CXCL7 ( OMIM 121010 ) , CYP27B1 , IL10 ( OMIM 124092 ) ) ( Table 1 ) . The effect of PBA on gene expression was also dose-dependent ( S4 Fig ) . However , while PBA had a greater effect on the number of genes regulated when treatment occurred during infection , the fold change in expression regulated by PBA was actually higher in uninfected cells ( Table 1 ) , suggesting that Mtb-infection interferes with the effect of PBA treatment . The 2 genes most highly induced by PBA during infection , were LTF and PROC . LTF encodes lactoferrin , an iron chelator with known anti-Mtb effects [32] , which PBA induced approximately 6-fold ( P<0 . 022 ) , the effect of PBA being infection-dependent ( Fig 3C ) . PROC , which encodes anticoagulant protein C , an anti-inflammatory molecule which supresses cytokine production and secretion [33] had the greatest induction ( 15–23 fold , P<0 . 01 ) , irrespective of infection ( Fig 3C and Table 1 ) . CAMP expression was also induced , but only 3-4-fold ( P≤0 . 019 ) 72 hrs post-treatment ( Table 1 ) . Of the 8 genes regulated by PBA during Mtb infection at both time points , four were involved in the inflammasome and IL-1 pathway; IL1B , IL36G ( IL1F9 ) , IL37 ( IL1F7b ) and CASP1 ( required for IL-1β post-translational activation ) were induced . NCF4 ( OMIM 601488 ) which transcribes a subunit ( p40-phox ) of NAPDH oxidase which activates the NLRP3 inflammasome , and NLRP3 ( OMIM 606416 ) were both induced by PBA during infection , at 24 and 72 hrs , respectively ( P≤0 . 026 ) . Six additional Mtb-associated genes encoding cytokines and chemokines were also regulated by PBA . Of these , CXCL8 ( IL-8 ) and IL12A ( IL-12p35 ) had the greatest induction of expression ( 3-6-fold , P = 0 . 009 ) ( Fig 3C and Table 1 ) . The effect of PBA on macrophage secretion of a panel of 30 chemokines , cytokines and growth factors in supernatants 24 and 72 hrs post-infection was then analysed , using a t-test for GLM with adjustment for donor variation . Similar to the effects on transcript abundance , PBA had a greater effect on protein secretion 72 hrs post-infection , modulating secretion of five analytes , compared to only one at 24 hrs ( Fig 3D and Table 1 ) . A dose-response was also confirmed for these proteins ( S5 Fig ) . Four of the five analytes were C-C ligand ( CCL ) chemokines: CCL2 ( OMIM 158105 ) secretion was inhibited by PBA at 24 and 72 hrs ( P≤0 . 027 ) , while CCL3 ( OMIM 182283 ) , CCL4 ( OMIM 182284 ) and CCL5 ( OMIM 187011 ) had increased secretion after 72 hrs ( Fig 3D , P≤0 . 036 ) . The concentration of soluble IL-2R ( OMIM 146710 ) was also significantly higher in PBA treated cultures at 72 hrs ( P = 0 . 0018 ) . Since PBA had a significant effect on macrophage gene expression and protein secretion during Mtb infection , we next investigated the hypothesis that PBA synergises with 25 ( OH ) D3 regulated gene expression during Mtb infection . Dose response experiments showed 100nM 25 ( OH ) D3 was the optimal physiological concentration for transcriptional activation ( serum levels ≥ 220nM are considered toxic [34] ) ( S6 Fig ) . MDM were therefore treated 4 hrs post-infection with 4mM PBA , 100nM 25 ( OH ) D3 or in combination and RNA was isolated 24 and 72 hrs post-infection for analysis by RT-PCR arrays , as before . To identify the synergistic effects of PBA and 25 ( OH ) D3 during co-treatment , a t-test for GLM , with adjustment for donor variation and time of treatment , was applied to three comparisons: 25 ( OH ) D3 vs . control , co-treatment vs . control , and co-treatment vs . PBA ( to determine the effect of 25 ( OH ) D3 in the presence of PBA ) , including samples from both time points . A total of 20 genes were identified to be significantly regulated in at least one of the three treatment conditions ( Table 2 ) . Hierarchical clustering applied to the list of 20 differentially regulated genes showed that 25 ( OH ) D3- and co-treated samples clustered together , indicating that 25 ( OH ) D3 had the greatest contribution to differential gene expression during co-treatment . PBA-treated samples formed their own cluster and all treatments clustered according to time of sampling , with 72 hrs samples showing a greater effect of each treatment ( Fig 4A ) . Of the 13 genes regulated by 25 ( OH ) D3 , six were also regulated by PBA monotherapy ( although the effect of 25 ( OH ) D3 on their expression was greater: four were induced [CAMP , IL36 , IL37 and TREM1] and two were inhibited [CXCL7 , CYP27B1] ) , two were regulated in an opposite direction to PBA ( CASP1 and VDR ) and four ( CXCL9 ( OMIM 601704 ) , CXCL10 ( OMIM 147310 ) , NOD2 ( OMIM 605956 ) , TNF ( OMIM 191160 ) ) were not significantly regulated by PBA monotherapy ( Tables 1 and 2 ) . Adding 25 ( OH ) D3 in the presence of PBA enhanced the response of eight 25 ( OH ) D3-regulated genes: five ( CAMP , CYP24A1 ( OMIM 126065 ) , IL36 , IL37 , TREM1 ) were synergistically induced , with the combined effect of co-treatment greater than an additive combination of individual effects , and three chemokines CXCL7 , and IFNγ-inducible CXCL9 and CXCL10 were all further inhibited by combined treatment , despite the fact that PBA only showed a trend towards inhibition of CXCL9 and CXCL10 during monotherapy ( Fig 4B and Table 2 ) . IFNG was also inhibited by co-treatment , but there was no independent effect of PBA or 25 ( OH ) D3 on IFNG expression ( Tables 1 and 2 ) . We also analysed the effect of co-treatment on uninfected cells and near identical patterns of synergism were observed for induced genes , except for the CXC chemokines which were only inhibited during infection ( S7 Fig ) . Of the two genes which were differentially regulated by PBA and 25 ( OH ) D3 monotherapies , CASP1 showed an intermediate expression between the two therapies during co-treatment with the effect dominated by 25 ( OH ) D3 , such that expression of CASP1 was inhibited during co-treatment . Furthermore , 25 ( OH ) D3 attenuated PROC induction by PBA , despite not having a significant effect of PROC during monotherapy ( Fig 4B and Table 2 ) . To determine whether the synergistic effect of PBA on 25 ( OH ) D3 gene expression was potentially associated with increased 1 , 25 ( OH ) D3 production or activity , the expression of CYP27B1 and VDR was also assessed , respectively . The inhibition of CYP27B1 and VDR expression by 25 ( OH ) D3 was very moderately attenuated by co-treatment with PBA ( Fig 4B and Table 2 ) , suggesting PBA may enhance the response to 25 ( OH ) D3 through increased VDR signalling , particularly as PBA monotherapy induces VDR . We next investigated whether co-treatment with PBA and 25 ( OH ) D3 had a synergistic effect on cytokine/chemokine secretion in supernatants from the same Mtb-infected MDM by applying the same GLM analysis technique to secretion data as for expression data . Only three analytes of the 30 investigated were identified to be significantly regulated by 25 ( OH ) D3 and only three were regulated during co-treatment ( Table 2 ) . CCL2 secretion , which was significantly inhibited by PBA ( Fig 3D ) , was also inhibited by 25 ( OH ) D3 and synergistically inhibited during co-treatment ( Fig 4C ) . Reflecting gene expression results , secretion of CXCL9 and CXCL10 was inhibited during co-treatment despite only CXCL10 being inhibited by 25 ( OH ) D3 monotherapy , while neither was inhibited by PBA monotherapy . Conversely , an opposing effect of co-treatment was observed for CCL5 and IL-1β , 25 ( OH ) D3 attenuated CCL5 induction by PBA , while PBA attenuated 25 ( OH ) D3 induction of IL-1β ( Fig 4C , Table 2 ) . In an epithelial cell line , mitogen-activated protein kinase ( MAPK ) signalling via JNK ( OMIM 601158 ) and ERK1/2 ( OMIM 601795/176948 ) pathways has been shown to regulate PBA-induced CAMP expression [26] . We therefore investigated whether PBA regulated synergistic 25 ( OH ) D3 gene expression in macrophages via the same MAPK pathways and whether it also utilised these pathways to regulate non-vitamin D-associated genes . MAPK pathway inhibitors for p38 kinase ( SB202190 ) , JNK ( SP600125 ) and ERK1/2 ( U0126 ) were added to MDM 4 hrs post-infection , for 1 hr prior to addition of PBA ± 25 ( OH ) D3 and gene expression analysed 24 hrs post-treatment ( Fig 5 ) . Synergistic induction of CAMP and CYP24A1 was completely inhibited by p38 kinase inhibition , and only partially by ERK1/2 and JNK inhibition . P38 kinase inhibition also had the greatest effect on attenuating IL37 induction by 25 ( OH ) D3 , while ERK1/2 inhibition had the greatest effect on attenuating PBA and co-treatment induction of IL37 . However , for TREM1 , while p38 inhibition prevented induction by 25 ( OH ) D3 and ERK1/2 inhibition somewhat attenuated PBA , ERK1/2 inhibition also attenuated expression in control cells and none of the MAPK inhibitors prevented synergistic induction , compared to control . Analysis of the two genes differentially regulated by PBA and 25 ( OH ) D3 , CASP1 and PROC , indicated very different patterns for the two treatments: CASP1 induction by PBA was attenuated by all three pathways although the most via p38 , while p38 also attenuated CASP1 inhibition by 25 ( OH ) D3 . Conversely , PROC expression was only moderately suppressed by ERK1/2 inhibition , during PBA and co-treatment , again not to untreated levels , and JNK inhibition actually led to increased expression by PBA and co-treatment ( Fig 5 ) . Finally , when we investigated genes independently regulated by PBA , we found that even though IL1B , LTF and NLRP3 expression can be inhibited via at least one of the signalling pathways , the induction by PBA of IL1B and LTF occurred even when these pathways were suppressed and only NLRP3 induction was partially attenuated by inhibition of ERK signalling ( Fig 5 ) . These results indicate that the vitamin D-independent mechanism of PBA-regulated gene expression is predominantly mediated via pathways other than MAPK signalling . Finally , as we found a significant synergistic effect of PBA on 25 ( OH ) D3 induction on CAMP expression ( Fig 5B ) , we investigated whether co-treatment with PBA enhanced 25 ( OH ) D3-dependent restriction of Mtb growth via cathelicidin production over 7 days of culture , treating macrophages 4 hrs post-infection with either mono- or dual-therapy . Consistent with dose response experiments ( Fig 2D ) , 4mM PBA restricted growth after 7 days ( 2 . 8-fold growth , compared to 7 . 9-fold growth in untreated cells , P = 0 . 0012 ) , treatment with 25 ( OH ) D3 alone resulted in 6 . 9-fold growth ( although not significantly different to control ) , while co-treatment resulted in 2 . 4-fold growth ( P = 0 . 0017 ) , and there was no synergistic effect compared to PBA monotherapy ( Fig 6A ) . We also confirmed by checkerboard Alamar Blue assay that there was no direct effect of 25 ( OH ) D3 on in vitro Mtb growth , in the absence or presence of PBA ( S8 Fig ) . The fact that there was no significant effect of 25 ( OH ) D3 monotherapy or synergistic reduction in Mtb growth , despite the 100-fold synergistic induction of CAMP expression , suggested that the antimicrobial action of cathelicidin may not have been activated in our in vitro model . Activation requires proteolytic processing of the translated pro-LL-37 into active LL-37 by proteinase 3 ( PR3 ) [8] . We therefore investigated expression of PRTN3 ( translated into PR3 ) in MDM compared to whole blood ( WB ) , PBMC , MN and infected MDM treated ± 25 ( OH ) D3 and PBA . We found MDM down-regulated PRTN3 6-15-fold ( P<0 . 038 ) compared to PBMC , WB and MN and that PRTN3 was further down-regulated over 72hr of infection ( Fig 6B ) . We therefore added PR3 to infected MDM during treatment with 25 ( OH ) D3 ± PBA to determine whether LL-37 activation was required for 25 ( OH ) D3-mediated Mtb growth inhibition . PR3 addition significantly inhibited Mtb growth in 25 ( OH ) D3-treated MDM to the same level seen with PBA co-treatment , while addition of PR3 during co- treatment further reduced Mtb growth ( Fig 6C ) . These findings suggest that 25 ( OH ) D3 and PBA restrict Mtb growth in MDM via a common cathelicidin-mediated pathway and that PBA also inhibits growth independantly of cathelidicin .
PBA is currently used to treat a range of clinical disorders , due to its pleiotropic effects on cellular differentiation , proliferation and as a prodrug for phenyl acetate . Here we show that clinically attainable concentrations of PBA 1 ) restrict intracellular and in vitro growth of Mycobacterium tuberculosis , 2 ) inhibit Mtb phagocytosis by PBMC and MDM , 3 ) induce antimicrobial and inflammatory pathway genes , including LTF , PROC , and the inflammasome cascade by MDM and 4 ) augment 25 ( OH ) D3-mediated anti-mycobacterial and anti-inflammatory effects . Our findings suggest that combined PBA and vitamin D may prove an enhanced adjunct therapy to intensive-phase anti-TB treatment; mediated by both broad host-directed and bacterial-directed actions that have the potential to enhance bacterial control as well as resolve lung pathology through broad-ranging anti-inflammatory effects . PBA has previously been suggested as an antimicrobial agent for the treatment of respiratory infections , due to its ability to reverse the inhibition of expression of the rabbit CAMP homologue , CAP-18 , in lung and trachea epithelium during Shigella infection and induce proCAP-18 processing [35 , 36] . For the first time we show that PBA not only induces CAMP expression in human macrophages during Mtb infection , independently and synergistically with physiological concentrations of 25 ( OH ) D3 , but it also synergistically enhanced expression of the most highly induced vitamin D-regulated gene CYP24A1 and three genes previously not known to be regulated by vitamin D: IL36G , IL37 and TREM1 ( Fig 4 ) . All three have immunomodulatory properties and therefore may be key regulators of yet unrecognised vitamin D-mediated immune modulation . Both members of the IL-1 superfamily , IL-36G and IL-37 have opposing effects , being pro- and anti-inflammatory , respectively [37 , 38] . We found that IL36G was induced by Mtb infection while IL37 was not . Furthermore , both genes were synergistically induced by PBA and 25 ( OH ) D3 , irrespective of Mtb infection , indicating treatment alone was required for induction . Our observations are the first reported evidence that vitamin D signalling increases IL37 transcript levels . IL-37 has recently been identified as a natural innate inhibitor , suppressing macrophage TLR-induced cytokine and chemokine secretion of IL-1α ( OMIM 147760 ) , IL-1β , IL-6 ( OMIM 147620 ) , IL-12 , G-CSF ( OMIM 138970 ) , GM-CSF ( OMIM 138960 ) , and TNF by up to 98% , without regulating IL-10 or IL-1RA ( OMIM 147679 ) . Mature IL-37 has been shown to traffic to the nucleus after caspase-1 processing where it complexes with Smad-3 to facilitate TFGβ mediated cytokine suppression , as well as reducing phosphorylation of p38 MAPK , and STAT1-4 [38] . IL-37 may therefore be an additional mediator by which vitamin D elicits its broad anti-inflammatory effects TREM1 ( triggering receptor on myeloid cells-1 ) regulates inflammation by modulating TLR and NACHT-LRR ( NLR ) signals [39 , 40] . While TREM1 is constitutively expressed by myeloid cells , the level of induction influences the outcome of infection , such that in a mouse model of sepsis , moderate silencing of TREM1 improved survival , but near-complete silencing reduced neutrophil oxidative burst and increased mortality [41] . We found that TREM1 was moderately induced by Mtb infection and it was synergistically induced by 25 ( OH ) D3 and co-treatment with PBA , irrespective of infection ( Figs 4 and S4 ) . Through the use of MAPK inhibitors we determined that PBA-25 ( OH ) D3 synergistic regulation was mediated particularly via p38 or ERK1/2 signalling , but differed by gene , and that the synergistic regulation of TREM1 was MAPK-independent . We also found that PBA alone induced VDR expression , and a recent study in epithelial cells has shown VDR knockdown prevents CAMP induction by PBA-25 ( OH ) D3 co-treatment , indicating VDR is vital to this synergistic regulation [42] . Surprisingly , despite the greater than 50-fold induction of CAMP expression by 25 ( OH ) D3 which was synergistically induced by PBA co-treatment , we found a trend but no significant effect of 25 ( OH ) D3 on Mtb growth in our MDM model . We found MDM significantly down-regulate PRTN3 expression and that exogenous PR3 is required to activate 25 ( OH ) D3-dependant growth suppression in MDM . In vivo , activated neutrophils , recruited by the infected macrophages , could be a source of exogenous PR3 . However , lack of vitamin D-mediated antimicrobial activity in vivo may be due to unavailability of PR3 to macrophages and future studies should investigate the distribution of PR3 in human TB lung tissues . We also found that PR3 enhanced growth suppression during co-treatment , which was dominated by PBA in the absence of PR3 to activate cathelicidin ( Fig 6 ) . This suggests that the synergistic anti-Mtb effects of PBA and 25 ( OH ) D3 are likely partially mediated via cathelicidin , but PBA also had cathelicidin-independent effects on Mtb growth . We found that growth restriction by PBA was both MDM-dependent and-independent . Clinically attainable concentrations of PBA completely inhibited Mtb growth in broth , with a reduced MIC99 under acidic conditions , when Mtb was inoculated into PBA-containing medium at low density . Moreover , when high density exponentially growing cultures were treated with PBA , growth was considerably restricted and this growth inhibition was maintained even when PBA was removed , suggesting that PBA may interfere with a structural component required for replication , as the defect was long-lived . This theory is supported by the phenotypic change of PBA-treated bacilli observed by confocal microscopy . While it may be suggested that the intracellular restriction of Mtb growth was due entirely to the direct effect of PBA on Mtb , we made a number of observations suggesting otherwise: 1 ) PBA modulates macrophage function independently of infection within 24–48 hrs; 2 ) pre-treatment of Mtb with PBA did not significantly inhibit 4 hrs uptake or 7 day intracellular growth; 3 ) significant inhibition of intracellular Mtb growth was only observed after 7 days of treatment , whilst growth in broth was inhibited after 2 days . The last observation may also be explained by the Mtb target of PBA only being expressed intra-phagosomally after a few days of infection , with acidification of the phagosome potentially enhancing the direct action of PBA . However the fact remains that PBA modifies infected-macrophage function more rapidly than the effect on Mtb is observed , suggesting a cell-mediated effect on growth also exists . Consistent with PBA inhibiting Mtb growth via an MDM-dependant mechanism , we found PBA induced expression of LTF , encoding the antimicrobial agent and iron chelator lactoferrin . Lactoferrin has been shown to enhance IFNγ-mediated killing of Mtb in MDM and oral administration of lactoferrin to Mtb-infected mice decreased lung CFU and inflammation [32] . PBA also induced expression of PROC , encoding the anti-inflammatory and anti-coagulant Protein C . Activated protein C ( aPC ) has been shown in vitro to suppress expression of the p50 and p52 subunits of NF-κB , blocking NF-κB signalling [33] , increasing production of IL-10 and TGFβ [43] and in vivo , blocking neutrophil chemotaxis and adhesion to vascular endothelial cells , resulting in decreased lung infiltration of neutrophils in an LPS-induced lung injury model [44] . The recent interest in neutrophils as a primary mediator of pathology in active TB suggests PBA-induced regulation of PROC may enhance resolution of pathologic inflammation during TB treatment [45] . The anti-coagulation effect of PROC may also reduce disseminated intravascular coagulation ( DIC ) , which has a high mortality rate in TB patients [46] . Four genes in the inflammasome pathway which may augment the antimicrobial activity of macrophages were also significantly induced by PBA [47] . Surprisingly , despite CASP1 , IL1B , NCF4 and NLRP3 expression being induced by PBA , IL-1β secretion was not . However , this may be because IL-37 , which is known to inhibit IL-1β secretion [38] , was simultaneously induced by PBA . In support of this theory , co-treatment with PBA attenuated the secretion of IL-1β induced by 25 ( OH ) D3 , at the same time as synergistically inducing IL37 expression . Further research is required to clearly delineate these interactions . The role of MAPK signalling in the vitamin D-independent transcriptional effects of PBA was also investigated; although MAPK inhibition partially attenuated expression of LTF , PROC and inflammasome-associated genes , it did not prevent their PBA-mediated induction . Future work will investigate the role of PBA as a HDACi in regulating the expression of these genes . Our findings are limited by the fact that we have not yet confirmed the direct and cell-mediated antimicrobial mechanisms of PBA , although we have observations that indicate PBA may be acting on the cell wall . Through the development of PBA-resistant mutants , transposon mutagenesis libraries and macrophage knockdown of LTF , future research will more clearly delineate these mechanisms . Moreover , while we investigated the effect of PBA on Mtb uptake and growth in PBMC we did not characterise the transcriptional effect of PBA on leukocytes other than macrophages . Recent work has shown that PBA and 1α , 25 ( OH ) 2D3 co-treatment of MN during differentiation into dendritic cells ( DC ) promotes the development of a CD14+/CD1a- DC subset which produces enhanced levels of cathelicidin and reactive oxygen species ( ROS ) and which have enhanced anti-Staphylococcus aureus activity [48] . It is therefore likely that vitamin D and PBA have additional cell-mediated effects on the TB immune response , yet to be elucidated . Together , our results suggest that PBA treatment could independently restrict Mtb growth in vivo as well as enhance the antimicrobial and anti-inflammatory effects of vitamin D , such that combined PBA and vitamin D therapy during anti-TB treatment may be superior to vitamin D alone . Clinical trials also need to be conducted to evaluate the efficacy of adjunctive PBA in reducing time to culture conversion , independent of vitamin D . However , the combined effect of these two compounds together on anti-microbial and particularly on anti-inflammatory molecules , which are proposed to have a significant benefit on reducing morbidity in TB patients , suggest this combined adjunct therapy might be of greater benefit than either compound alone . The fact that the first Phase 2 trial to investigate the in vivo efficacy of this combined therapy was recently completed in Bangladesh , is therefore timely [29] . Our findings of broad and synergistic action of these two compounds will have particular relevance to the interpretation of results from this trial , which will also provide the first in vivo study of PBA as an adjunctive therapy for TB .
Human leukocytes were obtained from healthy volunteers ( n = 21 ) . PBMCs from healthy donors were obtained from buffy coats processed by the National Blood Services , Colindale , UK , or heparinised whole blood from healthy volunteers following an explanation of the nature and possible consequences of the study and written informed consent . Ethical approval was received from the Human Research Ethics Review Board of the Faculty of Health Sciences , University of Cape Town , and UK MRC National Institute for Medical Research . Mtb H37Rv frozen stocks were prepared as previously described [49] ( S1 File ) . For PBA treatment experiments , Mtb was inoculated from frozen stocks into 7H9/ADC/0 . 01% tyloxypol and grown in rolling culture at 37°C . Ten-fold serial dilutions of exponential phase culture were plated on 7H11/OADC agar containing 0-4mM PBA and incubated at 37°C with CFU monitored over 21 days . To analyse the effect of PBA in liquid culture , exponential phase cultures were sub-cultured to OD600 0 . 01 and after 4 days growth ( Average OD600 of 0 . 56 ) PBA was added to at final concentrations of 0 . 4-4mM . OD600 was monitored over 10 days . Two and six days after addition of PBA , 10-fold serial dilutions ( in PBS/0 . 05% Tween 80 ) of each culture were plated for CFU analysis . After 10 days of treatment , each rolling culture was pelleted , washed twice in PBS/0 . 01% tyloxypol and resuspended in medium without PBA to OD600 0 . 1 and culture continued for a further 8 days , monitoring OD600 . PBA supplemented media and an aliquot of each 10-day PBA-treated culture was twice filter sterilised and pH was measured ( SevenEasy conductivity meter , Mettler Toledo ) . For the Alamar Blue ( Invitrogen ) reduction checkerboard assays Mtb was inoculated in 96-well plates at OD600 0 . 0002 in 7H9/ADC containing 2-fold serial dilutions of PBA , 25 ( OH ) D3 or HCl . After 13 days of culture , a tenth of the volume of Alamar Blue was added and colour formation observed after overnight incubation . To determine the effect of PBA pre-treatment of Mtb on the outcome of MDM infection , Mtb stocks were prepared by treating an exponential phase culture at 0 . 5 OD600 ± 4mM PBA for 4 days . Cultures were pelleted , washed twice in PBS/0 . 05% and resuspended in 7H9 , OD600 determined and then an infection stock prepared in RPMI for addition to MDM at final OD600 0 . 005 ( 0 . 2% 7H9 ) . Infection stocks were titrated and plated for CFU , confirming MOI 1:1 ( S3 Fig ) . For imaging , GFP-H37Rv was grown to exponential phase in 7H9/0 . 05% Tween 80 with 25μg/ml kanamycin , subcultured to 0 . 18 OD600 and grown ± 4mM PBA for 4 days . 1ml of culture was fixed in 6% PFA for 45min , pelleted , washed in PBS and resuspended in water or Mowiol for mounting under coverslip . Slides were viewed on an Axiovert 200M LSM 510 Meta confocal microscope ( Zeiss ) and images analysed in Zen Blue 2012 software ( Zeiss ) . PBMC were prepared on a Ficoll-Paque density gradient and used immediately without cryopreservation . PBMC were cultured at 5x105 cells/48-well in RPMI 1640 ( containing 10mM sodium pyruvate , 50mM glutamine and 10% foetal calf serum ( FCS ) ) for 3 days prior to infection at 37°C/5% CO2 . To generate MDM , MN were obtained from PBMC by CD14+ magnetic-activated cell sorting ( MACS , Miltenyi ) . MN were cultured at 2x106 cells/6-well in RPMI supplemented with 5ng/ml GM-CSF for 6 days . Following differentiation , MDM were detached and plated at 9x104 cells/96-well in RPMI and rested at 37°C for one hour before infection . PBMC were infected at a multiplicity of infection ( MOI ) normalized to average MN count ( 10% PBMC ) , giving MOI bacillus:PBMC 0 . 1:1 . At the time of PBMC infection a 2-fold serial dilution of PBA ( 0-8mM ) was added to cultures . After 4 and 96 hrs , PBMC were pelleted by centrifugation , washed twice with PBS , lysed for 30min with H2O/0 . 05% Tween 80 and 5-fold serial dilutions of lysate plated for intracellular CFU . MDM were infected ( MOI 1:1 ) for 4 hrs , extracellular Mtb removed by washing cells twice with PBS and then cultured in RPMI containing a 2-fold serial dilution of PBA ( 0 . 5-4mM ) , a 10-fold serial dilution of 25 ( OH ) D3 ( 10-1000nM ) , 4mM PBA+100nM 25 ( OH ) D3 , or vehicle control ( 0 . 01% EtOH ) for up to 7 days . To determine the effect of PBA on MDM phagocytic function , MDM were treated for 48 hrs with 4mM PBA , washed three times with PBS , infected with Mtb and intracellular Mtb plated for CFU 4 hrs post-infection . Additional 25 ( OH ) D3 and co-treated cultures were also supplemented with 10μg/ml PR3 at the time of treatment and CFU monitored 7 days post-infection . For MAPK signalling inhibition , MDM were pre-incubated for 1hr with 20μM SB202190 , SP600125 , U0126 ( Sigma ) or vehicle control ( 0 . 08% DMSO ) , prior to and during stimulation . MDM were detached after 48 hrs treatment with PBA , by incubating in Acutase ( Invitrogen ) at 37°C for 30min , washed twice with PBS , and suspended in PBS/5% FCS . Cells were incubated with either monoclonal mouse anti-human CD35/FITC , CD11c/PE , CD16/PE . Cy5 , CD11b/APC conjugated antibodies or respective IgG-conjugated antibodies ( BD Bioscience ) in the dark at 4°C for 30 min . Cells were washed , resuspended in CytoFix/Perm ( BD Bioscience ) for 20 min at 4°C and then washed twice and resuspended in PBS/5% FCS . An aliquot of unfixed cells were also incubated with propidium iodide . Ten thousand events were analysed for each sample by immunofluorescence using flow cytometry ( BD FACSCalibur ) . Results were analysed using Flow Jo ( version 10 ) . RNA was extracted from MDM samples following homogenisation in TRI Reagent ( Sigma-Aldrich ) and storage at -80°C until batch-processed . RNA was extracted with chloroform in phase lock tubes ( 5 Prime ) following the manufactures protocol , using linear poly-acrylamide ( Ambion ) as a carrier protein and suspension in RNase-free H2O ( Sigma ) . RNA integrity and concentration was determined on a NanoDrop 2000 ( Thermo Scientific ) and by running on a 1 . 5% TAE agarose gel . 150ng RNA was reverse transcribed using the RT2 First Strand Kit ( SABiosciences ) which includes a genomic DNA ( gDNA ) elimination step . cDNA was analysed using 384-well RT2 Profiler Custom PCR Arrays ( SABiosciences ) which included 28 genes involved in innate and adaptive immunity ( CAMP , CASP1 , CXCL7 , CXCL8 , CXCL9 , CXCL10 , IFNB1 ( OMIM 147640 ) , IFNG , IL1B , IL6 , IL10 , IL12A , IL12B ( OMIM 161561 ) , IL23A ( OMIM 605580 ) , IL36G , IL37 , LTF , NOD2 , NCF4 , NLRP3 , PGLYRP1 ( OMIM 604963 ) , PROC , ARG1 , TNF , TREM1 ) and vitamin D metabolism ( CYP24A1 , CYP27B1 , VDR ) , plus the house-keeping ( HK ) gene RPL13A ( UniProt P40429 ) and three control reactions to monitor gDNA contamination and reverse transcription efficiency on the Roche 480 platform . A comparison of 5 HK genes across all treatment and infection conditions indicated that RPL13A was the most stable ( S9 Fig ) . Fold induction was calculated from Ct values using the ΔΔCt method normalising all samples to baseline untreated . For PRTN3 expression analysis RNA was extracted from 1 million PBMC and MN following homogenisation in TRI Reagent ( as for MDM ) and from 3ml whole blood collected in Tempus tubes and extracted with the Tempus Spin RNA Isolation Kit ( Life Technologies ) and storage at -80°C . 100ng RNA was reverse transcribed using SuperScript VILO cDNA synthesis kit ( Life Technologies ) and real-time RT-PCR was performed using primers ( S1 Table ) for PRTN3 and GNB2L1 ( OMIM 176981 , HK ) with Fast SYBR Green chemistry using 10% cDNA on a QuantStudio 7 ( Life Technologies ) . Stable expression of GNB2L1 across all sample types was confirmed by Ct comparison ( S10 Fig ) . Expression levels of PRTN3 were normalised to that of GNB2L1 by ΔΔCt and fold change to mean MDM expression calculated . For CAMP , CYP27B1 and CYP24A1 expression analysis ( primer sequences in S1 Table ) 100ng RNA from MDM was reverse transcribed using High Capacity cDNA synthesis kit ( Life Technologies ) and real-time RT-PCR was performed with Fast SYBR Green chemistry ) , using 10% cDNA on an ABI 7500 fast ( Life Technologies ) . Absolute quantification was carried out using standard curves generated by serial dilution of target amplicon-containing plasmids ( pGEM-T easy , Promega ) , to cover up to 5 logs of amplicon copy number per microliter and absolute copy number normalized to GNB2L1 . Supernatants were harvested from MDM infection experiments 24 and 72 hrs post-infection and double sterilized through 0 . 2uM PVDF filters ( Corning ) . Concentrations of IL-1β , IL-1RA , IL-2 ( OMIM 147680 ) , IL-2R , IL-4 ( OMIM 147780 ) , IL-5 ( OMIM 147850 ) , IL-6 , IL-7 ( OMIM 146660 ) , IL-10 , IL-12 ( p40/p70 ) , IL-13 ( OMIM 147683 ) , IL-15 , IL-17 ( OMIM 603149 ) , G-CSF , GM-CSF , IFNα , IFNγ , TNF , CXCL8 , CXCL9 , CXCL10 , CCL2 , CCL3 , CCL4 , CCL5 , CCL11 ( OMIM 601156 ) , EGF ( OMIM 131530 ) , FGFβ ( OMIM 134920 ) , HGF ( OMIM 142409 ) and VEGF ( OMIM 192240 ) were quantified using a human 30-plex bead immunoassay panel ( Invitrogen ) and analysed on a BioPlex 200 platform ( Bio-Rad ) . Analytes at the limit of detection were given the value zero . Concentrations from infected samples were baseline corrected by subtracting the concentration of uninfected samples prior to statistical analysis . Cells from all donors received all treatments ( paired analyses ) and RNA , culture supernatants and CFU where matched for donor where multiple analyses where conducted under the same infection conditions , where possible . Infection experiments were performed on up to 10 donors , in triplicate for CFU and in duplicate for RNA and secreted protein analysis . Mtb culture experiments were done in duplicate and repeated . Samples were assigned a numerical de-identifier prior to analysis and decoded after raw data was captured . Statistical significance was tested using paired t-test of single treatment analysis , Wilcoxon match-pairs for non-parametric data , 1way-ANOVA , with Bonferroni multiple comparison test for dose response analysis at a single time point or 2way-ANOVA , with Dunnett's multiple comparison test for dose response analysis over multiple time points . GLM , PCA and hierarchical clustering were conducted on gene expression and protein secretion data using Qlucore Omics Explorer 2 . 2 ( Qlucore AB , Lund , Sweden ) ( S1 File ) .
|
Tuberculosis ( TB ) is the world’s leading bacterial cause of death . Effective treatment currently requires a minimum of 4 drugs taken for at least 6 months . While these drugs kill the TB causing bacteria ( Mtb ) , they do not directly resolve the inmmunopathology associated with morbidity . Immunomodulatory agents that not only enhance an individual’s ability to kill Mtb but also help heal lung pathology could be used as adjuncts to current therapies to improve treatment outcome . Phenylbutyrate ( PBA ) has been in clinical use for more than 30 years to treat a range of conditions . It has also been shown to synergise with vitamin D to induce cellular production of the anti-Mtb peptide , cathelicidin . We investigated whether PBA and vitamin D synergistically kill Mtb in human macrophages and whether PBA has any independent effect on macrophages and Mtb . At concentrations that are achieved in plasma clinically , PBA inhibited Mtb growth . PBA also inhibited growth of Mtb in human macrophages via a cell-dependent mechanism , inducing the inflammasome pathway and antimicrobial lactoferrin . PBA also synergistically enhanced macrophage response to vitamin D and co-treatment further inhibited Mtb growth , when synergistically-induced cathelicidin was activated . PBA and vitamin D may therefore prove an effective combinatorial adjunct therapy for tuberculosis .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[] |
2015
|
Phenylbutyrate Is Bacteriostatic against Mycobacterium tuberculosis and Regulates the Macrophage Response to Infection, Synergistically with 25-Hydroxy-Vitamin D₃
|
How can we explain the uneven decline of syphilis around the world following the introduction of penicillin ? In this paper we use antenatal syphilis prevalence ( ASP ) to investigate how syphilis prevalence varied worldwide in the past century , and what risk factors correlate with this variance . 1 ) A systematic review using PubMed and Google Scholar was conducted to identify countries with published data relating to ASP estimates from before 1952 until the present . Eleven countries were identified ( Canada , Denmark , Finland , India , Japan , Norway , Singapore , South Africa , United States of America ( USA ) , United Kingdom ( UK ) and Zimbabwe ) . The ASP epidemic curve for each population was depicted graphically . In South Africa and the USA , results are reported separately for the black and white populations . 2 ) National antenatal syphilis prevalence estimates for 1990 to 1999 and 2008 were taken from an Institute for Health Metrics and Evaluation database on the prevalence of syphilis in low risk populations compiled for the Global Burden of Diseases study and from a recent review paper respectively . National ASPs were depicted graphically and regional median ASPs were calculated for both time periods . 3 ) Linear regression was used to test for an association between ASP in 1990–1999 and 2008 and four risk factors ( efficacy of syphilis screening/treatment , health expenditure , GDP per capita and circumcision prevalence ) . WHO world regions were included as potential explanatory variables . In most populations , ASP dropped to under 1% before 1960 . In Zimbabwe and black South Africans , ASP was high in the pre-penicillin period , dropped in the post-penicillin period , but then plateaued at around 6% until the end of the 20th century when ASP dropped to just above 1% . In black Americans , ASP declined in the post penicillin period , but plateaued at 3–5% thereafter . ASP was statistically significantly higher in sub-Saharan Africa in 1990–1999 and 2008 than in the other world regions ( P < 0 . 001 ) . On multivariate analysis in both time periods , ASP was only associated with residence in sub-Saharan Africa . Further research is necessary to elucidate the reasons for the higher prevalence of syphilis in sub-Saharan Africa .
Can a meaningful pattern be discerned in the large variations in syphilis rates over the last century ? A first step toward answering this question is the mapping of syphilis rates across time and place followed by correlation analyses with possible explanatory variables [1] . Numerous papers have published descriptions of longitudinal changes in reported cases of syphilis within countries that are associated with events such as the introduction of penicillin , social disruption associated with wars or the collapse of the Soviet Union [2 , 3] . Other analyses have investigated reasons for the higher incidence in subpopulations such as men who have sex with men ( MSM ) [4] and non-Hispanic blacks in the United States of America ( USA ) [5] . The World Health Organization ( WHO ) and others have published estimates of world regional syphilis prevalence for 1998 , 2001 , 2005 and 2012 [6–9] and national estimates of antenatal syphilis prevalence for 2008 [10] . This paper extends this descriptive epidemiology of syphilis in three ways . Firstly , we conduct a systematic review of countries with published antenatal syphilis prevalence ( ASP ) estimates from pre-1952 till the present . We find 13 populations in 11 countries with available data over the last century and use this to chart variations in these populations’ ASP trajectories . Secondly , we generate ASP prevalence estimates by country for the time period 1990–1999 . The prevalence of syphilis plummeted in a number of hyper endemic countries around the time of the AIDS epidemic in the late 1990s . This decline was in part due to the widespread introduction of the syndromic approach to STI management [11] and in part due to the effect of AIDS mortality breaking up sexual networks [12–15] . As a result , correlation analyses of risk factors associated with national ASP from 1990–1999 would be less likely to be affected by this effect of AIDS mortality than those from 2008 . Thirdly , we use the 1990–99 and 2008 national ASP estimates to assess if four risk factors , efficacy of syphilis screening/treatment , health expenditure , GDP per capita and circumcision prevalence , are correlated with ASP in these two time periods . Accurate mapping requires a measure of syphilis that can be meaningfully used to compare syphilis rates between populations . The number of cases of early stage syphilis per year reported as part of national surveillance programs ( case-reporting-based syphilis incidence—CSI ) is a commonly used variable and has been shown to be useful to compare syphilis trends over time within countries [3] . Case reporting is however beset by numerous problems: many cases of syphilis are asymptomatic [6 , 16] , health seeking behavior varies between locales [17] and the completeness of reporting varies tremendously between different types of institutions [18] , regions within countries [18] and between countries . A twenty four country study from Europe found that syphilis case surveillance coverage varied from below 10% to over 75% in these developed countries [19] . In addition , there are considerable differences in how sexually transmitted illnesses ( STIs ) are classified ( such as syndromically or etiologically ) [6] and in the accuracy of diagnosis of syphilis when the etiological system is used [15 , 20] . As a result , the CSI indicator is suboptimal for comparing syphilis rates between populations [10 , 17 , 21 , 22] . The WHO has suggested that it should continue to be used to provide minimum estimates of incidence in low prevalence countries [6] . In 1960 , Guthe noted the deficiencies of CSI and proposed that “the most useful index of the prevalence of syphilis in a population is—for obvious reasons—the sero-reactor rate in pregnant women [21] . ” The WHO has recommended the use of ASP as an indicator of syphilis prevalence in high prevalence countries [6] . In this paper we take up this suggestion to use antenatal syphilis prevalence ( ASP ) —the serologically determined prevalence of syphilis in antenatal populations—to further describe the global epidemiology of syphilis .
We used various sources to obtain the number of cases of acquired syphilis per 100 000 per year for 8 of the 11 reviewed countries with available published data from surveillance data . Much of the SA and USA surveillance data was not disaggregated by race and thus we report both the overall national incidence and the more limited data available for the black and white racial groups in these countries . Details of the sources of data are provided in Table 2 . The estimated prevalence of active syphilis can vary according to whether it is estimated via a treponemal or a nontreponemal test or a combination of the two [36] . To account for this , we applied recently published correction factors to adjust the ASP estimates for the 13 population longitudinal analysis and for the 1990–99 estimates according to the testing algorithm used [36] . These correction factors are based on a systematic review and meta analysis that estimated the proportion of pregnancies with “probable active syphilis” based on different kinds of tests from studies that conducted both treponemal and nontreponemal testing on samples [36] . Studies that use both a treponemal and a nontreponemal test to diagnose infection were regarded as offering the most accurate measure of active syphilis prevalence and no correction factor was applied . Studies using only a treponemal test will falsely label persons with old or treated syphilis as having active syphilis and a correction factor of 0 . 536 was applied to these prevalence estimates . Studies using only nontreponemal testing will falsely diagnose active syphilis in persons with other inflammatory conditions that lead to biological false positive reactions in the nontreponemal test . A correction factor of 0 . 522 was applied to these prevalence estimates . In addition the study recommended the use of a correction factor of 0 . 686 for studies that did not report the type of testing used . This was calculated as the average of the two correction factors noted above ( 0 . 536 , 0 . 522 ) . The 2008 ASP estimates included a correction factor that similarly estimated the prevalence of active syphilis . All ASPs reported and used in analyses therefore refer to the adjusted estimates . Simple and multiple linear regression was used to evaluate the relationship between syphilis prevalence and each of the explanatory variables . The analyses were conducted separately with syphilis prevalence for 1990–1999 and 2008 as the outcome variables . A brief historical overview of the serological diagnostic tests used to assess syphilis prevalence and their diagnostic accuracy is provided in Box 1 . All analyses were performed in STATA 13 . 0 ( StataCorp LP , College Station , TX , USA ) .
ASP declined in all populations assessed in this period ( Fig 2 ) but at different times and rates . In the case of Canada , Denmark , Finland , Norway and whites in the USA , ASP dropped to below 1% before 1945 when penicillin started to become widely available . We found no data points for the UK between 1922 and 1947 but by 1947 its ASP had declined to 0 . 4% . By 1955 ASP in Singapore and Japan had declined to below 2% . There was little data available for whites in SA but in a small sample in 1972 , prevalence was 2 . 5% and at the time of the first large sample ( 1991 ) ASP was under 1% . ASP declined somewhat more slowly in India ( 1 . 6% in 1973 and 0 . 1% in 1982 ) , and USA blacks ( 3 . 4% in 1978 and 4 . 2% in 2001 ) . In Zimbabwe and black South Africans , ASP was high in the pre penicillin period , dropped in the post-penicillin period but then plateaued at around 6% until the end of the 20th century when ASP dropped precipitously to just above 1% . Changes in incidence of reported cases of syphilis per year per 100 000 population were found to be broadly commensurate with ASP figures ( Fig 3 and Table 2 ) . Denmark , Norway and the UK had low syphilis incidence prior to the introduction of penicillin . By 1939 all three countries had an incidence below 20 . After brief increases during the Second World War , incidence in these countries as well as Canada , Japan , Singapore and the USA ( overall population ) declined rapidly in the post-war period . By 1956 the incidence in Denmark , Norway , Canada , Japan , Singapore , UK and whites in the USA was below 30/100 000 . Incidence in blacks in the USA remained approximately an order of magnitude greater than whites at all time points for which we have data . Incidence in black South Africans in 1939 was five times higher than whites . The median incidence in ten cities in South Africa ( overall population ) between 1937 and 1939 was 1474 ( range 691–4028 ) . This declined somewhat to an estimated 530 by 1999 and more precipitously after 1999 in keeping with the contemporaneous declines in ASP [15 , 37] . Numbers of cases diagnosed with syphilis in Zimbabwe were high in all reports in the 20th century [38–41] but we have not reported incidence as population denominators were not provided and a number of expert reviews found evidence of both extensive misclassification of chancroid as syphilis [20 , 38 , 40] and endemic treponemes causing false positive serological diagnoses of syphilis [40] . In both 1990–99 and 2008 on bivariate analyses , ASP was negatively associated with GDP per capita , health expenditure and residence in sub- Saharan Africa ( henceforth termed Africa; Table 4 ) . On multivariate analysis in both time periods , ASP was only associated with residence in Africa ( Table 5 ) . We found no evidence of an association between GDP per capita , health expenditure , screening/treatment or circumcision prevalence and ASP . Differences in national ASP in the two time periods are represented graphically in Fig 4 . By 1990–99 , ASP had dropped to a median 3 . 0 ( IQR 1 . 4–4 . 4 ) in Africa versus 0 . 1 ( IQR 0 . 1–0 . 1 ) in Europe , 0 . 4 ( IQR 0 . 2–1 . 0 ) in the East Mediterranean , 0 . 4 ( IQR 0 . 2–1 . 4 ) , 0 . 6 ( IQR 0 . 1–2 . 5 ) in the Americas , 1 . 3 ( IQR 0 . 7–1 . 7 ) in South/South Eastern Asia and in the Western Pacific . ASP was statistically significantly higher in Africa in 1990–1999 than in the other world regions except South/South East Asia where this relationship was not statistically significant ( Table 4 ) . By the following decade the median prevalence of ASP had fallen in most regions including Africa but Africa’s median ASP remain statistically significantly higher than the ASP in Europe , Eastern Mediterranean and the Americas . Sensitivity analyses were conducted using the uncorrected ASPs . This did not alter the bivariate and multivariate results .
By 1959 , 600 tons of penicillin were being produced per year and there is little doubt that its widespread use played an important role in the decline of syphilis rates [21] . However , ASP in certain populations around the world exhibited more resilience in response to the introduction of penicillin and associated syphilis control strategies . The populations that in the 1990’s had high prevalences of syphilis and HSV-2 went on to have high HIV prevalences [52] . Although this analysis suffers from numerous limitations , if taken in conjunction with the other evidence reviewed here it generates the hypothesis that more connected sexual networks may have been partly responsible for the higher prevalences of syphilis and other STIs in high prevalence populations . High AIDS related mortality has been shown in both MSM in the USA and the generalized HIV epidemics of Africa to have disrupted sexual networks and thereby reduced the prevalence of syphilis [12–14] . It is possible that the widespread use of antiretroviral therapy may be followed by the reconstitution of more connected sexual networks and a return of syphilis in these populations—as has occurred in MSM in the USA and elsewhere [76] . An improved understanding of the factors underpinning variations in syphilis rates around the world in the last 100 years could help us to better understand the current and predict the future patterning of syphilis prevalence .
|
Syphilis rates have varied tremendously between different populations around the world . We conducted a systematic review of syphilis prevalence in pregnant women in 13 populations with available data for the last 100 years . Our findings were that in most populations syphilis prevalence dropped to under 1% before 1960 . In the 2 populations from sub Saharan Africa , the syphilis prevalence remained around 6% until 50 years after the introduction of penicillin . Other systematic reviews were utilized to provide syphilis prevalence estimates for all countries with available data for the periods 1990–1999 and 2008 . We assessed if there was a correlation between national syphilis prevalence in these periods and five explanatory factors . Only residence in sub-Saharan Africa was associated with syphilis prevalence in both time periods . These findings , considered in conjunction with other types of evidence we review , such as the strong correlations at population level between syphilis prevalence and those of Herpes Simplex Virus-2 prevalence and HIV prevalence , suggest that common risk factors may underpin the spread of all three of these sexually transmitted diseases . Establishing what these factors are is of great importance to improve the health of highly affected populations such as those in sub-Saharan Africa .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"antimicrobials",
"urology",
"medicine",
"and",
"health",
"sciences",
"enzyme-linked",
"immunoassays",
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"retroviruses",
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"bacterial",
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"immunodeficiency",
"viruses",
"women's",
"health",
"rna",
"viruses",
"antibiotics",
"sexually",
"transmitted",
"diseases",
"pregnancy",
"global",
"health",
"neglected",
"tropical",
"diseases",
"pharmacology",
"norway",
"immunologic",
"techniques",
"africa",
"research",
"and",
"analysis",
"methods",
"europe",
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"occupational",
"health",
"infectious",
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"penicillin",
"medical",
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"lentivirus",
"organisms",
"syphilis"
] |
2016
|
The Global Epidemiology of Syphilis in the Past Century – A Systematic Review Based on Antenatal Syphilis Prevalence
|
Nuclear factor κB ( NF-κB ) is a transcription factor important for regulating innate and adaptive immunity , cellular proliferation , apoptosis , and senescence . Dysregulation of NF-κB and its upstream regulator IκB kinase ( IKK ) contributes to the pathogenesis of multiple inflammatory and degenerative diseases as well as cancer . An 11–amino acid peptide containing the NF-κB essential modulator ( NEMO ) -binding domain ( NBD ) derived from the C-terminus of β subunit of IKK , functions as a highly selective inhibitor of the IKK complex by disrupting the association of IKKβ and the IKKγ subunit NEMO . A structure-based pharmacophore model was developed to identify NBD mimetics by in silico screening . Two optimized lead NBD mimetics , SR12343 and SR12460 , inhibited tumor necrosis factor α ( TNF-α ) - and lipopolysaccharide ( LPS ) -induced NF-κB activation by blocking the interaction between IKKβ and NEMO and suppressed LPS-induced acute pulmonary inflammation in mice . Chronic treatment of a mouse model of Duchenne muscular dystrophy ( DMD ) with SR12343 and SR12460 attenuated inflammatory infiltration , necrosis and muscle degeneration , demonstrating that these small-molecule NBD mimetics are potential therapeutics for inflammatory and degenerative diseases .
Nuclear factor κB ( NF-κB ) is a transcription factor essential for regulating immune responses , cell proliferation , apoptosis , embryonic development , senescence , and cancer [1] . In mammalian cells , the NF-κB family is composed of 5 subunits , RelA/p65 , RelB , C-Rel , p50 ( p105/NF-κB1 ) , and p52 ( p100/NF-κB2 ) , all containing a Rel-homology domain ( RHD ) required for homo- or heterodimerization [1] . NF-κB dimers are sequestered in the cytoplasm by an inhibitory protein IκBα , which masks the conserved nuclear localization sequence ( NLS ) of RelA/p65 to prevent nuclear translocation [1] . Upon stimulation , IκBα undergoes phosphorylation , polyubiquitination , and proteasome-mediated degradation , releasing the NF-κB dimers to enable nuclear translocation [1] . This activated NF-κB down- or up-regulates target gene expression by binding to the κB enhancer or promoter elements [1] . Inducers of NF-κB activity include pro-inflammatory cytokines , such as tumor necrosis factor α ( TNF-α ) , interleukin-1 ( IL-1 ) , and lipopolysaccharide ( LPS ) , as well as T-cell receptor ( TCR ) ligands , genotoxic , and oxidative stress [1] . NF-κB activation is regulated by the IκB kinase ( IKK ) complex , composed of 2 catalytic subunits , IKKα and IKKβ , and a regulatory subunit NF-κB essential modulator ( NEMO ) /IKKγ [2–4] . Domains in the C-termini of IKKα and IKKβ , required for interaction with the α-helical region in the N-terminus of NEMO , are termed NEMO-binding domains ( NBDs ) [5] . An 11–amino acid peptide derived from the NBD domain of IKKβ ( amino acids 735–745 ) can disrupt the association of IKKβ and NEMO and reduce NF-κB activation when fused to a protein transduction domain ( PTD ) for intracellular delivery [5] . The NBD peptide has strong therapeutic effects in numerous inflammatory and degenerative disease models in mice and other species . Chronic , systemic administration of the NBD peptide attenuates macrophage-mediated muscle necrosis and degeneration in mdx mice , a murine model of Duchenne muscular dystrophy ( DMD ) as well as in the golden retriever muscular dystrophy ( GRMD ) canine model of DMD [6–8] . Similarly , the NBD peptide ameliorates active chronic colitis in IL-10-deficient mice without affecting NF-κB basal activity when administered systemically [9] . Intra-articular injection of NBD peptide also attenuates synovial inflammation and the severity of arthritis in a rat model of adjuvant arthritis [10] . It also ameliorates inflammation-induced osteoclastogenesis and arthritis by down-regulating NF-κB target genes , TNF-α , and IL-1β [11] . Systemic delivery of the NBD peptide reduces the severity of Parkinson’s disease by suppressing nigral microglial activation and reducing dopaminergic neuronal loss as well as alleviates nephropathy and atherosclerosis in type 1 diabetic mice [12–14] . In addition , the peptide prevents LPS-induced pulmonary inflammation in sheep and improves pulmonary function in a piglet model of acute respiratory distress syndrome by topical administration [15 , 16] . Moreover , clinical testing of the NBD peptide for local treatment of canine diffuse large B cell lymphoma revealed a reduction in the proliferation of malignant B cells [17] . In addition , chronic systemic administration of NBD peptide delays the onset and reduces the severity of multiple aging symptoms and pathology in Ercc1-/Δ mice , a mouse model of human progeria [18] . Despite these strong and varied therapeutic effects of PTD-NBD peptides in animal models , the expense of peptide synthesis , the short half-life of the peptide , and its lack of oral bioavailability limit its clinical use . Thus , development of small molecules that mimic the NBD peptide , targeting the NBD of IKKβ to disrupt its binding to NEMO , would have clinical utility . Here , a structure-based pharmacophore model that mimics these interactions was derived from the crystal structure of the IKK complex , followed by virtual screening using this model against commercially available databases of drug-like molecules . The resulting hits were prioritized using in silico Absorption , Distribution , Metabolism , Excretion , and Toxicity ( ADME/Tox ) filtering and molecular docking to determine the higher-affinity hits . Using these as starting points , multiple rounds of medicinal chemistry optimization resulted in the discovery of compounds capable of inhibiting LPS- and TNF-α-induced NF-κB activation by disrupting the association between IKKβ and NEMO . Also , these compounds exhibited potent therapeutic effects in murine models of LPS-induced endotoxemia and DMD , suggesting their potential as therapeutic drugs for clinical management of diseases driven by IKK/NF-κB activation .
Recognition of small molecules by proteins is largely mediated by molecular surface complementarities [19 , 20] . Thus , the site of protein–protein interaction between NEMO and IKKβ potentially is a good target for in silico drug screening . To investigate the chemical features essential in the protein–protein interaction , the X-ray structure of the NEMO/IKKβ complex retrieved from the Protein Data Bank ( PDB ) ( ID 3BRV ) was used to generate a structure-based pharmacophore ( Fig 1A ) [21] using the pharmacophore generation module of LigandScout [22 , 23] . Each interacting atom from each residue was “translated” into a pharmacophoric feature , resulting in the structure-based pharmacophore ( Fig 1B ) consisting of 8 features and 13 exclusion volumes , representing important atoms from the protein’s environment . This pharmacophore model was used to screen a subset of the drug-like ZINC 10 . 0 database set ( approximately 13 . 5 million compounds ) [24] . We identified 161 compounds that matched at least 6 features out of 8 of the pharmacophore model . Twenty hits had a root-mean-square deviation ( RMSD ) <1 and were further prioritized using ADME/Tox-predicted properties . Three compounds successfully passed these filters ( Fig 1C ) . To determine whether the small molecules identified by the in silico screening inhibit NF-κB activation , a HEK293 cell line stably expressing a luciferase reporter driven by a synthetic , NF-κB-dependent promoter was utilized [25] . To induce NF-κB activation , cells were treated with 10 ng/mL of TNF-α and harvested 3 h post treatment for analysis of luciferase activity . Treatment with ZINC12909780 slightly down-regulated NF-κB activation at a concentration of 100 μM , whereas the luciferase activity remained unchanged in cells treated with ZINC05682974 or ZINC09327678 ( Fig 2A ) . To determine whether ZINC12909780 inhibits NF-κB in a dose-dependent manner , concentrations were tested at 0 , 6 . 25 , 25 , 50 , and 100 μM . Only the high concentrations ( 50 and 100 μM ) of the compound were able to inhibit TNF-α-induced NF-κB activation significantly ( Fig 2B ) [25] . To identify NBD mimetics with higher biological activity than ZINC12909780 or the NBD peptide , the ZINC 10 . 0 database ( approximately 13 . 5 million compounds ) was screened in silico for structurally similar compounds [26] . Fifteen analogs with a similarity score >90% were identified , and 13 that passed all the ADME/Tox filters were acquired for testing ( S1 Table ) . Four of the compounds—ZINC9642366 ( Zinc1 ) , ZINC3369392 ( Zinc5 ) , ZINC3269261 ( Zinc8 ) , and ZINC3264658 ( Zinc9 ) , as well as an IKK active site inhibitor IKKi VII used as a positive control—lowered NF-κB activity robustly , while other analogs had minimal effects ( Fig 2C ) . To rule out the possibility that the reduction observed in luciferase assays was due to drug toxicity , a colorimetric MTT assay using 3- ( 4 , 5-dimethylthiazol-2-yl ) -2 , 5-dephenyltetrazolium bromide was performed to assess cell viability . Treatment with ZINC8 resulted in 40% of cell death at 24 h , suggesting that at least part of the reduction in luciferase activity could be attributed to cytotoxicity ( Fig 2D ) . Because ZINC5 displayed potent NF-κB inhibitory efficacy in cell culture with little toxicity ( less than 10% ) , it was tested for dose-dependent inhibition of NF-κB . ZINC5 showed a greater inhibitory effect compared to ZINC12909780 without overt cell toxicity ( Fig 2E & 2F ) . To confirm that the NBD mimetics reduce IKK kinase activity , phosphorylation of IκBα by IKK in response to 10 ng/ml of TNF-α was measured by western blot at 0 , 5 , and 10 min after stimulation . ZINC5 reduced the level of p-IκBα following stimulation , while ZINC12909780 led to a less robust reduction ( Fig 3A ) . To determine whether the mimetics also reduce NF-κB DNA binding activity , electrophoretic mobility shift assay ( EMSA ) was conducted both in vitro and in vivo . ZINC5 and ZINC12909780 were tested in C2C12 cells ( a mouse myoblast cell line ) at 200 μM and were shown to inhibit TNF-α-induced NF-κB DNA binding activity ( Fig 3B ) . Similarly , a single intraperitoneal ( i . p . ) injection of either of these 2 small molecules at 10 mg/kg inhibited NF-κB DNA binding activity in quadriceps that is chronically up-regulated in mdx mice ( Fig 3C ) . However , ZINC5 was extremely unstable even in cell culture media containing FBS , while ZINC12909780 was relatively more stable . The addition of acetonitrile increased the stability of both ZINC compounds , especially ZINC 5 ( Fig 3D ) . ZINC5 and ZINC12909780 both contain ester bonds , leading to their rapid degradation in the presence of serum ( Fig 3D ) . To improve bioactivity and stability , structure–activity relationship studies ( SARs ) were performed , and more than 100 small molecules were synthesized and tested . Four lead NBD mimetics that showed enhanced inhibitory effects , compared to the original ZINC compounds , were identified , including 3 non-esters—SR12343 , SR12460 , and SR12454—and 1 ester SR11481 ( Fig 4A & 4B ) . These 3 non-esters significantly inhibited TNF-α-mediated NF-κB activation with half maximal inhibitory concentrations ( IC50 ) of 11 . 34 μM , 20 . 24 μM , and 37 . 02 μM , respectively ( Table 1 , Fig 4B & S1A Fig ) . The ester SR11481 was not as effective ( IC50 45 . 03 μM ) , possibly due to the presence of the ester bond ( Fig 4B ) . A renilla luciferase reporter was cotransfected for normalization . To determine whether the inhibitory effect of the mimetics is limited to TNF-α-mediated induction of IKK/NF-κB , LPS-mediated NF-κB activation was examined . NF-κB activity was induced in Raw 264 . 7 by 1 μg/ml of LPS for 2 h , and the IKKi VII ( 2 μM ) and the 8K-NBD peptide ( 400 μM ) were included as positive controls . Expression of the NF-κB target genes cyclooxygenase 2 ( COX-2 ) , IL-6 , IL-1β , TNF-α , IκBα , and inducible nitric oxide synthase ( iNOS ) were determined by quantitative real-time polymerase chain reaction ( qRT-PCR ) analysis . SR12460 and SR12454 , which are more similar in structure than the other 2 mimetics , were able to significantly inhibit the transcription of all NF-κB target genes tested ( Fig 4C ) . SR12343 displayed a similar profile to the 8K-NBD peptide , showing significant inhibition on COX-2 , IL-6 , and iNOS expression at a much lower concentration ( 50 μM ) compared to NBD peptide ( 400 μM ) . SR11481 did not induce a detectable suppression of the NF-κB target genes , likely due to its poor stability . IKKi VII , while able to inhibit most NF-κB target gene expression , failed to down-regulate iNOS expression , which was significantly inhibited by the NBD peptide and all non-ester NBD mimetics . This suggests that IKK inhibitors targeting the ATP-binding pocket likely down-regulate the expression of a slightly different set of NF-κB regulated genes . To confirm the qRT-PCR results , the levels of IL-6 production in Raw 264 . 7 cells activated by LPS were analyzed by ELISA . SR12460 and SR12454 were able to inhibit IL-6 secretion significantly in a dose-dependent manner ( Fig 4D ) . Similarly , SR12343 inhibited IL-6 production , but was not as effective as SR12460 and SR12454 , consistent with its higher IC50 in HEK293 cells . Although SR11481 failed to inhibit IL-6 significantly at the mRNA level , there was a significant reduction in the accumulation of IL-6 protein at 24 h . Moreover , SR11481 appears to be more effective at a lower concentration of 25 μM versus 50 μM . To determine whether the novel NBD mimetics target the NEMO-IKKβ interaction in vivo , co-immunoprecipitations were performed using extracts from Raw 264 . 7 macrophages ( Fig 5A ) . All 4 of the mimetics reduce the IKKβ-NEMO interaction as well as or better than the NBD peptide , with SR12343 being the most effective . SR12343 also reduced the association between NEMO and IKKβ in Raw 264 . 7 cells in a dose-dependent manner ( Fig 5B ) . No interaction between NEMO and TNF receptor-associated factor 2 ( TRAF2 ) or IκBα was observed under these conditions . Previous studies suggested that the NBD peptide also inhibits the interaction of NEMO with IKKα [27] . However , SR12343 had only a marginal effect on NEMO/IKKα binding only at the highest dose , suggesting that these inhibitors affect the NEMO/IKKα interaction with a much lower efficiency ( Fig 5B ) . To demonstrate that SR12343—which was the most effective mimetic in disrupting the NEMO/IKKβ interaction in vivo—affects the NEMO/IKKβ interaction directly , in vitro glutathione S-transferase ( GST ) pull-down assays were performed using recombinant GST-NEMO and FLAG-IKKβ . As shown in Fig 5C , SR12343 was able to disrupt the interaction between GST-NEMO and FLAG-IKKβ even at a dose of 12 . 5 μM . To demonstrate that the reduction of NF-κB-mediated transcription upon stimulation of TNF-α and LPS ( Fig 4 ) is not due to off-target effects , the activation of NF-κB signaling and mitogen-activated protein kinase ( MAPK ) pathways was examined by western blot analysis . The levels of phosphorylated IKK complex , IκBα , and p65 in response to TNF-α and LPS were all reduced by SR12343 ( Fig 5D & 5E ) . Consistently , the degradation of IκBα was partially reduced ( Fig 5D & 5E ) . However , there were no changes in the levels of phospho-c-Jun N-terminal kinase ( p-JNK ) , p-p38MAPK , total JNK , and p38MAPK by treatment with SR12343 ( 150 μM ) in response to TNF-α or LPS stimulation ( Fig 5G & 5H ) . These results suggest that the observed inhibitory effects of SR12343 are mediated directly through IKK/NF-κB but not due to off-target effects such as through the JNK or p38MAPK pathways . SR12343 treatment also had no effect on the activation of noncanonical NF-κB pathway by anti-lymphotoxin β receptor ( LTβR ) as shown by unaltered processing from p100 to p52 ( Fig 5F ) . To determine the stability of the NBD mimetics in vivo , their levels were measured in the plasma of mice 2 h post i . p . injection with 10 mg/kg of each compound . The concentration of SR12460 was very high in plasma ( >6 . 5 μg/mL; 20 μM ) , while levels in brain , muscle , spleen , and liver were lower . SR12343 and SR12454 had lower plasma concentrations , with SR12343 showing a higher level in liver and SR12454 showing higher concentrations in muscle and spleen . The level of SR11481 was undetectable in plasma and tissues ( Fig 6A ) . Because SR12454 and SR12460 share similar chemical structures and SR11481 demonstrates poor in vivo exposure , SR12460 and SR12343 were selected for further in vivo analysis . However , it is important to note that SR12343 was not as orally active as SR123460 ( S1B Fig ) . To determine their NF-κB inhibitory effects in vivo , SR12343 and SR12460 were tested in an acute model of LPS-induced systemic endotoxemia . C57BL/6J mice were pretreated with vehicle control , 8K-NBD peptide , or NBD mimetics at 10 mg/kg for 30 min , followed by LPS induction at 10 mg/kg ( Fig 6B ) . Lung and liver were harvested 2–4 h post treatment for qRT-PCR analysis of NF-κB target genes . SR12343 was able to significantly inhibit NF-κB transcriptional activity in lung , demonstrated by the inhibition of iNOS , IκBα , COX-2 , and IL-6 , while expression of TNF-α was unchanged ( Fig 6C ) . The NF-κB inhibitory effects of the compounds were less effective in liver compared to lung , demonstrated by significant inhibition of only COX-2 expression ( Fig 6C ) . Despite lower plasma and tissue concentrations of SR12343 compared to SR12460 , its inhibition of NF-κB/IKK in liver and lung were greater than that of SR12460 ( Fig 6C ) . Similarly , acute treatment with SR12343 also affected the LPS-induced expression of NF-κB target genes at protein level . The extent of phosphorylation of IκBα and the levels of COX-2 were reduced in lung and liver tissues treated with SR12343 ( Fig 6D and 6E ) . Serum levels of IL-6 detected by ELISA increased significantly following LPS induction and were reduced by acute treatment with SR12343 ( Fig 6F ) . These results are consistent with our RT-PCR analysis shown in Fig 6C . However , no significant differences in lung histopathology were observed . Finally , there was a slight reduction in the number of white blood cells and neutrophils in the SR12343-treated group ( Table 2 ) . Taken together , the results demonstrate that SR12343 and SR12460 are effective at attenuating LPS-induced acute lung inflammation by suppressing NF-κB target gene expression . Because SR12343 and SR12460 both reduced LPS-induced NF-κB activation in vivo , they were further tested in mdx mice , a mouse model of DMD in which NF-κB is chronically activated [28] . Mdx mice develop normally at birth and undergo a massive myonecrosis starting at 3 wk . Treatment of mdx mice with IKK/NF-κB inhibitors effectively reduces inflammation , blocks necrosis , and increases muscle regeneration . Initially , the effect of acute treatment of SR12343 on NF-κB DNA binding activity in tibialis anterior ( TA ) muscle in 9-wk-old mdx mice was examined by EMSA . Treatment with a single injection of 30 mg/kg of SR12343 resulted in a reduction in NF-κB DNA binding 2 h post injection ( S4C Fig ) . Subsequently , Mdx mice were chronically treated with vehicle , SR12343 ( 30 mg/kg ) , SR12460 ( 30 mg/kg ) , or 8K-NBD ( 10 mg/kg ) starting from day 21 , 3 times/wk for 4 wk , similar to the dosing regimen used with the NBD peptide ( Fig 7A ) [7] . No significant weight loss was observed in chronically treated mdx mice ( S3A Fig ) . In addition , there was no increase in levels of aspartate aminotransferase ( AST ) , alanine aminotransferase ( ALT ) , or alkaline phosphatase ( ALP ) in chronically treated mdx mice ( S3B Fig ) , suggesting that treatment with SR12343 and SR12460 had no overt liver toxicity . To determine whether SR12343 and SR12460 improve muscle pathology , TA muscle was stained with hematoxylin–eosin to assess inflammatory infiltration , necrosis , central nucleation , and fibrosis . Vehicle-treated TA muscles exhibited extensive infiltration and necrosis as reflected by clusters of inflammatory cells , disorganized myofibers , and nonuniform staining , with limited muscle regeneration . Consistent with previous studies , 8K-NBD peptide treatment reduced inflammatory cell infiltration and necrosis as shown in Fig 7B & 7C , while not significantly affecting the percentage of centralized myonuclei ( S4A Fig ) [29 , 30] . SR12343 treatment led to the most significant pathological improvement , represented by limited infiltration and enhanced muscle reconstruction ( Fig 7B & 7C and S4B Fig ) . Similarly , SR12460 treatment improved muscle pathology , although not as effectively as SR12343 . In addition , Masson trichrome stain was conducted to further measure muscle fibrosis . Chronic treatment with SR12343 reduced muscle fibrosis ( blue ) in diaphragm and TA muscle tissues in comparison to control group ( Fig 8A & 8B ) . To further quantitate the extent of inflammation in mdx muscle tissues , diaphragm and TA muscle were immunostained for cluster of differentiation 68 ( CD68 ) , a macrophage marker . Treatment with SR12343 reduced the number of CD68+ cells by almost 50% per myofiber in both diaphragm and TA tissues ( Fig 8A & 8B ) . Notably , there is a higher level of macrophage infiltration in diaphragm in comparison to TA , as reflected by a greater number of CD68+ cells per myofiber . This result is consistent with the Trichrome stain in which the diaphragm exhibited more severe muscle fibrosis compared to TA . Moreover , qRT-PCR analysis revealed significant improvement of myofiber regeneration in SR12343-treated TA muscle relative to vehicle controls , evidenced by increased expression of embryonic myosin heavy chain ( eMyHC ) as well as paired box protein 7 ( Pax7 ) , a marker of skeletal muscle satellite cells ( Fig 7D ) . SR12460 and the 8K-NBD peptide also increased eMyHC expression , but to a lesser extent than SR12343 . To quantify the average fiber size in an unbiased way , TA muscle was stained with laminin to outline myofibers , and Feret diameter of all myocytes was quantitated in a whole muscle section . Fiber size of centrally and noncentrally nucleated myofibers was smaller in mice treated with SR12343 and the 8K-NBD peptide , suggesting an active reconstruction of myofibers during the regenerative phase ( Fig 7E & 7F ) . To determine whether the NBD mimetics improved muscle strength , grip strength was measured 2 wk and 4 wk post treatment to assess forelimb strength ( Fig 7G ) . Compared to vehicle group , SR12343 significantly improved forelimb strength after 2 wk of treatment , indicating rapid muscle repair . All treatment groups , compared to control , displayed significantly strengthened forelimb strength 4 wk post treatment ( Fig 7G ) . Collectively , the 2 lead compounds—SR12460 and , in particular , SR12343—markedly improved muscle function and muscular pathology in mdx mice .
The NBD peptide derived from the NBD in IKKβ is highly therapeutic in numerous mouse and canine models of inflammatory and degenerative diseases [6–9 , 12 , 13 , 17 , 18] . In fact , the NBD peptide is far more effective as a therapeutic than small-molecule inhibitors of the active site of IKKβ kinase . However , synthesis of the NBD peptide on a large scale can be problematic and extremely expensive , which impedes its clinical utility in humans , for whom a larger dose is needed compared to rodents and dogs . Also , the NBD peptide is not orally active . Thus , the development of small-molecule NBD mimetics represents a significant advance towards clinical translation of this established molecular target . Here , we identified and optimized several novel NBD mimetics that selectively inhibited IKK/NF-κB activation . The 2 lead compounds , SR12343 and SR12460 , inhibited both TNF-α- and LPS-induced NF-κB activation more efficiently and at a lower concentration than the NBD peptide in both HEK293 and Raw 264 . 7 cells . To identify the NBD mimetics , a computational screening campaign based on the pharmacophore model was used [21] . This structure-based pharmacophore model used has been utilized successfully to identify small-molecule inhibitors of p53 up-regulated modulator of apoptosis ( PUMA ) targeting its binding with B cell lymphoma 2 ( Bcl-2 ) family members through the BH3 domain [31] . A crystal structure study of the NBD-IKKβ interaction demonstrated that amino acid residues W741 , W739 , and F734 are essential IKKβ hydrophobic motifs for dynamically interacting with NEMO [21] . Furthermore , a comprehensive analysis of binding energy hot spots revealed 3 regions critical for the protein–protein interaction interface between NEMO and IKKβ: the documented NBD regions ( W739 , W741 , and L742 ) and 2 novel hot spot regions centered on IKKβ residues L708/V709 and L719/I723 [32] . Consistently , a mutated 11-mer NBD peptide ( 735–745 ) —with substitutions of arginine for W741 and W739—was unable to bind to NEMO [27] . A longer IKKβ-derived peptide ( 701–746 ) containing all 3 residues and domains exhibited the strongest affinity to NEMO , with IC50 around 10 nM , compared to the traditional 11-mer NBD peptide that was less potent , with IC50 around 100 μM [21 , 32–34] . To identify NBD mimetics with greater bioactivity , F734 not contained within the 11–amino acid NBD peptide was included in our pharmacophore model and in silico screening . The novel NBD mimetics identified and optimized were able to inhibit NF-κB activity in a dose-dependent manner ( IC50 approximately 10–40 μM ) and were more potent than the 11-mer NBD peptide [21 , 33] . These results are consistent with other results suggesting that , although IKKβ737–742 is the core component essential for IKK complex formation , the larger region spanning IKKβ701–746 enhances the binding affinity between IKKβ and NEMO [32 , 34 , 35] . The identified and optimized NBD mimetic SR12343 was able to dissociate the NEMO/IKKβ complexes both in vitro and in vivo ( Fig 5 ) , consistent with previous reports that NBD peptide blocks the association of preformed IKK complexes in inactive state [27 , 33] . Also , although the NBD peptide has been reported to block interaction of NEMO with IKKα and reduce IL-1-induced , IKKα-dependent NF-κB activation in mouse embryonic fibroblasts ( MEFs ) [27 , 36] , SR12343 disrupted the binding of NEMO and IKKα much less effectively . This specificity for NEMO/IKKβ could be due to the fact that there is a methionine in IKKα at position 734 instead of a phenylalanine , while F734 along with W739 and W741 was included for the generation of the pharmocophore model . Our results also demonstrated that the SR12343 suppressed TNF-α- and LPS-induced NF-κB activity by inhibiting the phosphorylation of IKK , IκBα , and p65 while not affecting JNK and p38MAPK signaling ( Fig 5 ) . It appears that SR12343 has a more substantial inhibition in LPS-induced NF-κB activation in comparison to TNF-α stimulation . This is possibly due to a stronger secondary NF-κB activation upon TNF-α stimulation by up-regulating the expression of multiple pro-inflammatory factors . MAPK signaling can be stimulated by similar pro-inflammatory factors , such as TNF-α and LPS , increasing expression of pro-inflammatory genes via AP-1 and NFAT [1 , 37] . In addition , SR12343 did not affect the noncanonical NF-κB pathway signaling through the LTβR ( Fig 5F ) . NBD mimetics inhibited a unique subset of target genes compared to IKK kinase inhibitors , particularly iNOS both in vitro and in vivo . The expression of iNOS in response to LPS can be down-regulated 6-fold in Raw 264 . 7 cells and by 50% in both lung and liver , by the treatment of mice with NBD mimetics . iNOS is involved in arginine metabolism , leading to the production of citrulline and nitric oxide ( NO ) , the latter of which—acting as a free radical—promotes cytotoxicity and tissue injury [38] . iNOS-null mdx mice have significantly reduced macrophage cytolysis and decreased myofiber injury at both acute , necrotic phase ( 4 wk ) and regenerative phase ( 6–12 wk ) , suggesting a critical role of NO-mediated myonecrosis in the pathology of mdx mice [38] . We demonstrated a reduction in necrosis , fibrosis , and inflammatory infiltration in both the diaphragm and TA muscle in treated mdx mice—in particular , SR12343-treated mdx mice—compared to controls . Also , similar to mdx mice treated with NBD peptide , treatment with the NBD mimetics—SR12343 , in particular—resulted in increased myogenesis as shown by higher expression of eMyHC and Pax7 and the smaller size of centrally nucleated fibers ( Fig 6 ) . This is consistent with the observed ability of NF-κB to regulate cell differentiation via its transcriptional regulation of cyclin D1 [39] . Taken together , our results suggest that small-molecule NBD mimetics can simultaneously inhibit pro-inflammatory responses , reduce macrophage cytotoxicity , and improve muscle degeneration , with an even greater efficacy than NBD peptide . No overt signs of liver toxicity from the chronically treated mice were observed , based on serum clinical chemistries . We previously demonstrated that chronic treatment of the Ercc1−/Δ mouse model of accelerated aging with the NBD peptide delayed the onset of numerous age-related symptoms , improved pathology , and reduced cellular senescence . Similar to the NBD peptide , preliminary experiments suggest that chronic treatment of Ercc1−/Δ mice with SR12343 resulted in an extended health span . Thus , these novel NBD mimetics could be used not only for treatment of inflammatory and degenerative diseases but also for aging . Collectively , our data demonstrate that the novel small-molecule NBD mimetics are potent and highly selective IKK inhibitors that act by disrupting the association of IKK complexes . They exhibit significant inhibitory effects on NF-κB activation in the model of LPS-induced acute lung injury ( ALI ) and the murine model of DMD ( mdx mice ) , suggesting the potential of NBD mimetics to become a distinct class of anti-inflammatory drugs . Taken together , NBD mimetics may provide therapeutic value for the chronic management of inflammatory diseases and cancers in the future .
The animal studies were reviewed and approved by the Scripps Florida Institutional Animal Care and Use Committee ( protocols #15–017 and 15–020 ) and were in compliance with the U . S . HHS Guide for the Care and Use of Laboratory Animals . The day-to-day care of animals was managed by the Scripps Florida Animal Research Center . Scripps Florida has an Animal Welfare Assurance on file with the Office of Laboratory Animal Welfare ( OLAW ) , National Institutes of Health . The assurance number is #A4460-01 , effective January 30 , 2009 . Scripps Florida’s registration under USDA regulations is certificate 93-R0015 , effective December 5 , 2005 . The Association and Accreditation of Laboratory Animal Care International ( AAALAC ) awarded Scripps Florida full accreditation on June 25 , 2008 , and continuation was awarded on November 9 , 2011 . Mice were euthanized at 7 wk of age by carbon dioxide inhalation . HEK293 cells and MEFs were grown in Dulbecco’s Modified Eagle Medium ( with 4 . 5 g/L glucose and L-glutamine ) , supplemented with 10% fetal bovine serum , penicillin , and streptomycin . Raw 264 . 7 cells were cultured in RPMI-1640 media containing 10% heat-inactivated fetal bovine serum , penicillin , and streptomycin . C57BL/10ScSn-Dmdmdx/J and female C57BL/6 mice were purchased from the Jackson Laboratory . Mice were housed in the animal facilities of Scripps Florida under constant temperature and humidity . Animal protocols used in this study were approved by Scripps Florida Institutional Animal Care and Use Committee . Three-wk-old sex-matched mdx mice were dosed with SR12343 ( 30 mg/kg ) , SR12460 ( 30 mg/kg ) , 8K-NBD peptide ( 10 mg/kg ) , or vehicle by i . p . injection 3 times per wk for 4 wk . Mice were euthanized at 7 wk of age by carbon dioxide inhalation , and TA muscle was collected for histological analysis . 8K-NBD ( KKKKKKKKGGTALDWSWLQTE ) peptide was synthesized at the peptide core facility of the University of Pittsburgh . For i . p . injections , the peptide was dissolved in 10% DMSO in PBS . The NBD mimetics were formulated in 10:10:80 of DMSO:Tween 80:Water for in vivo administration . ZINC small molecules were purchased from Enamine . All stock solutions for in vitro experiments were prepared in DMSO at 40 μM . LPS ( strain O111:B4 ) was prepared in PBS at a sublethal dose of 10 mg/kg . Female WT mice 8 to 10 wk old ( 20–30 g ) were dosed by i . p . injection with vehicle , NBD peptide ( 10 mg/kg ) , or small molecules ( 10 mg/kg ) for 30 min , followed by i . p . injection of saline ( 1 mL/kg ) or LPS ( 10 mg/kg ) . Mice were euthanized 2 to 4 h post treatment , and lung and liver tissues were collected for further analysis . Seven-wk-old treated or untreated mdx mice were measured for forelimb grip strength by using a digital grip strength meter paired with a metal grid ( Bioseb , Vitrolles , France ) . Mice were allowed to grip the metal grid tightly , and readings were obtained by gently pulling the tail backward until release . Five sequential measurements were performed , and the average force was calculated . HEK293 cells stably transfected with luciferase reporter plasmid driven by NF-κB were seeded in 96-well plates in triplicate and pretreated with DMSO or varying small molecules at indicated concentration for 30 min , followed by the stimulation of 10 ng/ml of TNF-α for 3 h [25] . Cells were washed with PBS once and harvested in Passive Lysis Buffer ( Promega , Madison , WI ) . Luciferase assay ( Promega ) was performed by using a luminometer according to the manufacturer’s instructions . HEK293 cells grown in 10 cm plates were cotransfected with a coreporter of a Renilla plasmid driven by SV40 ( Promega ) and a firefly luciferase plasmid driven by NF-κB at the ratio of 1:3 with Lipofectamine 2000 ( Invitrogen , Carlsbad , CA ) . Transiently transfected HEK293 cells were grown and treated as described above and subjected to a dual-luciferase reporter assay according to the manufacturer’s instruction . Briefly , NF-κB firefly luciferase and Renilla luciferase activity were detected sequentially . The relative luciferase activity was calculated by normalizing NF-κB luciferase to Renilla luciferase activity . HEK293 cells were grown in a 96-well plate at 3 × 104 cells/well in triplicate and treated with DMSO or listed small molecules at indicated concentrations for 24 h . Cell survival was determined by adding 20 μl of 5 mg/ml MTT ( thiazolyl blue tetrazolium bromide ) to each well , followed by incubation in 37 °C for 3 h . Media were removed , and purple formazan was dissolved in 100 uL of DMSO . Absorbance was measured at 590 nm on a microplate reader ( PerkinElmer , Waltham , MA ) . Cell viability was calculated by normalizing values to untreated controls . Cell lysate was prepared in RIPA buffer ( 20 mM Tris-HCl [pH 7 . 5] , 150 mM NaCl , 1 mM Na2EDTA , 1 mM EGTA , 1% NP-40 , 1% sodium deoxycholate , 2 . 5 mM sodium pyrophosphate , 1 mM beta-glycerophosphate , 1 mM Na3VO4 , and 1 μg/ml leupeptin ) , supplemented with 1X protease inhibitor cocktails ( MilliporeSigma , St . Louis , MO ) and 1X Halt phosphatase inhibitor cocktail ( ThermoFisher , Waltham , MA ) . Protein concentrations were determined by Lowry protein assays , and 30 μg of protein was resolved by MINI-PROTEAN TGX 4%–15% SDS-PAGE gels . Blots were incubated in primary antibodies overnight at 4 °C and secondary antibodies at room temperature for 1 h . Reagents and antibodies used are as follows: recombinant murine TNF-α ( 10 ng/mL; PeproTech #315-01A ) , LPS from Escherichia coli , serotype O111:B4 ( 1 μg/mL; Enzo #ALX-581-012-L001 ) , recombinant murine IL-1α ( 0 . 5 ng/mL; PeproTech #211-11A ) , anti-LTβR antibody ( 1:300; Abcam #ab65089 ) , anti-p-IκBα ( 1:1000; CST ) , IκBα ( C-21 ) antibody ( 1:1000; #sc-371 ) , p-IKKα/β ( Ser176/180 ) antibody ( 1:500; CST #2697 ) , IKKα antibody ( 1:1000; CST #2682 ) , anti-IKKβ ( 1:1000; CST #8943 ) , p-p65 ( Ser536 ) ( 93H1 ) antibody ( 1:2000; CST #3033 ) , p65 ( D14E12 ) antibody ( 1:5000; CST #8242 ) , p100/p52 antibody ( 1:500; CST #4882 ) , p-TAK1 ( Thr184/187 ) antibody ( 1:500; CST #4508 ) , and anti-GAPDH ( 1:5000; CST #5174 ) . Raw 264 . 7 cells were seeded at 1 × 106 cells/well in 6-well plates or 6 × 106 cells/10 cm plate and pretreated with vehicle , small-molecule inhibitors , or NBD peptide for 30 min . Cells lysates were harvested in NP-40 lysis buffer supplemented with 1X protease inhibitor cocktails ( Sigma ) . Proteins were immunoprecipitated by incubating 150 μg of lysates ( 1 μg/μl ) with 10 μl of agarose-conjugated NEMO ( FL-419 ) antibody ( sc-8330 AC , Santa Cruz , Dallas , TX ) on a rotary shaker at 4 °C for 4 h . Alternatively , we incubated 500 μg protein lysates with 10 to 15 μl of NEMO ( FL-419 ) antibody ( Santa Cruz ) for 2 h at 4 °C , then added 50 μl Dynabeads ( ThermoFisher ) to the Ab-Ag complex and incubated on a rotator for 1 h at 4 °C . Ag-Ab-Bead complex was then washed with NP-40 buffer 3 times and PBS once . Protein was then denatured in SDS-sample buffer and resolved by MINI-PROTEAN TGX 4%–15% SDS-PAGE . Ten percent of cell lysates were probed as input controls for co-immunoprecipitation . Antibodies used are as follows: anti-IKKβ ( 1:1000; CST #8943 ) , anti-NEMO ( 1:1000; CST #2685 ) , TRAF2 ( C192 ) antibody ( 1:1000; CST #4724 ) , IKKα antibody ( 1:1000; CST #2682 ) , and IκBα ( C-21 ) antibody ( 1:1000; #sc-371 ) . Recombinant full-length NEMO tagged with GST at the N-terminus ( SignalChem , Richmond , British Columbia , Canada ) at 15 nM were preincubated with inhibitors for 15 min at RT , followed by incubation with 15 nM of recombinant full-length IKKβ tagged with FLAG at the C-terminus ( Origene ) for 30 min at 30 °C . TNT buffer ( 50 nM Tris [pH 7 . 5] , 200 mM NaCl , 1% Triton X-100 , and 1 mM DTT [pH 7 . 4] ) was used as binding and washing buffer . Binding products were then incubated with 25 μl of glutathione agarose beads ( ThermoFisher ) for 1 h at 4 °C on a rotator . Final products were washed extensively with TNT buffer 4 times before the addition of sample buffer . Samples were then resolved by SDS-PAGE gels . Antibodies used are as follows: GST antibody ( B-14 ) HRP ( 1:3000; #sc-138 HRP ) and monoclonal anti-FLAG M2 antibody ( 1:2000; Sigma-Aldrich , F3165 ) . Cytoplasmic and nuclear fractions were extracted using the NE-PER nuclear and cytoplasmic extraction reagents ( ThermoFisher ) according to the manufacturer’s instructions . The gel shift assay was performed by following the previously described method [39] . In brief , 5 ug nuclear extract was incubated with 5X gel shift binding buffer for 10 min at room temperature ( Promega ) . Next , an alpha-32P-deoxycytodine triphosphate-radiolabeled probe containing the consensus NF-κB binding sequence , at a concentration of 200 , 000 cpm/μL , was added to the reaction mix and incubated for 20 min at room temperature ( MP Biomedicals , Santa Ana , CA ) . The reaction product was separated on a 6% nondenaturing polyacrylamide gel , prior to autoradiographic imaging . The oligonucleotide sequences are as follows: NF-κB template oligo , 5′-CAGGGCTGGGGATTCCCCATCTCCACAGTTTCACTTC-3′; NF-κB annealing oligo , 5′-GAAGTGAAACTGTGG-3′ ( Integrated DNA Technologies , Coralville , IA ) . Snap-frozen tissues were preserved in RNAlater RNA stabilization solution ( ThermoFisher ) . Total RNA was extracted from cells or tissues by using TRIZOL reagent ( Life Technologies ) , and 1 , 500 ng of mRNA was subjected to synthesis of cDNA using SuperScript VILO cDNA synthesis kit . qRT-PCR was performed in a StepOnePlus Real-Time PCR system using Platinum SYBR Green qPCR SuperMix-UDG ( ThermoFisher ) . Target gene expression was calculated using the comparative CT method ( ΔΔCT ) by normalizing to an internal control gene Actb ( β-actin ) . Primers used are as follows: Ptgs2 ( COX-2 ) forward: ACTCATAGGAGAGACTATCAAG; Ptgs2 ( COX-2 ) reverse: GAGTGTGTTGAATTCAGAGG; Nfkbia ( IκBα ) forward: CAGAATTCACAGAGGATGAG; Nfkbia ( IκBα ) reverse: CATTCTTTTTGCCACTTTCC; Il1b ( IL-1β ) forward: GGATGATGATGATAACCTGC; Il1b ( IL-1β ) reverse: CATGGAGAATATCACTTGTTGG; Nos2 ( iNOS ) forward: TGAAATCCCTCCTGATCTTG; Nos2 ( iNOS ) reverse: CCATGTACCAACCATTGAAG; Tnf ( TNF ) forward: CTATGTCTCAGCCTCTTCTC; Tnf ( TNF ) reverse: CATTTGGGAACTTCTCATCC; Il6 ( IL-6 ) forward: AAGAAATGATGGATGCTACC; Il6 ( IL-6 ) reverse: GAGTTTCTGTATCTCTCTGAAG . Actb ( β-actin ) forward: GATGTATGAAGGCTTTGGTC; Actb ( β-actin ) reverse: TGTGCACTTTTATTGGTCTC . Raw 264 . 7 cells were grown in 96-well plates and pretreated with vehicle , IKKi VII ( 2 μM ) , and small molecules ( at indicated concentration ) for 1 h , followed by the stimulation with 1 μg/ml of LPS . Supernatant was collected 24 h later for ELISA analysis . IL-6 concentration was measured using a mouse IL-6 ELISA kit ( BD ) according to the manufacturer’s instructions . The levels of TNF-α and IL-6 in mouse serum were detected using the Mouse TNF-α ELISA Kit ( BD Biosciences , Franklin Lakes , NJ ) and Mouse IL-6 ELISA Kit ( BD Biosciences ) per manufacturer’s specifications . The absorbance was quantified at 450 nm using a Spectramax i3 ( Molecular Devices , San Jose , CA ) plate reader . All standards and samples were measured in duplicate . Tissues fixed in 10% neutral buffered formalin ( NBF ) overnight were embedded in paraffin . Tissue was sectioned at 5 μm using a microtome . Hematoxylin–eosin staining was conducted following a standard protocol [30] . Masson trichrome stain was performed with the Masson Modified IMEB Trichrome staining kit ( IMEB , San Marcos , CA ) , as instructed by the manufacturer’s protocol , which stains collagen blue , muscle fibers red , and nuclei black . Briefly , the frozen tissue sections were fixed with 10% formalin and subsequently incubated in iodine solution , hematoxylin solution , Biebert’s Scarlet Acid Fuschin solution , Phospotungistic Phosphomolybdic Acid Solution , and Aniline Blue Stain Solution . Slides were then rinsed , dehydrated , and mounted for imaging . To ensure rigorous quantitation of average fiber size , TA muscle was stained with laminin to outline myofibers . Using NIS Elements ( Nikon , Melville , NY ) analysis software , the laminin signal was corrected for background and underwent signal homogenization and structure amplification by utilizing a local contrast and kernel-based smoothing algorithm . Muscle fibers were defined by applying a uniform binary mask to the negative space of the laminin image , then segmented with the use of a Boolean operator to isolate only negative space overlapping with the total tissue area present in the image . Nuclei defined by Hoechst labeling ( Sigma bisBenzimide H 33258 ) was defined using a uniform binary mask , and segmentation was performed with a watershed algorithm . Centrally nucleated fibers were determined by first eroding the binary mask of the muscle fibers so that only the central portion of each fiber was present . A Boolean operator was used to determine whether the eroded fibers overlapped with the nuclear-associated mask , and if so , they were considered to be centrally nucleated . Using the original muscle fiber mask , Feret diameters for centrally nucleated and noncentrally nucleated fibers were then measured . The frozen tissue sections were fixed with 4% paraformaldehyde for 1 h and incubated with 1% BSA for blocking . The primary antibodies CD68 ( Abcam , Cambridge , MA ) and utrophin ( Santa Cruz ) were applied at 1:200 for 2 h . The secondary antibodies Alexa Fluor 594 and Alexa Fluor 488 were then applied at 1:400 for 1 h . DAPI ( 1;1000 ) solution was used to stain the cell nucleus . All the processes were performed at room temperature . Nine mice ( male; C57BL/6 , 9–13 wk ) were randomly divided into 3 groups with 3 mice in each group . Mice were injected i . p . with vehicle ( DMSO:Tween80:H2O , 10:10:80 ) or small-molecule SR12343 ( 30 mg/kg ) for 30 min and then injected i . p . with saline ( 1 ml/kg ) or LPS ( 10 mg/kg in saline ) . After 2 h , mice were euthanized by CO2 asphyxiation prior to blood sampling via cardiac puncture . The blood from each mouse was put into 2 collection tubes with coagulant or anticoagulant heparin ( approximately 0 . 6 mL each ) . The samples in the tubes coated with the anticoagulant heparin were hand-mixed several times and placed on wet ice for hematological analysis using an automatic hematology analyzer ( Hemavet 950FS; Drew Scientific , Miami Lakes , FL ) . Samples in tubes containing no anticoagulant were allowed to clot before centrifugation and submission for serum biochemical analysis using the VetScan VS2 Analyzer with the Comprehensive Diagnostic Profile reagent rotor ( Abaxis , Union City , CA ) . The ALT , AST , and ALT activities in the serum were quantitated by a colorimetric , enzymatic method using the Clinical Chemistry Analyzer Cobas c311 ( Roche Diagnostics , Risch-Rotkreuz , Switzerland ) as per the manufacturer’s instructions . The pharmacokinetic profile of the NBD mimetics was determined in male C57BL/6J mice ( n = 3 ) . The drugs were formulated in 10:10:80 of DMSO:Tween 80:water and were dosed by i . p . injection at a final dose of 10 mg/kg . Blood , brain , muscle , spleen , and liver were collected 2 h post treatment and were analyzed by mass spectrometry by following a protocol previously described [40] . X-ray structure of the complex NEMO/IKKβ retrieved from the PDB ( ID 3BRV ) was used to generate a structure-based pharmacophore model [21] . The three-dimensional ( 3D ) pharmacophore model was created with LigandScout [22 , 23] and was based on interactions that define the protein–protein interaction , such as hydrophobic interactions , hydrogen bonding , and electrostatic interactions . Features identified by the LigandScout software are those that take into consideration chemical functionality but not strict structural topology or definite functional groups . As a result , completely new potential pharmacons can be identified through database screening . Moreover , to increase the selectivity , the LigandScout model includes spatial information regarding areas inaccessible to any potential ligand , thus reflecting possible steric restrictions . In particular , excluded volume spheres placed in positions that are sterically not allowed are automatically added to the generated pharmacophore model . In this way , the structure-derived pharmacophore model contains the pharmacophore elements of the candidate ligands in response to the protein’s active site environment [41] . Recognition of small molecules by proteins is largely mediated by molecular surface complementarities . Structure-based drug design approaches use this as the fundamental guiding principle; that is , closely related molecules will elicit similar activity in a biological assay [42 , 43] . The morphological similarity is a similarity technique dependent only on surface shape and charge characteristics of ligands [44] . Morphological similarity is defined as a Gaussian function of the differences in the molecular surface distances of 2 molecules at weighted observation points on a uniform grid . The computed surface distances include both distances to the nearest atomic surface and distances to donor and acceptor surfaces . This function is dependent on the relative alignment of the molecules , and consequently their alignment and conformation must be optimized . The conformational optimization problem is solved by fragmentation , conformational search , alignment , and scoring , followed by incremental reconstruction from high-scoring aligned fragments . The alignment problem is addressed by exploiting the fact that 2 unaligned molecules or molecular fragments that have some degree of similarity will have some corresponding set of observers that are seeing the same things . Optimization of the similarity of 2 unaligned molecules or molecular fragments is performed by finding similar sets of observers of each molecule that form triangles of the same size [41 , 44] . Computational modeling tools were used to estimate the bioavailability , aqueous solubility , blood brain barrier potential , human intestinal absorption , the cytochrome P450 ( i . e . , CYP2D6 ) enzyme inhibition potential , mutagenicity , and hERG inhibition of the hits obtained from the database screening . The bioavailability , aqueous solubility , and human intestinal absorption were estimated using the ACD/ADME Boxes software ( ACD Labs , Toronto , Canada; http://www . acdlabs . com ) , while mutagenicity , hERG , and CYP2D6 inhibition were estimated with ACD/Tox screening ( ACD Labs , Toronto , Canada; http://www . acdlabs . com ) . All values were presented as mean +/− SEM or mean +/− SD . Microsoft Excel and Graphpad Prism 6 were used for statistical analysis . Two-tailed Student t test was performed to determine differences between 2 groups . When comparing differences in more than 2 groups , 1-way ANOVA ( Dunnett test ) was conducted . A value of P < 0 . 05 was considered statistically significant , shown as “*” for P <0 . 05 , “**” for P <0 . 01 , and “***” for P < 0 . 001 .
|
Aberrant up-regulation of the transcription factor nuclear factor κB ( NF-κB ) and the IκB kinase ( IKK ) that regulates NF-κB is associated with a variety of inflammatory and degenerative diseases in humans , including aging . Thus , development of effective and specific drugs able to decrease IKK/NF-κB activity has significant therapeutic potential . In this study , a structure-derived computational approach was used to screen for small-molecule inhibitors of the protein–protein interaction between the IKKß and IKKγ subunits of the IKK complex . We identified and developed a novel class of small molecules that selectively inhibit IKK/NF-κB activation by dissociating the IKK complex without affecting c-Jun N-terminal kinase ( JNK ) /p38-mitogen-activated protein kinase ( MAPK ) signaling . These novel molecules reduce lipopolysaccharide ( LPS ) -induced acute inflammation in mice and improve muscle pathology in the mdx mouse model of Duchenne muscular dystrophy ( DMD ) , suggesting that they would have potential clinical utility .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
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] |
2018
|
Development of novel NEMO-binding domain mimetics for inhibiting IKK/NF-κB activation
|
Johne's disease ( JD ) , a persistent and slow progressing infection of ruminants such as cows and sheep , is caused by slow replicating bacilli Mycobacterium avium subspecies paratuberculosis ( MAP ) infecting macrophages in the gut . Infected animals initially mount a cell-mediated CD4 T cell response against MAP which is characterized by the production of interferon ( Th1 response ) . Over time , Th1 response diminishes in most animals and antibody response to MAP antigens becomes dominant ( Th2 response ) . The switch from Th1 to Th2 response occurs concomitantly with disease progression and shedding of the bacteria in feces . Mechanisms controlling this Th1/Th2 switch remain poorly understood . Because Th1 and Th2 responses are known to cross-inhibit each other , it is unclear why initially strong Th1 response is lost over time . Using a novel mathematical model of the immune response to MAP infection we show that the ability of extracellular bacteria to persist outside of macrophages naturally leads to switch of the cellular response to antibody production . Several additional mechanisms may also contribute to the timing of the Th1/Th2 switch including the rate of proliferation of Th1/Th2 responses at the site of infection , efficiency at which immune responses cross-inhibit each other , and the rate at which Th1 response becomes exhausted over time . Our basic model reasonably well explains four different kinetic patterns of the Th1/Th2 responses in MAP-infected sheep by variability in the initial bacterial dose and the efficiency of the MAP-specific T cell responses . Taken together , our novel mathematical model identifies factors of bacterial and host origin that drive kinetics of the immune response to MAP and provides the basis for testing the impact of vaccination or early treatment on the duration of infection .
Mycobacterium avim subsp . paratuberculosis ( MAP ) infects intestine of ruminants ( e . g . , cattle and sheep ) and causes a chronic inflammatory disease called Johne's disease ( JD ) [1] , [2] . Due to reduction of milk production and early culling of diseased animals , JD causes a significant economic loss to animal industries [3] , [4] . MAP has also been suspected as a causative agent of Crohn's disease , an inflammatory bowel disease in human [5] . Infection of animals occurs mainly through ingestion of materials contaminated with MAP-containing feces [6] . After the ingestion MAP bacilli reach intestine of the animal , are taken up by M cells and enterocytes , and are engulfed by submucosal macrophages [7]–[10] . MAP survives in resting tissue macrophages by inhibiting phagosome maturation [11]–[15] . At late stages of JD , MAP-infected animals shed bacilli in their feces thereby completing the infection cycle . MAP infection follows a lengthy latent and sub-clinical period in which the infection is difficult to diagnose [1] , [2] . Current research efforts focus on developing tools that aide in early detection of the infection before the infected animals start shedding MAP into the environment [16] . Vaccines are available for ovine and bovine JD [17] . Although the vaccines reduce or delay clinical symptoms and shedding of MAP into feces , they do not prevent new infections [17] . The lack of progress in vaccine development is in part due to poor understanding of the nature of the protective immune response against MAP infection [18] . In this respect , experimental infections of animals with MAP have been carried out in several studies examining the kinetics of MAP-specific immune responses [18]–[20] . These studies demonstrated that animals with paucibacillary lesions ( at early stages of the infection ) are likely to express a cell mediated ( Th1-type ) immune response measured by the expression of IFN- [21] . This response is likely to be protective against intracellular pathogens since IFN- can induce intracellular killing of MAP by macrophages [22] , [23] , and infected animals with a dominant Th1 response have very few lesions [20] . At later stages of the infection animals with multi-bacillary lesions express predominantly a Th2-type immune response that is measured by the presence of MAP-specific IgG1 antibodies , production of which is driven by IL-4 or IL-10 producing CD4 T cells [18]–[20] . Although high levels of MAP-specific antibodies are detected in animals in late stages of the disease , these antibodies do not appear to be protective and may even be detrimental by increasing uptake of extracellular bacteria by macrophages [24] . Thus , experimental infections of sheep with MAP suggest a switch from dominance of the MAP-specific Th1 immune response in the early stages of the disease to a predominantly Th2 response at later stages of the disease ( Figure 1A ) . More detailed analysis of the kinetics of MAP-specific Th1 ( IFN- ) and Th2 ( antibody ) responses in experimentally infected sheep revealed that the majority of animals do not display the “classical” Th1/Th2 switch ( Figure 1B–D ) . Around 50% of infected animals have combined Th1/Th2 responses ( Figure 1B&C ) while the minority of animals ( 11% ) show only Th1 response ( Figure 1D ) . Reasons for such different patterns of the kinetics of Th1/Th2 responses are not well understood . Th1 and Th2 subsets of helper CD4 T cell responses are defined by a set of cytokines they secrete and transcription factors that drive the development of each subset . Both Th1 and Th2 effectors differentiate from naïve CD4 T cells depending on the type of cytokines in the environment and the stimulating antigen [25] , [26] . Interleukin 12 ( IL-12 ) , IFN- , and strong antigenic stimulation upregulate expression of a transcription factor T-bet in naïve CD4 T cells that in turn drives differentiation of T cells into Th1 effectors . IL-4 and weak antigenic stimulation upregulate expression of a transcription factor GATA-3 that in turn drives differentiation of naïve T cells into Th2 effectors . Differentiated effector T cells themselves also start producing cytokines . Th1 effectors produce proinflammatory cytokines such as tumor necrosis factor- ( TNF- ) and IFN- . These cytokines activate macrophages to kill intracellular bacteria [13] . Th2 effectors produce a different group of cytokines such as IL-4 , IL-5 , IL-6 , IL-10 , and IL-13 . Th2 effectors and these cytokines direct B cells to produce MAP-specific antibodies [26] . There is a competition between Th1 and Th2 responses as observed in in vitro experiments [27]–[29] . Cytokines produced by Th1 cells inhibit differentiation of naïve CD4 T cells into Th2 cells and vice versa [30] . Mathematical modelling has influenced the current understanding of Th1/Th2 cell differentiation [31]–[36] . These models can be divided into three categories ( i ) models that describe different T cell phenotypes induced by transcription factors that govern the molecular mechanism for lineage selection and maintenance [31]–[33] , [37]–[39] , ( ii ) differentiation of naïve T cells into a mixed population of Th1 and Th2 effectors in response to cytokines induced by antigen-presenting cells and each T cell subset [34]–[36] , [40] , [41] , and ( iii ) regulatory network reconstruction with a repertoire of molecular and cellular factors that control Th cell differentiation [42]–[45] . As far as we know , only limited modelling work has been done on dynamics of Th1/Th2 responses to specific pathogens , such as viruses ( e . g . , human immunodeficiency virus ) and mycobacterial pathogens ( e . g . , Mycobacterium tuberculosis ) . Most of mathematical models did not specify pathogen to simulate differentiation of naïve CD4 T cells into different Th cell subsets [31]–[36] , [38] , [41] , [45] . The switch from a Th1 to a Th2 immune response in MAP-infected animals often occurs together with signs of clinical disease [20] , [21] . Mechanisms underlying this switch are still poorly understood , in particular it is unclear 1 ) which factors contribute to the timing of the switch , 2 ) whether the timing of the switch can be regulated , and 3 ) whether the switch is the driver of infection to the clinical stage or it is just a consequence of the progression to clinical disease . To address these questions , we developed a mathematical model of the immune response to MAP infection . We use this model to understand and identify conditions under which switch from Th1 to Th2 immune response occurs during MAP infection . We specifically consider two hypotheses: 1 ) switch is driven by accumulation of extracellular bacteria that in turn skew differentiation towards the Th2 response , and 2 ) switch is caused by exhaustion/suppression of Th1 response and concomitant rise of Th2 response . We investigate the conditions under which these mathematical models give rise to the Th1 to Th2 switch . We show that the following factors strongly influence Th1/Th2 switching dynamics: the mechanism by which MAP-specific Th cells are maintained at the site of infection ( continuous differentiation from naïve T cells or local proliferation ) , rate at which Th1 response is exhausted , longevity of extracellular bacteria , and the efficiency at which immune responses cross regulate each other .
To study factors that may contribute to the dynamics of MAP and MAP-specific Th1 and Th2 responses we propose a novel mathematical model . The model is based on the current biological understanding of basic properties of mycobacterial infections [20] , [21] , [46] , and experimental and theoretical understanding of Th1 and Th2 effector differentiation from naïve T cells [28] , [30] , [33] , [38] ( see introduction ) . The model includes interactions between extracellular MAP bacteria ( ) , macrophages ( ) ( target cells ) , naïve CD4 T cells ( ) , and the two subsets of the MAP-specific immune response , Th1 ( ) and Th2 ( ) cells ( Figure 2 ) . Infection is initiated by extracellular bacteria at the dose . Macrophages internalise extracellular bacteria and get infected at a rate giving rise to infected macrophages ( ) . There is still uncertainty in the literature on how macrophages are maintained at local sites such as the gut [47] . In the model we assume that during infection , macrophages are supplied from progenitor monocytes that are recruited from the blood to the site of infection at a rate . Infected macrophages burst at a rate releasing bacteria into the extracellular environment . Th1 effectors remove infected macrophages at a rate and intracellular bacteria are killed in this process . Effector CD4 T cells activate macrophages to kill intracellular bacteria and help with the generation of the MAP-specific CD8 T cell response which in turn clears infected macrophages [48] . Extracellular bacteria are cleared at a rate . Some extracellular bacteria are taken up by macrophages and are destroyed at a rate . Furthermore , given available experimental data [24] we assume that MAP-specific antibodies ( Th2 response ) are ineffective at eliminating extracellular bacteria . Uninfected and infected macrophages have death rates of and , respectively . Selective differentiation of naïve CD4 T cells into either Th1 or Th2 effectors is established during priming caused by interaction of major histocompatibility complex ( MHC ) -specific peptide complexes on antigen-presenting cells and T-cell receptors [25] , [26] . It is generally believed that Th1 responses are generated against intracellular pathogens such as viruses while Th2 responses are generated against extracellular pathogens such as extracellular bacteria [40] , [49]–[51] . Factors that influence priming and differentiation of CD4 T cells include the dose and type of antigen , co-stimulatory molecules and/or antigen presenting cells , and the cytokine environment present during priming [25] , [38] . In our model , MAP-specific naïve CD4 T cells ( Th0 ) are produced at a rate continuously from the thymus [52] and decay at rate . Th0 cells are recruited into the Th1 and Th2 immune responses at per capita rates and , respectively . Recruitment rates depend on the density of infected macrophages and extracellular bacteria . We make the simplest assumption that Th1 response is driven by the density of infected macrophages , and Th2 response is driven by the density of extracellular bacteria . Following recruitment , naïve CD4 T cells undergo a program of division and differentiation resulting in a large population of MAP-specific effectors . Therefore , in the model Th1 effectors are produced at a rate and Th2 effectors are produced at a rate where and are the parameters determining the magnitude of clonal expansion of the Th1 and Th2 responses , respectively . Th1 and Th2 effectors decay at rates and , respectively . With these assumptions ( Figure 2 ) the basic mathematical model is given by the following system of differential equations ( see the Supplemental Information ( Text S1 ) for the basic properties of the model and derivation of the basic model reproduction number , ) : ( 1 ) ( 2 ) ( 3 ) ( 4 ) ( 5 ) ( 6 ) Very little quantitative detail regarding MAP infection of ruminants is available , and therefore , most of the parameters of our mathematical model are unknown . Information on immunological responses to MAP infection was collected from different ruminant species when there was no strong contradictory findings among the species . Thus , our model is not for a particular ruminant . We used several different strategies to provide some estimates for the model parameters . Some parameters have been estimated from published experimental data ( Table 1 ) . Also , we have carried out literature search to obtain general acceptable ranges for other parameters . Well established biological knowledge was used to estimate cell populations in the sheep gut ( Table 2 ) . Where information about a parameter was not found , estimates were used that enable observation of cell kinetics within an acceptable range of the estimated cell population per in the gut . Yet , it still should be emphasized that most of our parameters are only educated guesses . Therefore , to determine which parameters in the model impact the most the time of the Th1 to Th2 switch we also performed standard parameter sensitivity analysis [53] .
From many experiments it is well known that Th1 and Th2 responses cross-inhibit each other [28] , [38] , [46] , [54]; in particular Th1 cytokines generally suppress differentiation of naïve CD4 T cells into Th2 effectors and vice versa [30] , [38] , [55] , [56] . However , most of the information on cross-inhibition comes from in vitro studies under strong polarising conditions and it remains unknown if such cross-inhibition also occurs during infections in vivo . In our basic mathematical model we neglected the possibility of cross-inhibition and investigated whether Th1/Th2 switch could be achieved if Th1 and Th2 responses are not directly cross-suppressive . Surprisingly , this model was able to predict accumulation and loss of the Th1 response and thus the switch from the dominant Th1 to dominant Th2 response during the infection ( Figure 3A ) . In the model , the phenomenon occurs due to the following steps . Due to high infectivity of free bacteria and a large population of resident macrophages , many macrophages become infected and very few extracellular bacteria exist . The large population of infected macrophages leads to generation of a Th1 response which , however , lacks the ability to eliminate the infection [57] , [58] . Infected macrophages produce new bacteria which in turn infect newly arriving macrophages . A quasi equilibrium is established . Because Th1 response is unable to clear extracellular bacteria and because in this simulation extracellular bacteria are relatively long lived ( Table 1 ) , extracellular bacteria accumulate over time . The increase in free bacteria then skews differentiation of Th0 cells toward Th2 phenotype , and this process indirectly suppresses generation of Th1 response which starts to decline over time . Therefore , the two assumptions are sufficient to drive the switch of the initially dominant cellular ( Th1 ) response to antibody ( Th2 ) production . These assumptions are 1 ) the generation of Th1 response is driven by density of infected macrophages while the generation of the Th2 response is driven by free bacteria ( see Eqns . ( 1 ) – ( 6 ) ) , and 2 ) extracellular bacteria are long lived . Indeed , increasing the death rate of extracellular bacteria ( ) effectively removes the Th1/Th2 switch whereby both responses are able to persist and Th1 response remains dominant ( Figure 3B ) . This occurs because if extracellular bacteria are cleared rapidly , density of the bacteria remains proportional to the density of macrophages and thus , Th2 response never outgrows the initially dominant Th1 response . Further insights into the dynamics of the Th1/Th2 switch can be obtained by calculating the dynamics of the ratio of the density of Th1 to Th2 response , ( see the Derivation of the Th1/Th2 ratio equation Section in the Supplemental Information ( Text S1 ) for the derivation of the equation for dynamics ) . The dynamics of the ratio in the model is given by ( 7 ) When under assumption of a quasi steady state for ( ) we find that , and therefore for the ratio to slowly change over time , the ratio of infected macrophages to free bacteria should change from a value more than one to a value less than one . This in general occurs when extracellular bacteria are long lived outside of macrophages [59] and the number of bacteria released per infected macrophage is relatively low [60] . However , Eqn . ( 7 ) also shows that if the decay rate of the Th1 response is much greater than that of Th2 cells ( i . e . , ) , the Th1/Th2 switch may still occur at high rates of clearance of extracellular bacteria ( results not shown ) . Experimental infections of sheep with MAP showed four different patterns of the immune response development: the so-called classical Th1/Th2 switch ( Figure 1A ) , delayed Th1/Th2 switch ( Figure 1B ) , a combined Th1/Th2 response ( Figure 1C ) and a Th1 only response ( Figure 1D ) [20] . We investigated whether our basic mathematical model can reproduce these experimental patterns . To compare model predictions with experimental data , we normalised the predicted Th1 and Th2 response by their maximum value reached in infection . Note that this is different from the Th1/Th2 dynamics shown in Figure 3 where cell populations are not normalised . To avoid over-fitting , we selected parameters for fitting the data predicted as most important for determining timing of Th1/Th2 switch using sensitivity analysis ( Figure S1 and the sensitivity analysis Sections in the Supplemental Information ( Text S1 ) ) . These include parameters that drive infection dynamics ( , ) and parameters that control differentiation and recruitment of effector T cells ( , , , and ) . The least squares method was employed using the patternsearch function in MATLAB . As our results show , the model can relatively well reproduce all major patterns of Th1/Th2 dynamics in MAP-infected sheep ( Figure 4 ) . The major parameters that determine the type of the response is the initial bacterial dose and parameters determining the kinetics of the immune response . Timing of the Th1 and Th2 switch was further investigated by varying the initial bacterial inoculation dose and the burst size of infected macrophages ( Figure 5 ) . Both of these parameters can be manipulated experimentally . Increasing the initial bacterial dose resulted in a faster switch , but a very large increase in the dose is needed to observe a noticeable decrease in the switch times ( Figure 5A&B ) . Increasing the dose size results in more macrophages being initially infected leading to a rapid depletion of uninfected macrophages and generation of the Th1 immune response . Rapid growth of the population of infected macrophages leads to accumulation of extracellular bacteria . Early and rapid growth of the bacterial population pushes for early Th2 immune response development , resulting in an early Th1/Th2 switch . Importantly , increasing the burst size is more effective at reducing the time of the Th1/Th2 switch than equivalent increase in the initial bacterial dose ( Figure 5C ) . Both of these predictions can be tested by infecting animals with different initial doses or with MAP strains that differ in virulence .
Progression of MAP infection in ruminants often occurs concomitantly with a switch from dominance of the MAP-specific Th1 immune response to a dominance of a Th2 response . Previous studies have shown that animals with paucibacillary lesions are likely to express a cell mediated , Th1-type , immune response that is protective against intracellular bacteria , while a Th2-type response is generally detected in animals with multi-bacillary lesions [18]–[20] . In this study , we have developed the first mathematical model of the helper T cell response to MAP and have analysed mechanisms that influence the dynamics of Th1 to Th2 switch during disease progression in MAP-infected animals . A number of interesting results emerged from the analysis of the model . First , the model is able to simulate two main infection outcome scenarios , ( i ) elimination of infection , this is associated with an initial strong Th1 immune response ( Th1 only response ) , ( ii ) infection persistence ( or latency ) , this is marked by both a Th1 and a Th2 response with high expression of a Th1 response over a Th2 response ( classical and delayed switch ) . Second , if the extracellular bacteria are not readily removed by the host's innate immune system , simulations show a Th1/Th2 switch which is characterized by accumulation of long-lived extracellular bacteria . Third and finally , the basic model was able to explain different patterns of the dynamics of MAP-specific Th1 and Th2 responses as was observed in experimental infections of sheep ( Figures 1 and 4 ) . These results from the basic model suggest that Th1/Th2 switch may be a result of disease progression rather than the cause . We find that in our basic mathematical model the longevity of extracellular bacteria is one of the key factors driving Th1/Th2 switch and disease progression in MAP-infected animals ( Figure 3 ) . Long survival of extracellular bacteria also naturally explains increased shedding of bacteria in animals with JD . Of note , extended models that assume a relatively short survival time of extracellular bacteria do not predict accumulation of MAP over the course of infection ( see Section Alternative models in the Supplemental Information ( Text S1 ) ) . Estimation of the average survival time of MAP in extracellular environment in the host will allow further refinement of our mathematical model . Such measurements may also suggest which additional mechanisms need to be involved to explain kinetics of Th1/Th2 responses in MAP-infected animals . Several other mechanisms may influence the likelihood and the kinetics of the Th1/Th2 switch including inhibition of Th0 cell differentiation by Th1 and Th2 responses , proliferation of effector T cells at the site of infection , and exhaustion of protective Th1 responses due to exposure to the antigen ( see Section Alternative models in the Supplemental Information ( Text S1 ) ) . The first two mechanisms , differentiation inhibition and local proliferation can dramatically alter the course of MAP-specific Th1 and Th2 responses depending on the strength of inhibition and sensitivity of Th1/Th2 cells to the local antigen concentrations . Therefore , future experimental studies should focus more on these dynamic processes and determine whether they occur in MAP-infected animals . Interestingly , we found that inhibition of effector functions of Th1 cells by Th2 response ( and vice versa ) did not influence the kinetics of the Th1/Th2 switch in the basic mathematical model when clearance rate of extracellular bacteria is high ( results not shown ) . In part , this is because in the basic model the Th1/Th2 switch is driven by the accumulation of extracellular bacteria and reduction of the efficacy of MAP-specific Th1 cells does not influence bacterial loads , and therefore , does not impact the kinetics of the Th1/Th2 switch . Although our mathematical model considers a number of important biological processes and illustrates the wealth of different scenarios for the dynamics of MAP-specific Th1 and Th2 responses , several potentially important biological details have been ignored in the model . First , we did not consider the dynamics of MAP-specific Th17 cells and regulatory T cells which are thought to play important role during intestinal infections in mice and humans [61] . There is , however , limited evidence that these subsets play a critical role in control of MAP replication but more experimental data in this area need to be collected . Secondly , in our mathematical model we ignored so-called “cellular plasticity” of effector CD4 T cell responses where effectors with a particular phenotype ( e . g . , Th1 ) may potentially convert into effectors of another phenotype ( e . g . , Th2 ) [62] . Exact mechanisms of how such cellular plasticity is regulated especially during chronic infections are still unknown . In our mathematical model we assumed only “population plasticity” where the change in the phenotype of MAP-specific T cells occurs due to differences in the rates of differentiation , proliferation and death at the site of infection [62] . Additional experimental studies will be needed to determine if conversion of protective MAP-specific Th1 cells into detrimental Th2 cells actually occurs during MAP infection . Thirdly , to keep the model simple , we only captured the role of Th1 cells ( CD4 T cells ) to represent the cell mediated response . There is evidence of accumulation of CD8 T cells in MAP infection [63] , which also secrete IFN- and can lyse infected macrophages . However , whether their involvement contribute significantly in the control of MAP is yet to be clearly demonstrated . Mycobacteria mainly resides within vacuoles of infected cells and therefore most of bacterial antigens are presented on MHC-II molecules ( which are recognised by CD4 T cells ) . It was postulated in the study of Chiodini and Davis [64] , that CD8+ T cells may be key in the development of the protective immunity through modulating the regulatory activity of T cells , although the exact mechanism of this cooperation was not presented . Also , in this study we did not include the role of M-cells and enterocytes , which are essential in the establishment of MAP infection as entry vehicles to pass the bacteria to professional phagocytes ( mucosal macrophages ) . There is potential to understand better the host-pathogen immune interactions using a spatial model that captures pathogen and immune response components ( both innate and adaptive ) at different locations and stages of disease . However , currently there is limited quantitative data to parameterise a spatial compartmentalised model . Furthermore , infection progression and priming of the adaptive immune response is mostly centred on macrophages , which ( i ) have the capacity to stimulate the immune response and ( ii ) harbor MAP for a long time period . Apart from phagocytic properties , macrophages also present antigens to CD4+ cells in the context of MHC-II molecules . Hence , the main modeling assumptions used in this current study and the choice of a simple model that does not include different compartments . Our study suggests that Th1/Th2 switch in MAP infection can be explained through ( i ) different regulation of Th cell differentiation , ( ii ) bacteria accumulation , ( iii ) proliferation and differentiation inhibition of T cells , and ( iv ) Th1 immune exhaustion . Some of these results are echoed in previous mathematical modeling studies . For instance , the studies [34] , [35] showed that when effectors fail to clear the antigen , the initially dominant Th1 response is lost and Th2 response arises . The studies [31] , [33] , [37]–[39] showed that cytokines that are present at the site of infection can influence the direction of naïve CD4 T cell differentiation , and therefore may determine the shift in the effector dominance . This mechanism was shown to be regulated by specific transcription factors such as T-bet , GATA-3 , FoxP3 and ROR-t which directs differentiation of naïve T cells into specific effector subsets [62] . It should be noted that previous mathematical models of Th1/Th2 regulation focused on cross regulation of Th1/Th2 cell responses based on cell to cell interactions via Th1/Th2 cytokines [34]–[36] , [41] . To the best of our knowledge our study is the first to model Th1/Th2 dynamics in MAP infection ( see the discussion for Th1/Th2 insights from previous mathematical studies ) . In summary , our JD immunology models indicated that the following factors can determine the timing of Th1/Th2 switch: ( i ) bacteria dose size and the burst size of infected macrophages , ( ii ) longevity of extracellular bacteria , ( iii ) degree of competition between Th1 and Th2 responses , and ( iv ) Th1 immune exhaustion . Testing these model predictions using more detailed experimental data that can be obtained from infecting animals with ( i ) MAP-strains of different virulence , ( ii ) different initial doses of MAP , and ( iii ) measuring the fraction of intracellular vs . extracellular MAP in tissues in vivo will help identify the mechanism controlling the kinetics of Th1/Th2 immune responses . JD is associated with slow disease progession in cattle , therefore experiments using small ruminants such as sheep and goats that are associted with relatively fast disease progression are recommended to make the experiments less expensive and to allow for reasonable time to collect the required data .
|
Mycobacterium avium subsp . paratuberculosis ( MAP ) is the causative agent of Johne's disease , a chronic enteric disease of ruminants such as sheep and cows . Due to early culling and reduction in milk production of affected animals , MAP inflicts high economic cost to diary farms . MAP infection has a long incubation period of several years , and during the asymptomatic stage a strong cellular ( T helper 1 ) immune response is thought to control MAP replication . Over time , Th1 response is lost and ineffective antibody response driven by Th2 cells becomes predominant . We develop the first mathematical model of helper T cell response to MAP infection to understand impact of various mechanisms on the dynamics of the switch from Th1 to Th2 response . Our results suggest that in contrast to the generally held belief , Th1/Th2 switch may be driven by the accumulation of long-lived extracellular bacteria , and therefore , may be the consequence of the disease progression of MAP-infected animals and not its cause . Our model highlights limitations of our current understanding of regulation of helper T cell responses during MAP infection and identifies areas for future experimental research .
|
[
"Abstract",
"Introduction",
"Model",
"Results",
"Discussion"
] |
[
"immune",
"cells",
"antigen-presenting",
"cells",
"immunology",
"host-pathogen",
"interaction",
"microbiology",
"mathematics",
"population",
"modeling",
"bacterial",
"pathogens",
"theoretical",
"biology",
"t",
"cells",
"infectious",
"disease",
"modeling",
"biology",
"pathogenesis",
"nonlinear",
"dynamics",
"immune",
"response",
"immune",
"system",
"systems",
"biology",
"antibody-producing",
"cells",
"b",
"cells",
"monocytes",
"computational",
"biology"
] |
2014
|
Competition for Antigen between Th1 and Th2 Responses Determines the Timing of the Immune Response Switch during Mycobaterium avium Subspecies paratuberulosis Infection in Ruminants
|
To develop new approaches to control HIV-1 replication , we examined the capacity of recently described small molecular modulators of RNA splicing for their effects on viral RNA metabolism . Of the drugs tested , digoxin was found to induce a dramatic inhibition of HIV-1 structural protein synthesis , a response due , in part , to reduced accumulation of the corresponding viral mRNAs . In addition , digoxin altered viral RNA splice site use , resulting in loss of the essential viral factor Rev . Digoxin induced changes in activity of the CLK family of SR protein kinases and modification of several SR proteins , including SRp20 and Tra2β , which could account for the effects observed . Consistent with this hypothesis , overexpression of SRp20 elicited changes in HIV-1 RNA processing similar to those observed with digoxin . Importantly , digoxin was also highly active against clinical strains of HIV-1 in vitro , validating this novel approach to treatment of this infection .
Current highly active anti-retroviral therapies ( HAARTs ) have successfully delayed the progression of HIV-1-infected individuals to AIDS by targeting viral entry and all HIV-1 enzymes [1] , [2] . However , the clinical application of ARTs is being affected by the spread of drug resistant viral strains [3] , [4] , [5]; detection of drug resistant forms of HIV-1 in newly infected patients has increased ∼3-fold from 2000 to 2007 to 16% [6] , [7] . To overcome these hurdles , more drugs with better profiles , and especially , novel mechanisms of action , are necessary for continued success in combating HIV-1 [1] , [2] , [8] . However , the majority of drugs currently undergoing clinical trials target the same enzymes/proteins for which drugs are already available [1] , [2] , [9] , [10] . In addition , the persistence of virus in reservoirs continues to be a challenge with standard HAART . There are at least 200 host factors required for HIV-1 infection and replication [11] , [12] , [13] . Efforts to understand the role of these factors in the lifecycle of HIV could aid development of future therapies . Among these are the factors regulating RNA processing . HIV-1 requires a balanced regulation of viral RNA processing to generate >40 mRNAs for synthesis of 15 viral proteins , an effect achieved through alternative splicing of a single 9 kb pre-mRNA transcript ( Fig . S1 ) [14] , [15] , [16] , [17] . HIV-1 RNA processing involves the combinatory use of four 5′ splice sites ( splice donors , SD1–4 ) and eight suboptimal 3′ splice sites ( splice acceptors , SA1–7; Fig . S1 ) . Use of 3′ splice sites ( ss ) is regulated by host trans-acting factors that function in an antagonistic fashion by binding to cis-acting elements adjacent to the 3'ss , either impeding ( hnRNPs ) or promoting ( SR proteins ) their use [14] , [16] , [18] , [19] , [20] . Three classes of HIV-1 mRNAs result from HIV-1 RNA splicing ( Fig . S1 ) : unspliced RNAs ( US ) encoding Gag or Gagpol proteins , singly spliced RNAs ( SS ) producing Env , Tat ( p14 ) , Vif , Vpr , or Vpu , and multiply spliced RNAs ( MS ) for synthesis of Rev , Tat ( p16 ) , or Nef [16] , [17] , [21] . Among these , Tat and Rev factors play central roles in HIV-1 replication; Tat activates transcription of all viral RNAs , while Rev transports the incompletely-spliced RNAs ( US , SS ) to the cytoplasm for translation [14] , [22] , [23] , [24] , [25] , [26] . Imbalances in RNA processing can dramatically affect viral replication [27] , [28] , [29]; undersplicing results in the loss of key regulatory proteins such as Tat and Rev ( from MS RNA ) , while oversplicing would reduce incompletely-spliced RNAs ( US , SS ) encoding viral structural proteins ( Gag , Env ) and accessory factors ( Vif , Vpr , Vpu ) . Knowledge of how to manipulate these processes to alter HIV-1 RNA splicing in cells could prove advantageous as a strategy for controlling HIV infection . This hypothesis is supported by studies where modulating SR protein abundance ( by overexpression/depletion ) caused imbalances in HIV-1 splicing , resulting in gross changes in viral protein synthesis [18] , [20] , [30] , [31] . This hypothesis is also supported by the observation that HIV-1 infection leads to a decrease in overall SR protein/activity which can be reversed by increasing SR protein kinase ( SRPK ) 2 function [32] . Consistent with these studies , we have successfully suppressed HIV-1 gene expression through modulation of another family of SR protein kinases , the Cdc2-like kinases ( CLKs ) [33] . While use of small molecular weight ( MW ) inhibitors of SRPK 1 and 2 have met with limited effect against HIV [32] , we recently demonstrated that chlorhexidine ( an inhibitor of CLKs 2 , 3 , and 4 ) is able to alter HIV-1 RNA processing , leading to inhibition of HIV-1 replication [33] . However , the toxicity of chlorhexidine in peripheral blood mononuclear cell ( PBMC ) cultures precludes its systemic use . Further supporting the viability of this approach is recent work demonstrating the suppression of HIV-1 RNA splicing using indole derivatives that function by modulating SR protein function [19] , [34] , [35] . To explore this strategy further , we tested compounds shown to modulate host alternative RNA splicing to identify new inhibitors of HIV-1 replication [36] , [37] . We report here that digoxin , a drug widely used in treatment of congestive heart failure [38] , [39] , is a potent inhibitor of HIV-1 replication . Digoxin treatment drastically reduced HIV-1 gene expression in stably HIV-1 transduced HeLa and SupT1 cell lines and is effective in inhibiting replication of HIV-1 clinical strains in human CD4+ PBMCs . Digoxin accomplishes these effects through two mechanisms: inducing oversplicing of HIV-1 RNA , resulting in an alteration in splice site usage of HIV-1 pre-mRNA as well the loss of the key regulatory protein , Rev . Consequently , this response impairs expression of viral structural proteins . Reduced Rev expression leads to HIV-1 incompletely-spliced RNAs ( US , SS ) being sequestered in the nucleus . Expression of Rev in trans led to a partial rescue of HIV-1 structural protein ( Gag ) synthesis . Coincident with the changes in viral RNA processing , digoxin treatment also induced changes in the modification of a subset of SR proteins ( SRp20 , Tra2β , SRp55 , and SRp75 ) and the activity of the CLK family of SR protein kinases . Our findings support the hypothesis that HIV-1 RNA processing can be effectively targeted without severe toxicity to the host cell . Since this stage of the virus lifecycle is not targeted by current anti-retroviral therapies ( ART ) [1] , [2] , digoxin ( and potentially the cardiac glycoside family of drugs ) represent a novel class of HIV-1 inhibitors with the potential for rapid development into an ART .
In our search for novel HIV-1 inhibitors , drugs with the capacity to alter RNA splicing were screened for antiretroviral activity [36] , [37] . We used a human cell line stably transduced with a modified X4 HIV-1 ( LAI ) provirus regulated by a Tet-ON system that requires addition of doxycycline ( Dox ) for activation of viral gene expression [33] , [40] , [41] . The effects of drugs on HIV-1 gene expression were monitored by treating HeLa rtTA-HIV-ΔMls cells for 4 hours with drugs prior to induction of virus gene expression by Dox ( Fig . 1 ) . After 20 hours , media and cell lysates were harvested for analysis of HIV-1 Gag protein expression by p24CA ELISA ( Fig . 1A ) or Western blots for Gag and Env ( gp120 ) ( Fig . 1B , top and middle , respectively ) . We observed that digoxin ( 100 nM ) caused a 94% inhibition of HIV-1 Gag protein expression relative to DMSO control ( Fig . 1A ) . In contrast , other drugs shown to affect RNA splicing such as clotrimazole and flunarizine ( 10 µM ) showed no significant effects [36] . Western blot analysis of Gag protein expression in cell lysates of digoxin-treated cells ( Fig . 1B , top ) confirms a complete loss of the Gag products , capsid ( CA ) and matrix ( MA ) -CA , and a marked reduction in Gag protein species relative to controls ( untreated and TG009 , + ) . Western blot analysis of Env ( Fig . 1B , middle ) demonstrated a loss in both gp120 and gp160 proteins to near undetectable levels compared to controls . Upon subsequent analysis of the dose response curve ( Fig . 1C ) , digoxin demonstrated potent inhibition of HIV-1 Gag protein expression with an IC50 of ∼45 nM ( IC90 = 100 nM ) . Parallel assessment of the cytotoxicity of digoxin treatment on this cell line ( Fig . 1D ) revealed no significant effects on cell viability at the dose ranges required to inhibit HIV-1 gene expression ( 50–100 nM ) as measured by XTT and Trypan blue ( TB ) exclusion assays ( 0–200 nM ) ( Fig . 1D ) . To validate our findings in a more relevant setting , the ability of digoxin to suppress HIV-1 replication in the context of human CD4+ PBMCs was examined . Isolated PBMCs were infected with a R5 BaL strain of HIV-1 in the presence of increasing doses of digoxin and the extent of virus replication was monitored by p24CA ELISA ( Fig . 2A ) . Analysis of the data revealed a profound suppression of HIV-1 replication upon addition of digoxin ( IC90 = ∼25 nM ) . Parallel examination of the effect of these treatments on cell viability ( Fig . 2B ) determined that negative effects were only discernible at doses of ≥50 nM ( by XTT assay ) , above the dose required to strongly suppress HIV-1 replication . In comparison to the stable cell line , analysis of media from PBMC infections at earlier time points ( day 3; Fig . S2 ) , representing less cycles of replication , demonstrated significant reduction in HIV-1 replication without significant effects on cell viability ( data not shown ) . As a further test of the efficacy of digoxin in suppressing HIV-1 replication , a similar trial was performed using CD8+-depleted PBMCs obtained from treatment-naïve HIV-infected patients . As shown in Fig . 2C and 2E , while Gag accumulated over time in control samples ( DMSO ) , digoxin inhibited HIV-1 replication over the 20 days of the assay to a level comparable to the nucleoside reverse transcriptase inhibitor ( NRTI ) , 3TC ( Fig . 2E and 2F ) . Furthermore , dose response curves ( Fig . 2D ) demonstrate inhibition of HIV-1 replication at an IC90 of 2 nM with no detectable effects on cell viability . To determine the mechanism underlying the response to digoxin , we analyzed its effect on the abundance of all three classes of HIV-1 mRNA by qRT-PCR ( Fig . 3 ) . Using the HeLa HeLa rtTA-HIV-ΔMls cell line , digoxin treatment induced an 84% reduction in US mRNA levels ( encoding Gag and Gagpol ) and a 68% decrease in SS mRNA ( encoding Env , p14 Tat , Vpr , Vif , or Vpu ) . In contrast , digoxin increased MS mRNA ( p16 Tat , Rev , Nef ) by 300% . The effect of digoxin on HIV-1 RNA abundance was also dose dependent ( Fig . S3 ) , in agreement with its effects on the expression of viral structural proteins , Gag and Env ( Fig . 1 ) . These results are consistent with digoxin inhibition being due to the induction of viral RNA oversplicing , which is in contrast to the inhibition of splicing induced by indole derivatives [19] , [35] , [42] . The response to digoxin results in a specific loss of larger , incompletely-spliced mRNA species ( encoded by US and SS ) that , in turn , reduces the synthesis of proteins necessary for virus assembly . To validate that the response observed was not unique to the HeLa cell line , assays were repeated in 24ST1NLESG cells , a human T cell line ( SupT1 ) chronically infected with a HIV-1 variant ( NLE−S-G , a pNL4-3-based virus vector ) [43] . Assays determined that digoxin also suppressed HIV-1 Gag expression in the SupT1 cell line ( Fig . S4C ) , inducing a similar reduction in abundance of incompletely-spliced viral RNAs ( US , SS ) and increasing MS RNA accumulation ( Fig . S4D ) as seen for HeLa rtTA-HIV-ΔMls cells ( Fig . 3 ) . To analyze the effects of digoxin on HIV-1 RNA processing in greater detail , we examined for changes in viral RNA splice site selection ( Fig . 4A–C ) . Using RNA from HeLa rtTA-HIV-ΔMls cells incubated in the presence or absence of digoxin , effects on alternative RNA splicing were analyzed by RT-PCR of the HIV-1 MS ( 2 kb ) mRNA class . Position of the primers is illustrated in Fig . 4A . Upon comparison to control samples ( Fig . 4B ) , we noted that digoxin significantly reduced the level of Rev 2/1 mRNA ( generated by the use of SA4c , a , b ) , while having limited effect on other spliced 2 kb mRNAs . Subsequent densitometry analysis of each MS mRNA species ( Fig . 4C ) revealed that digoxin induced a 73% loss of Rev 2/1 mRNA levels compared to control samples as well as a slight increase in Tat 1 ( generated by the use of SA3 ) . In contrast , other splice modulator drugs such as clotrimazole and flunarizine had no significant effect on HIV-1 MS splice site selection ( Fig . S5 ) . These results reveal that digoxin causes selective alterations in the use of viral MS pre-mRNA splice sites , leading to the specific loss of the mRNA species encoding a key HIV-1 regulatory factor , Rev ( Fig . 4B and C ) . To assess the impact of digoxin's alteration of splice site usage at the protein level , we performed western blots of cell extracts to examine for changes in the viral regulatory factors Rev and Tat . Analysis of Rev ( Fig . 4D , top ) revealed a profound loss of Rev protein expression levels relative to DMSO control ( + ) consistent with the reduced level of the corresponding mRNA ( Fig . 4B and 4C ) . This response was achieved without detectable changes in the level of p16 Tat , a Rev-independent isoform encoded by MS RNA , demonstrating selectivity in the responses observed . However , digoxin did reduce expression of p14 Tat ( Fig . 4D , bottom ) , a Rev-dependent isoform produced from SS mRNA . Reduced p14 Tat levels is consistent with both a decrease in Rev expression ( Fig . 4D , top ) and of SS mRNA ( Fig . 3 ) . These observations confirm that digoxin selectively blocks Rev protein production , leading to impaired export of Rev-dependent mRNAs ( US and SS ) that produce viral structural proteins as well as a subset of regulatory/accessory factors ( illustrated in Fig . S1 ) . As further verification that digoxin results in reduced Rev activity , in situ hybridization was performed to look for changes in HIV-1 US RNA distribution associated with drug treatment . As shown in Fig . 5A , in the presence of doxycycline alone ( DMSO +Dox ) , signal for HIV-1 US RNA is observed throughout the cell with intense staining at the sites of proviral integration . In contrast , addition of both doxycycline and digoxin results in viral US RNA being predominately restricted to the nucleus ( Fig . 5A , Digoxin +Dox , ) . Treatment of cells with the NRTI , 3TC , had no effect on the distribution of the viral US RNA ( Fig . 5A , 3TC +Dox ) . To determine whether reduction of Rev alone was responsible for the loss of HIV-1 structural protein expression , cells were transfected with control ( dsRed ) or Rev ( dsRed-Rev ) expression vectors in the presence of digoxin and Gag protein synthesis monitored ( Fig . 5B , C ) . These assays revealed that trans expression of Rev ( ds Red Rev ) yielded a partial recovery of HIV-1 Gag protein synthesis in comparison to the control vector ( ds Red ) . Digoxin inhibits the function of the sodium-potassium ( Na+/K+ ) ATPase in the plasma membrane resulting in increased intracellular levels of calcium as well as the activation of a number of signaling cascades [38] , [39] , [44] . How events at the plasma membrane ultimately result in altered HIV-1 RNA processing in this system is not immediately apparent . However , many of the kinase cascades affected by cardiac glycosides have been described to influence alternative RNA splicing [45] , [46] , [47] . One hypothesis is that digoxin-induced alteration of cellular signaling cascades ultimately affect the activity of factors , such as SR proteins , known to regulate HIV-1 RNA splicing [16] , [19] . To test whether any alteration in SR protein function occurred in our experimental system , we first examined the effect of digoxin treatment on SR protein kinases belonging to the CLK family ( 1–4 ) [48] , [49] , [50] . As indicated in Fig . 6A and S6 , overexpression of any of these kinases results in a shift in the subnuclear distribution of SR proteins ( such as SC35/SRSF2 ) from being localized to nuclear speckles to being dispersed throughout the nucleus ( compare GFP− with GFP+ cells treated with DMSO ) . Treatment with digoxin reversed the effects of all CLK kinases tested ( Fig . 6A and S6 , Digoxin ) ; SC35 remained in nuclear speckles in the presence of digoxin despite CLK overexpression , consistent with reduced activity of the transfected kinases . Impaired activity of a family of SR protein kinases in response to digoxin addition suggests that an alteration in SR protein function underlies the inhibition of HIV-1 replication . To explore this hypothesis , SR proteins were analyzed by western blot of cell lysates ( Fig . 6B ) for changes in abundance or migration due to drug treatment . Initial analysis of phospho-SR proteins by 1H4 antibody determined that digoxin treatment increased the levels of at least two phospho-SR proteins ( Fig . 6B ) : increasing SRp55 and moderately increasing SRp75 relative to DMSO controls ( +/− ) . No consistent changes in the overall phospho-SR protein levels were observed in the presence or absence of HIV-1 expression by this antibody . To further explore specific members of SR proteins affected by digoxin , we performed western blot analysis on a panel of SR proteins with specific antibodies to SRp20 , Tra2β , 9G8 , and SF2/ASF ( Fig . 6C ) . Recent work [51] demonstrated that treatment with digitoxin ( another cardiac glycoside ) induced marked alterations in SRp20 and Tra2β abundance . Consistent with the selective effect of a cardiac glycoside on a subset of SR proteins , we observed that SRp20 ( Fig . 6C ) underwent a shift to a higher MW species upon digoxin treatment compared to DMSO-treated cells ( +/− ) . Treatment of extracts with alkaline phosphatase confirmed that the shift observed in SRp20 was due to hyperphosphorylation of the protein ( Fig . 6D ) . In the case of Tra2β ( Fig . 6C ) , digoxin treatment increased the level of a high MW form of Tra2β that was reduced upon induction of HIV-1 ( + Dox ) compared to control ( −Dox ) . However , alkaline phosphatase had no effect on the higher MW forms of Tra2β blots induced by digoxin treatment ( data not shown ) . Analysis of other SR proteins , 9G8 and SF2/ASF ( Fig . 6C ) , showed little or no change in levels or MW upon digoxin treatment . These data are consistent with the recent work of Anderson et al . [51] in that only a subset of SR proteins are affected by digoxin treatment , suggesting that at least one or a combination of these splice factors play a critical role in mediating the change in HIV-1 RNA processing or expression . The increased SRp20 phosphorylation or changes in Tra2β modification in response to digoxin raised the possibility that the alterations in HIV-1 RNA splicing could be attributed to increased activity of either factor . To test this hypothesis directly , HeLa rtTA-HIV-ΔMls cells were transfected with vectors expressing these factors and their effects on viral structural protein and RNA accumulation were assessed ( Fig . 7 ) . To ensure that only cells taking up DNA expressed the HIV-1 provirus , cells were also co-transfected with plasmids expressing the TetO activator , tTA , to induce provirus expression , and secreted enzyme alkaline phosphatase ( SEAP ) as an indicator of global effects on gene expression . As shown in Fig . 7C , detection of HIV-1 Gag by p24CA ELISA was dependent upon transfection with tTA ( see −tTA vs . +tTA ) . Transfection of SRp20 or either isoform of Tra2β ( Tra2β1 and Tra2β3 ) resulted in a marked reduction in Gag protein expression with unchanged or increased expression of SEAP . Subsequent analysis of viral RNA accumulation indicated that each factor functioned in a different manner . qRT-PCR of each of the HIV-1 mRNAs ( Fig . 7D ) determined that SRp20 overexpression resulted in reduced accumulation of both US and SS viral RNAs with a trend towards increased MS RNA levels . In contrast , overexpression of either isoform of Tra2β resulted in reduced accumulation of all HIV-1 mRNAs . Subsequent analysis of splice site selection within the MS class of viral RNAs revealed distinct differences in how these factors affected HIV-1 MS RNA splicing ( Fig . 7E , F ) . Similar to digoxin , SRp20 overexpression induced a shift in splice site usage that resulted in increased Tat1 accumulation while reducing Rev1/2 and Nef2 levels . In contrast , Tra2β1 overexpression elicited little change in splice site selection while Tra2β3 overexpression induced a marked accumulation of Nef1 , generated by splicing the first 5'ss of HIV-1 to the last 3'ss of the virus . Taken together , the response to SRp20 overexpression is most similar to that observed upon digoxin treatment .
Despite the success of ART/HAART , there are many caveats with current HIV-1 therapies , including the emergence of drug resistant forms of HIV-1 , high cost , and toxicity [1] , [2] , [10] . New drugs with improvement in these profiles and novel mechanisms of action are necessary [1] , [2] , [9] . A number of strategies have targeted HIV regulatory and accessory proteins to date , but most remain under development [9] , [52] . It is unclear whether disrupting cellular processes essential for HIV-1 replication can yield alternative therapies without significant cellular toxicity . However , a number of existing therapies for other human diseases ( e . g . heart disease , cancer , and dementia ) do work by altering host protein function and are well tolerated [53] , [54] , [55] , [56] . In this report , we demonstrate a novel and alternative use of the FDA-approved cardiovascular drug , digoxin , as an anti-HIV-1 therapeutic ( summarized in Fig . 8 ) . More importantly , digoxin was found to inhibit virus replication by a novel mechanism , inducing oversplicing of HIV-1 RNA ( Figs . 3 , S3 , 4 , and S4D ) —a stage of the virus lifecycle not targeted by current HIV-1 inhibitors and under host cell control . Digoxin achieves this effect by altering the splicing of HIV-1 RNA , reducing accumulation of two classes of viral mRNA ( US and SS; Figs . 3 , S3 , S4D ) that encode structural proteins essential for new virion assembly ( Gag , Gagpol , and Env; Fig . 1 ) . In addition , digoxin selectively inhibits expression of the HIV-1 regulatory factor Rev through specific alteration of viral RNA splice site use without affecting the expression of other viral proteins ( p16 Tat; Fig . 4 ) . While digoxin induced a 73% reduction in Rev2/1 RNA accumulation , it also increased MS viral RNA levels ∼3 fold ( Fig . 3 ) . Combined , these alterations may not account for the complete loss of Rev protein observed , suggesting the possibility that digoxin may have effects beyond the changes in viral RNA processing detected . The loss of Rev further impairs expression of incompletely-spliced viral mRNAs ( US and SS ) by preventing Rev-mediated export of RNAs to the cytoplasm ( Fig . 5A ) for translation into respective viral structural proteins ( Gag , Gagpol , and Env ) and regulatory/accessory factors ( p14 Tat , Vif , Vpr , and Vpu ) ( Figs . 1 , 2 , S2 , 4D ) . Furthermore , the effects were achieved at concentrations of digoxin that did not impact HeLa , SupT1 , and PBMC cell viability relative to control treatments ( Figs . 1 , 2 , and S4 ) . Rev expression in trans ( Fig . 5B–C ) only partially reversed the effects of digoxin , indicating that loss of Rev alone is not sufficient to explain the full effect of digoxin . Rather , in light of the demonstration that Rev acts primarily on newly synthesized viral RNA [57] , the enhanced processing of the viral RNA induced by digoxin may result in the incompletely-spliced HIV-1 RNAs having too short a half-life to be engaged by Rev even when Rev is present . In summary ( Fig . 8 ) , digoxin selectively impairs HIV-1 replication at two levels: ( 1 ) through global alterations in the efficiency of HIV-1 RNA processing and ( 2 ) blocking export of incompletely-spliced viral RNAs to the cytoplasm . Digoxin and other cardiac glycosides are known to bind the Na+/K+-ATPase pump in the plasma membrane , initiating the activation of multiple signaling cascades that result in increased intracellular calcium concentrations as well as signaling of Src , AKT , and MAPK kinases [44] , [58] , [59] . How this response initiated at the cell membrane can alter RNA splicing was not immediately clear . In light of the observed changes in HIV-1 RNA processing , we initially focused on factors known to modulate these events: SR proteins [60] , [61] , [62] , [63] . Consistent with the findings of Anderson et al . [51] , our results reveal that a subset of SR proteins ( SRp20 , Tra2β , SRp55 , and SRp75 ) are altered as well as the function of a number of SR protein kinases ( CLKs 1–4 ) upon digoxin addition ( Fig . 6 ) . In the work of Anderson et al . [51] , only a subset of the exons examined were affected by treatment with digitoxin , suggesting that the response is not a general perturbation of host RNA splicing but is more selective . Since the modifications of SRp20 or Tra2β1 detected might increase their activity , we subsequently examined the impact of overexpression of both factors on HIV-1 RNA processing ( Fig . 7 ) . While the three factors tested ( SRp20 , Tra2β1 , and Tra2β3 ) all elicited a marked reduction in HIV-1 Gag synthesis upon overexpression , analysis of the effects on viral RNA splicing determined that the basis for the response was markedly different . Of the three factors tested , overexpression of SRp20 most closely mimics the changes induced by digoxin; reducing accumulation of US and SS viral RNAs while trending towards increased MS RNA abundance . Furthermore , SRp20 induced increased accumulation of Tat1 and reduced Rev1/2 mRNA levels as observed with digoxin . The response documented here differs significantly from those induced by overexpression of SC35 , SRp40 , 9G8 , and SF2/ASF previously reported [18] , [20] , [31] . In these studies , overexpression of SC35 , SRp40 , or 9G8 resulted in almost exclusive formation of MS RNA encoding Tat ( Tat1 ) , while SF2/ASF increased usage of the splice sites for Vpr . However , the effects of these factors on HIV-1 RNA accumulation and expression differ among published reports: one indicates that SF2/ASF , SC35 , or SRp40 overexpression increases US viral RNA accumulation [18] while another showed marked reduction of all viral RNAs with only SF2/ASF significantly decreasing Gag expression [31] . None of these reports demonstrated selective alterations in Rev1/2 RNA abundance comparable to digoxin or SRp20 overexpression reported here . Future efforts will be focused on understanding how SRp20 achieves this response on HIV-1 , through either direct interaction with sites on the viral RNA and/or manipulation of abundance/activity of other host factors . In contrast to the effects of SRp20 , overexpression of Tra2β1 and Tra2β3 reduced levels of all viral RNAs ( Fig . 7D ) while only Tra2β3 altered splice usage to favor Nef1 ( Fig . 7E , F ) . The difference in activity of Tra2β1 and Tra2β3 is of particular interest since both share a common RRM domain as well as a C-terminal RS domain ( Fig . 7A ) and interact with a common set of SR proteins [64] . However , previously analyses had indicated that Tra2β3 had limited or no ability to modulate splicing of a number of RNA substrates tested [65] . Our demonstration that the two Tra2β isoforms have quite distinct effects on splice usage in the context of HIV-1 RNA splicing suggests that variation in abundance of these two isoforms of Tra2β is likely to yield quite distinct effects on host cell RNA splicing . The response seen upon Tra2β3 overexpression is most similar to alterations induced upon mutation of the exon splicing enhancer ( GAR ) adjacent to SA5 . Previous studies had determined that reduced function of GAR resulted in increased accumulation of spliced RNA corresponding to Nef1 [66] , [67] , raising the possibility that Tra2β3 functions by interfering with GAR function . Our determination that digoxin can alter the equilibrium in viral RNA processing demonstrates that this step of the virus lifecycle can be manipulated to block HIV-1 replication . In principle , targeting host factors essential for HIV-1 replication offers the promise of broad spectrum activity against multiple viral strains and a reduced potential of resistance . Although digoxin has potent effects on HIV-1 in our assays , its use in the treatment of cardiovascular conditions has a narrow therapeutic dose range of 0 . 5–2 . 0 ng/mL ( max . 5 nM ) with higher doses yielding increased toxicity ( including death ) [38] , [39] . Our experiments using the stably transduced HeLa rtTA-HIV-ΔMls and 24ST1NLESG cell lines determined that complete suppression of HIV-1 gene expression requires concentrations of digoxin ( IC90 = 100 nM , Fig . 1C; IC90 = 370 nM , Fig . S4C , respectively ) well above what is compatible for use in humans . However , our subsequent studies using PBMCs showed that reduced doses of digoxin are sufficient to achieve a significant response ( IC90 = 25 nM , Fig . 2A , B ) . In experiments using HIV infected patient PBMCs , doses as low as 2 nM strongly suppressed HIV-1 replication ( Fig . 2C , D ) . The differences in the dose of digoxin required to achieve a measurable response between the various assays might reflect differences in the ability to activate the signaling cascade initiated by the binding of digoxin to the Na+/K+ ATPase at the cell surface [44] . Given the transformed nature of both HeLa and SupT1 cells , it is not unexpected that portions of this cascade may be altered relative to PBMCs . Alternatively , differences in the response of the different cell types ( HeLa/SupT1 vs . PBMCs ) to digoxin may reflect the nature of the assay itself . In the experiments using the stably transduced cell lines ( HeLa/SupT1 ) , >90% of the cells are expressing viral proteins upon induction and , hence , inhibition would require significant alterations in HIV-1 RNA processing/protein synthesis . In contrast , for PBMCs , detection of Gag expression is dependent upon the exponential amplification of the virus in the culture . In this context , even small perturbations in HIV-1 replication will result in significant differences over multiple rounds of replication . The benefit is that doses of digoxin within the therapeutic range were able to suppress HIV infection . Better responses might be achieved using derivatives of digoxin with improved activity and a better therapeutic index [44] , [58] . The determination that digoxin , acting through the Na+/K+-ATPase ( a plasma membrane receptor ) , can suppress HIV-1 gene expression suggests that its downstream effectors might also prove to be therapeutic targets . In addition , compounds which mimic digoxin's effect on CLKs and/or SR protein function could prove equally capable of altering HIV-1 RNA processing . Several compounds affecting CLK function ( TG003 and chlorohexidine ) have already been described [36] , [68] , and we recently demonstrated that chlorhexidine ( but not TG003 ) inhibits HIV-1 gene expression [33] . Recent studies [59] , [69] have identified multiple kinases mediating the effect of cardiac glycosides in transformed cells . Determining which of these kinases is responsible for mediating digoxin's effect on HIV-1 RNA processing would be useful in developing a more targeted approach to manipulating viral gene expression . However , the demonstration that digoxin can inhibit HIV-1 replication through a novel mechanism without significant toxicity to the host cell serves as proof that this strategy is viable and could be used in junction with existing treatments for better control of this infection .
Screening of drugs for effects on HIV-1 RNA processing was performed using the HeLa rtTA-HIV-ΔMls cell line containing an inducible Tet-On HIV-1 provirus [40] , [41] as described in our previous study [33] . Activation of virus gene expression in these cells was achieved by addition of doxycycline ( Dox ) or transfection of plasmid expressing tTA . In drug screens , cells were seeded one day prior in IMDM containing 10% FBS , 1X Pen-Strep , and 1X Amphotericin B ( Wisent Corporation ) while drugs were solubilized to ∼1000X of its final treatment concentration in DMSO . Next , cells were treated for 4 h with 100–200 µL of drugs pre-diluted to ∼25X of its final concentration in Opti-MEM ( Invitrogen , #31985070 ) and HIV gene expression was induced with Dox ( 2 µg/mL ) . After ∼24 h of drug treatment , cells and media were harvested . To monitor effects of drug treatments , p24CA ELISA , western blotting , and RNA analyses were performed as described below . Cell viability was assessed using biochemical ( XTT assay; Sigma-Aldrich , #TOX2 ) and/or physical ( trypan blue exclusion; Invitrogen , #15250-061 ) assays . Written informed consent was obtained from volunteer blood donors in accordance with the guidelines for conduct of biomedical research at the University of Toronto , and all experimental protocols were approved by the University of Toronto institutional review board . Human primary cells were obtained for experiments either from healthy volunteer blood donors ( uninfected with HIV ) or drug-naïve HIV-infected individuals . For infection experiments , PBMCs were isolated , infected with HIV-1 ( BaL ) , and cultured as described previously [33] . Cells were treated with drugs pre-diluted in RPMI in the same manner as described above . Every 3–4 days , 0 . 5 mL of media was harvested for p24CA ELISA and replaced with 0 . 5 mL of fresh R-10 medium containing fresh drug treating 1 mL ( ∼1 . 5X final with fresh and decayed drug ) . The effect of drugs on cell health was assessed in parallel by XTT and/or trypan blue assays . For experiments using HIV-infected patient samples , PBMCs were first depleted of CD8+ T cells using Dynabeads CD8 ( Invitrogen , #111 . 47D ) as outline by manufacturer . Remaining cells were then activated by treatment with anti-CD3 anti-CD28 antibodies ( Bio Legend #302914 and 317304 , respectively; 1 µg/ml of each ) as well as 50 U/ml of IL-2 ( BD Pharmingen #554603 ) in the presence or absence of indicated drugs as mentioned above . Media ( 0 . 5 ml ) was collected every 3–4 days and replaced with fresh media ( 0 . 5 ml ) containing 20 U/ml of IL-2 and fresh drug . Effect of compounds on cell viability was monitored in parallel by XTT assay and expressed relative to control ( DMSO ) treated cells . HIV-1 growth in cultures was monitored by p24CA ELISA of cell supernatants . RNA was extracted from cells by Aurum Total RNA Mini Kits ( Bio-Rad , Cat . #732-6820 ) . Purified RNA was reverse transcribed using M-MLV ( Invitrogen , Cat . No . 28025-013 ) and resulting cDNAs were used to quantitate HIV-1 mRNA levels by qRT-PCR as described [33] . To monitor for changes in HIV-1 US RNA subcellular distribution in response to digoxin , cells were treated with digoxin , 3TC , or DMSO solvent for 4 h and then viral gene expression was induced by addition of Dox . After 20 h , cells were fixed in 3 . 7% formaldehyde-1XPBS . Cells were permeabilized by treatment with 70% EtOH , then rehydrated in hybridization buffer ( 10% formamide , 2XSSPE ) . Hybridization was performed using a mixture of 48 Quasar 570-labelled oligonucleotides spanning the MA , CA , and nucleocapsid ( NC ) regions of HIV-1 as detailed by the supplier ( Biosearch Technologies ) . Following washing to remove unbound probe , nuclei were stained with DAPI and images acquired using a Leica DMR microscope at 630× magnification . The effect of drugs on HIV-1 splice site usage within the 2 kb , MS RNA class was analyzed by performing RT-PCR of cDNA obtained from RNA purified and reverse transcribed as previously described [33] . HeLa rtTA-HIV-ΔMls cells were transfected with vectors expressing GFP-tagged CLK1 , CLK2 , CLK3 , or CLK4 . Twenty-four hours post-transfection , cells were treated with either digoxin or DMSO for 24 h , fixed , processed , and analyzed by immunofluorescence microscopy [33] . Effects on SC35 localization was assessed using a mouse anti-SC35 antibody ( BD Pharmingen , #556363 ) and a secondary Texas Red-conjugated donkey anti-mouse IgG antibody ( Jackson Immunoresearch , #715-075-151 ) , while nuclei were stained with DAPI . To monitor HIV-1 gene expression or virus replication ( Gag synthesis ) , cell culture supernatants were assayed by a HIV-1 p24CA antigen capture assay kit ( AIDS & Cancer Virus Program , NCI-Frederick , Frederick , MD USA ) . Media harvested from PBMC cultures infected with HIV-1 ( BaL ) were diluted ∼250-fold ( or as needed ) prior to performing this assay . For analysis of HIV-1 and SR protein expression by Western blot , cells were solubilized in RIPA buffer , quantitated by Bradford assay , and run on 8 , 10 , or 12% SDS-PAGE under reducing conditions , and then transferred to PVDF . Normally , 25–30 µg of protein was loaded , blots blocked in either 5% Milk-T ( 5% skim milk , 0 . 05% tween-20 , 1XPBS ) or 3% BSA-T for 1 h at room temperature ( RT ) according to the antibody diluent used , and blots incubated with antibody at RT for ∼2 . 5 h , unless otherwise specified . Specific antibodies and conditions used for Tat , anti-tubulin , and isotype-specific HRP-conjugated antibodies were used as described [33] . Additional antibodies and conditions used in this study include a mouse anti-p24 supernatant from hybridoma 183 ( provided by M . Tremblay , Laval University ) : 1/10th dilution in PBS-T incubated for 1 h at RT , blocked in 5% Milk-T overnight at 4°C . Mouse anti-gp120 purified supernatant from hybridoma 902 ( obtained through the AIDS Research and Reference Reagent Program , Division of AIDS , NIAID , NIH: Hybridoma 902 ( anti-gp120 ) from Dr . Bruce Chesebro ) : 1/10th dilution in PBS-T incubated normally or O/N at 4°C , blocked in 3% BSA-T at RT for 2 . 5 h . Mouse anti-Rev ( Abcam , #ab85529 ) : 1/1000th dilution in 3% BSA-T incubated O/N at 4°C , loaded with 30–40 µg of protein . Mouse anti-phospho-SR ( 1H4 ) ( Invitrogen , #33-9400 ) : 1/5000th dilution in 3% BSA-T , blocked for ∼2 . 5 h at RT or overnight at 4°C . Rabbit anti-Tra2β ( Abcam , #ab3135353 ) : 1/10 , 000th dilution in 3% BSA-T incubated for 1 . 5 h at RT . Rabbit anti-9G8 serum ( Znk1 . 4 ) : 1/3000th dilution in 5% Milk-T . Mouse anti-SRp20 ( Invitrogen , #334200 ) , 1/1000th dilution in 3% BSA-T , loaded with 20 µg of protein . Generally , Western Lightning-ECL ( Perkin-Elmer , #NEL101 ) but for anti-Rev , -Tat , and -gp120 blots , Western Lightning Plus-ECL ( #NEL105 ) were used for development of signals onto autoradiography film . In addition , phosphatase inhibitors ( e . g . 10 mM sodium fluoride , 2 mM sodium orthovanadate ) were added to solutions for SR protein analyses . Lastly , SR protein phosphorylation was confirmed through treatment of ∼20 µg of cell lysate with 20 U of calf intestinal alkaline phosphatase ( NEB , #M0290S ) for ∼45 minutes at 37°C prior to western blot analysis . To assess effects of protein overexpression , cells were transfected in the presence or absence of the tTA expression vector , CMV PLAP ( expressing SEAP/alkaline phosphatase ) , and either empty vector ( CMVmyc pA ) , CMVmyc SRp20 , CMVmyc Tra2β1 , or CMVmyc Tra2β3 using polyethylene imine ( PEI ) . At 48–72 h post-transfection , cells and media were harvested . To monitor effects of these manipulations , p24CA ELISA , western blotting , and RNA analyses were performed as described previously [33] . To assess the ability of expression of Rev in trans to rescue the synthesis of HIV-1 Gag in the presence of digoxin , cells were transfected as described above with plasmids expressing either dsRed or a dsRed-Rev fusion . At 24 h post-transfection , cells were treated with digoxin for 4 h then HIV-1 expression was induced for 20 h by addition of doxycycline . Cells were subsequently fixed and examined by immunofluorescence for co-expression of Gag and dsRed signal using a Leica DMR microscope . Data was analyzed using Microsoft Excel and expressed as means ± standard error of the mean ( SEM ) . Differences between two groups of data ( i . e . drug treatment vs . DMSO ( +Dox ) control , drug treatment vs . DMSO ( +HIV ) , or transfected factor vs . mock vector ( +tTA ) were compared by Student's t-test ( two-tailed ) . Statistical significance of results are indicated on each graph as follows: p value<0 . 05 , * , p value<0 . 01 , ** , and p value<0 . 001 , *** , unless otherwise indicated .
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Antiretroviral therapies ( ART ) for HIV/AIDS are successful in slowing disease progression by inhibiting viral proteins . However , the ability of HIV to adapt to ARTs has given rise to drug-resistant virus strains that now represent ≥16% of newly infected people . This development calls for the generation of new treatment strategies . Since HIV is dependent upon RNA processing under control of the host , we searched for compounds/drugs that inhibit HIV-1 replication at this step . We identified digoxin as a potent inhibitor of HIV-1 replication . The drug inhibited expression of HIV-1 structural proteins and a key factor involved in viral RNA export . This response was accomplished by altering the efficiency and splicing choices in HIV-1 RNA processing . Since this stage of the virus lifecycle is not targeted by current ARTs , the digoxin family of drugs represent a novel class of HIV-1 inhibitors . Since digoxin targets host factors and is already in clinical use , it and potentially the cardiac glycoside family of drugs has the possibility for swift development into a new ART for HIV-1 infection .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biology",
"microbiology",
"molecular",
"cell",
"biology"
] |
2013
|
Digoxin Suppresses HIV-1 Replication by Altering Viral RNA Processing
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Antagonistic interactions are likely important driving forces of the evolutionary process underlying bacterial genome complexity and diversity . We hypothesized that the ability of evolved bacteria to escape specific components of host innate immunity , such as phagocytosis and killing by macrophages ( MΦ ) , is a critical trait relevant in the acquisition of bacterial virulence . Here , we used a combination of experimental evolution , phenotypic characterization , genome sequencing and mathematical modeling to address how fast , and through how many adaptive steps , a commensal Escherichia coli ( E . coli ) acquire this virulence trait . We show that when maintained in vitro under the selective pressure of host MΦ commensal E . coli can evolve , in less than 500 generations , virulent clones that escape phagocytosis and MΦ killing in vitro , while increasing their pathogenicity in vivo , as assessed in mice . This pathoadaptive process is driven by a mechanism involving the insertion of a single transposable element into the promoter region of the E . coli yrfF gene . Moreover , transposition of the IS186 element into the promoter of Lon gene , encoding an ATP-dependent serine protease , is likely to accelerate this pathoadaptive process . Competition between clones carrying distinct beneficial mutations dominates the dynamics of the pathoadaptive process , as suggested from a mathematical model , which reproduces the observed experimental dynamics of E . coli evolution towards virulence . In conclusion , we reveal a molecular mechanism explaining how a specific component of host innate immunity can modulate microbial evolution towards pathogenicity .
Bacteria can be used to study evolution in real time in controlled environments , i . e . experimental evolution [1] . Different studies have demonstrated that bacterial populations have an enormous potential to adapt to relatively simple abiotic challenges under laboratory environments [2] , [3] . On the other hand , far less is known on how biotic interactions shape bacterial adaptive evolution . Antagonistic interactions ( predation , parasitism ) are likely to be important determinants of the rate of adaptive change observed in bacteria , their trait diversity and genome complexity [4] , [5] , [6] . The best-studied antagonistic interaction in an evolutionary laboratory setting is the one involving bacteria and their phages , which increases rates of bacterial adaptation and diversification [7] , [8] , demonstrating that biotic interactions can have an important role in bacterial evolution [9] . Another common antagonistic interaction faced by bacteria occurs when these infect mammals and are directly exposed to cells of the host immune system . To our knowledge this interaction has never been addressed in an experimental evolution context . Here , we determined the mechanisms via which E . coli evolve to overcome the antagonistic interaction imposed by one of the central components of host innate immunity , namely monocyte/macrophages ( MΦ ) . E . coli is both a commensal and a versatile pathogen , acting as a major cause of morbidity and mortality worldwide [10] . Moreover , there is evidence that some pathogenic E . coli evolved from commensal strains [11] , [12] , making E . coli an ideal organism to study the transition from commensalism to pathogenicity . E . coli colonizes the infant gastrointestinal tract within hours after birth , and typically builds a mutualistic relation . However , non-pathogenic strains of E . coli can become pathogenic , when the gastrointestinal barrier is disrupted as well as in immunosuppressed hosts [13] , [14] , [15] . MΦ are a key component of host defense mechanisms against pathogens [16] . They can provide direct bactericidal response through phagocytosis , a process by which bacteria are killed inside endocytic phagosomes , through the generation of reactive oxygen and nitrogen species among other effector mechanisms . Yet many bacterial species are capable to escape and resist eukaryotic cells [17] , [18] , suggesting that several bacterial defense mechanisms evolve upon encounter with MΦ . Adaptive microbial mechanisms to escape MΦ include surface masking and capsule formation ( to avoid engulfment and phagocytosis ) , increased motility , filamentation and biofilm formation . Mechanisms acting after engulfment by MΦ include toxin release . Within the species of E . coli alone , there are examples of several different mechanisms [19] . In the present study , we established an in vitro system in which E . coli is allowed to evolve under continuous selective pressure of MΦ , and ask how quickly and by which mechanisms commensal E . coli evolve resistance to one of the sentinels of the innate immune system , the MΦ .
We followed the evolution of six E . coli populations ( all founded from the same ancestral clone ) , when adapting to the antagonistic interaction imposed by the murine monocytic cell line ( RAW 264 . 7 ) , referred throughout the text as MΦ . The bacterial populations ( M1 to M6 ) evolved , by serial passage , in complete culture medium with MΦ and were propagated at a multiplicity of infection ( MOI ) of 1∶1 ( 106 E . coli to 106 MΦ , see Fig . S1 ) . After 24 hours bacterial numbers reach around 4×108 and are subsequently bottlenecked to start the next passage with 106 bacteria again . In parallel , we also evolved E . coli under identical experimental conditions in the absence of MΦ ( the resulting evolved clones are named CON ) . In this case the population is propagated by daily passages involving a bottleneck of 104 cells at each passage . This results in ∼15 generations per day , given the increase in bacteria numbers observed during 24 hours . All populations evolved for a period of 30 days , which corresponds to approximately 450 generations . We note that this is an approximate value because as adaptation proceeds the population dynamics will change and differences in the number of generations per day will occur . Adaptation of the bacterial lines in the presence of MΦ was characterized by the emergence of phenotypic variation within populations . After 4 days of evolution , i . e . approximately 60 generations , distinct colony morphologies emerged in all populations , detected when plating on LB plates ( Fig . 1A ) . Such morphological diversity was never observed in control populations evolved for 30 days under the same experimental conditions in the absence of MΦ ( n = 6 ) . Two distinct heritable morphs were identified and scored , i . e . small colony variants ( SCV ) and large translucid mucoid ( MUC ) colonies and their frequencies were quantified over time ( Fig . 1B ) . SCVs were observed in five out of six populations , but this morph remained at low frequency and was only detected transiently . The parallel emergence of SCVs in independent evolving populations , suggests that this phenotype constitutes an initial adaptation of E . coli to the antagonistic interaction imposed in vitro by MΦ . In contrast , MUC clones which rose in frequency in all populations , reached fixation in five out of six populations by day 30 . The changes in frequency of SCVs and MUCs showed complex dynamics ( Fig . 1B ) . In some populations , once SCVs decreased in frequency MUCs tended to increase , e . g . populations M2 and M3 . This suggests that MUCs can outcompete SCVs , presumably due to a larger fitness advantage . These observations suggest that E . coli morphological diversity can emerge very rapidly as a result of their adaptation to MΦ . Competitive fitness of E . coli populations was measured at two time points during the process of evolution ( day 19∼285 generations and day 30∼450 generations ) , revealing that all populations exhibit a significant fitness increase ( Fig . 2A ) . On average , fitness increase was of 0 . 10 ( 2SE = 0 . 07 ) and 0 . 27 ( 2SE = 0 . 10 ) after 19 and 30 days , respectively . Fitness increased between generations 285 and 450 across populations ( Students' paired t-Test , P = 0 . 02 ) . The observation that SCVs emerged in at least 80% of the independent evolving populations but with low frequency strongly suggests that SCVs have a transient selective advantage that is outcompeted over time . To better understand this selective advantage we performed two assays: 1 ) exposure of MΦ in vitro to SCVs to test for possible intracellular versus extracellular growth differences relative to that of the ancestral strain; 2 ) a fitness assay to determine the ability of SCV to outcompete the ancestral strain , in the presence of MΦ . We did not observe any difference in SCV growth either intracellularly ( Rr = 0 . 99+0 . 16 ( 2SE ) ) or extracellularly ( Rr = 1 . 01+0 . 13 ( 2SE ) ) relative to the ancestral non-evolved clone , while there was an advantage in the competitive fitness assay ( Fig . 2B ) . SCVs ( clones SCV_M1_D8 and SCV_M3_D5 ) exhibited a fitness advantage relative to the ancestral strain , inside MΦ , as assayed 2 hours after infection . However , this advantage was restricted to the early phase of infection , given that SCVs showed a disadvantage outside MΦ 24 hours after infection ( Fig . 2B ) . These results probably explain why SCVs increased in frequency but failed to reach fixation ( see Fig . 1B ) . We tested the in vitro evolved SCVs for traits common to those of clinical SCV isolates from different bacterial species [20] [21] . The evolved E . coli SCVs showed an increased resistance to aminoglycosides , but not to other antibiotics ( see Supplemental Table S1 ) , were catalase negative and showed a remarkable instability . In rich medium SCVs reverted to a large colony phenotype at a frequency of 9×10−4 ( 2SE = 4×10−4 ) and supplementation with hemin enhanced their growth relative to the ancestral ( SCV_M1_D8: 2 . 9±1 ( 2SE ) and SCV_M3_D5: 2 . 5±0 . 7 ( 2SE ) ) . These results imply that the selective pressure of MΦ led to the emergence of phenotypes typical of pathogenic bacteria . Mucoidy , the trait evolved in the MUC clones , is also a trait observed in certain infections , for example in Pseudomonas aeruginosa or E . coli [22] , [23] . The in vitro evolved MUCs produce high levels of exopolysaccharides when plated on LB . Since colanic acid is present in most natural E . coli isolates [24] , and this capsule is made in mutants of E . coli that emerge under stress conditions [25] , we tested mucoid clones for overproduction of this exopolysaccharide . Mucoid clones showed overproduction of colanic acid ( Fig . 2C , Fig . S2 ) , suggesting that rapid evolution to change this trait can occur under the specific selection pressure imposed by MΦ . We tested whether MUCs escaped MΦ engulfment , by quantifying the relative abundance of intracellular and extracellular of MUC after 3 hours co-incubation with MΦ . Relative abundance of intracellular bacteria in MΦ was lower for MUC versus the ancestral strain in 6 out of 6 MUC clones tested ( Fig . 2D ) . Moreover , the extracellular abundance of MUC clones relative to ancestral was higher in 4 out of 6 MUCs tested . We then asked whether MUCs would trigger MΦ cytotoxicity , a process that would contribute to reduce the negative impact exerted by MΦ on MUC versus ancestral clones . MΦ cytotoxicity was similar in the presence of MUC versus ancestral clones ( Fig . S3A and Fig . S3B ) . Furthermore MUCs did not cause any significant changes in MΦ ability to engulf the ANC clone ( Fig . S3C ) . Taken together , these results strongly suggest that MUCs are better adapted to escape MΦ but do not diminish the ability of MΦ to internalize ancestral E . coli . We tested whether adaptation of evolved MUC clones to escape MΦ is associated with increased virulence . We compared the survival of mice infected systemically via the intra-peritoneal route , with increasing amounts of MUC versus ANC bacteria or bacteria that evolved in the absence of MΦ ( CON ) ( Fig . 3A ) . The lethal dose 50 ( LD50 ) of MUC infection ( LD50: 2 . 8×107 , with 95% CI 1 . 4×107–5 . 8×107 ) was 5–10 times lower than that of ANC ( LD50: 1 . 6×108 , with 95% CI 8 . 5×107–2 . 8×108 ) or CON ( LD50: above 1×108 ) , as inferred from the confidence intervals ( Fig . 3A and 3B ) , suggesting that MUC clones have increased virulence . In agreement with these observations , infection with ancestral or with bacteria evolved in the absence of MΦ at a dosage corresponding to the MUC LD50 was not lethal , i . e . , 100% survival of mice occurred ( log-rank test: χ22 = 9 . 9 , p = 0 . 007; Fig . 3C ) . Higher lethality of MUC infection was associated with significant reduction in temperature ( but not weight ) , as compared to infection with ANC bacteria at the dosage corresponding to the MUC LD50 ( χ22 = 0 . 61 , p = 0 . 0004; Fig . 3D ) . We then asked whether MUC bacteria elicited a MΦ response in vitro that would be somehow altered , as compared to the response elicited under the same conditions by the ANC or CON clones . When co-cultured with MUC , primary mouse peritoneal MΦ produced similar levels of the pro-inflammatory cytokine TNF , as compared to MΦ co-culture with ANC or CON clones ( see Text S1 and Fig . S4 ) . This suggest that although MUC clones have evolved to escape MΦ in vitro and increasing their pathogenicity in vivo , these clones are still readily detected by MΦ , as revealed by TNF secretion . This read out was used hereby as out-put of pattern recognition receptor triggered signaling leading to the activation of a core pro-inflammatory signal transduction pathway , which appears to be equally responsive to the different bacterial clones tested . Overall our results show that the MUC clones , which overproduce colanic acid and dominated the bacteria populations during the interaction with MΦ , exhibit increased virulence . Given the phenotypes of the MUCs and their dynamics , we sought to determine the molecular basis of the mutations responsible for their increase in frequency along the evolutionary process . Whole genome sequencing of a clone sampled from M3 population at day 19 of the evolution process ( MUC_M3_D19 ) revealed that it carries two transposon insertions , i . e . a IS186 insertion into the promoter region of lon and one IS1 insertion upstream of the yrfF gene ( see Table 1 ) . The IS1 insertion event occurred in all sequenced clones sampled at day 30 ( Table 1 , Fig . 4A ) , revealing parallelism at the genetic level across all independently evolved lines . The function of the yrfF gene is unknown in E . coli , but its homologue in Salmonella , i . e . igaA , prevents over-activation of the Rcs regulatory system , which regulates colanic acid capsule synthesis [26] . It is therefore likely that the insertion upstream of yrfF alters E . coli ability to produce colanic acid , in keeping with the observation that MUC clones produce high levels of colanic acid , as compared to ANC bacteria ( Fig . 2C ) . Other important parallelisms ( observed in 3 out of 6 populations analyzed ) include two transposition events , namely , one in yiaW coding region and the other in the coding region of the pot operon . potD is one of the four genes of the potABCD operon , a spermidine-preferential uptake system [27] . All four genes are essential for spermidine uptake , indicating that the insertions in potD detected in clones MUC_M4_D30 and MUC_M6_D30 , or the insertion in potA observed in clone MUC_M3_D30 , are likely to impair uptake of spermidine . We tested the effect of polyamines in the evolved MUC clones and observed that while all exhibit a growth advantage in the presence of spermidine , the clones with insertions in potD ( MUC_M4_D30 and MUC_M6_D30 ) exhibit an increased growth advantage compared to the other MUC and the ancestral , in the presence of spermine ( Fig . S5 ) . During adaptation to MΦ , insertion in yiaW ( whose function is unknown ) was followed rapidly by insertion in potA or potD genes ( see Fig . 4B , M3 , M4 and M6 populations ) , indicating a potential interaction between these two events . This parallelism was observed in populations exposed to MΦ and not in bacteria that evolved in the absence of MΦ , suggesting that insertions in yiaW contribute functionally to adaptation of E . coli to MΦ . Given that many of the adaptive mutations observed under the different forms of stress imposed by MΦ were caused by IS insertions , we tested if the frequency of spontaneous mutations ( including IS insertions ) is higher in the presence versus absence of MΦ in the ancestral strain . No significant differences were found , suggesting that selection was the main force driving the increase in frequency of IS elements ( see Text S1 and Fig . S6 ) . Other parallelisms were observed at the level of point mutations in two clones with the same non-synonymous SNP in fusA , a gene coding for elongation factor G , which catalyzes the elongation and recycling phases of translation [28] . Mutations in fusA reduce the rate of protein synthesis , a hallmark of stress responses , with pleiotropic effects on bacterial physiology [29] . Mutations in fusA have also been related with the development of SCVs in S . aureus [30] . We sequenced fusA in our in vitro evolved E . coli SCVs ( 11 clones sampled from M1 population at day 8 and 10 clones sampled at day 4 ) but did not find any substitutions in this gene . To further understand the dynamics of adaptation in each independent evolved bacterial population , we sought to determine the frequency of the mutations found ( see Table 2 ) , in clones sampled along the evolution experiment . Adaptation involved the competition between distinct haplotypes and the successive accumulation of beneficial mutations , mainly caused by IS insertions ( Fig . 4B ) . Such haplotype dynamics is characteristic of clonal interference [3] , where clones carrying distinct beneficial mutations compete for fixation . We modeled this process , within the basic ecological scenario of our experiment ( see Fig . 5 and Text S1 , Fig . S7 to S11 ) , fully reproducing the complex dynamics of the mucoid and non-mucoid phenotypes observed in Figure 1B . An IS186 insertion into the promoter region of lon , was observed in clones sampled from populations M3 and M4 ( Fig . 4 ) . Lon ( Long Form Filament ) is a heat shock protease responsible for degradation of defective proteins in the cell [31] . The promoter region of lon is a hotspot for IS186 insertions [31] , which may contribute to the occurrence of this mutation in independently evolved clones . We tested if the proportion of spontaneous lon::IS186 mutants is higher in the presence versus absence of MΦ , however no difference was observed ( see Text S1 ) . As lon mutants tend to overproduce colanic acid [32] , a trait that appears to be strongly selected for in our experimental system , it is possible that this was the main beneficial effect caused by the insertion . However , the IS186 insertion could only be detected at intermediate time points in the experiment and not at day 30 ( see Fig . 4B ) . Interestingly , lon has been reported to be a mutator gene in mutants that bear an IS186 insertion in its promoter , thus increasing the rate of IS transpositions 10- to 100-fold [33] . This happens because the stability of several transposases is dependent on the Lon protease [34] , [35] , which seems to regulate their transposition activity . We tested MUC_M3_D19 for increased mutagenesis . This clone carries an IS186 inserted in −10 promoter region of lon and since mutations in this position were shown to significantly decrease level of lon transcription [36] , it is likely that it could be a mutator . If so this could contribute to the burst of transposition events that occurred during adaptation . We found a significant increase in the frequency of D-cycloserine resistant clones in MUC_M3_D19 relative to the ancestral non-evolved clone ( median frequency 2 . 6×10−6 vs . 1×10−7 , for the ancestral background , P = 5 . 5×10−13 , W = 203 . 5 , Mann-Whitney U test , Fig . S12 ) . Consistent with this increased mutagenesis being driven by IS insertions , no significant differences in the frequency of rifampicin resistant clones , which are caused by point mutations , were observed ( median frequency 3 . 3×10−7 vs 6 . 9×10−7 for the ancestral background , P = 0 . 1 , W = 21 , Mann-Whitney U test ) . The presence of IS186 in the lon promoter region was also found to be highly unstable . A spontaneously derived non-mucoid clone from MUC_M3_D19 ( MUC_M3_D19_REV ) shows a precise excision of this element , while maintaining the IS1 insertion in regulatory region of yrfF ( see Table 1 ) . These results indicate that this IS186 insertion enhances mucoidy levels , increases mutagenesis and is also very unstable in this genetic background . The latter may explain why it did not fix in the populations . The dynamics of the IS186 insertion in populations M3 and M4 suggest that this mutation was beneficial in the background with an IS1 insertion upstream of the homologue of igaA . Support for a selective advantage of this mutation is suggested by the observation that , in Salmonella , the transcription of igaA ( yrfF in E . coli ) is regulated by lon [37] .
Bacterial evolution towards pathogenicity may occur through the acquisition of new genes – a gain of function mechanism- or modification of their current genomes , including loss of genes - change-of-function mechanism [38] . The later constitutes a pathoadaptation , in which mutations enhance bacterial virulence without horizontal transfer of specific genes . For example , the deletion of hemB in Staphylococcus aureus increases its ability to persist intracellularly [39] while the loss of mucA increases Pseudomonas aeruginosa ability to evade phagocytosis and resist to pulmonary clearance [40] . We followed the evolution of a commensal strain of E . coli under the selective pressure imposed by MΦ phagocytosis , to determine the rate of adaptive evolution and to uncover the nature of possible E . coli pathoadaptive mutations . From the infection dynamics and the fitness assays ( Fig . 1B and 2 ) , we conclude that at least two different adaptations , detected by the emergence different colony morphologies , occurred , namely , i ) an intracellular advantage evolved by SCV clones early in the process; ii ) an extracellular advantage evolved by MUC clones emerging later . The intracellular adaptation is characterized by increased bacterial resistance , plasticity and survival in the early phase of interaction with MΦ , and was accompanied by a reduced extracellular growth . The extracellular adaptation is associated with overproduction of colanic acid and characterized by increased resistance to MΦ phagocytosis . The functional link between overproduction of colanic acid and escape from phagocytosis is likely but remains to be formally established . Overtime this phenotype dominated all populations . The mutations acquired by commensal E . coli adapting to MΦ , occurred within a few hundred generations and were characterized by traits reminiscent of those found in pathogenic bacteria . Clinical isolates sampled from patients suffering from recurrent and persistent infections in the blood [41] or urinary tract [21] , [42] , are SCVs . The distinctive traits of this phenotype are: i ) ability to form small colonies , to revert to larger colony forming bacteria at high frequencies and ii ) increased resistance to certain antibiotics . In S . aureus SCVs have been implicated as an intermediate form before mutations in gyrA occur to produce ciprofloxacin resistance [43] . In addition , Besier et al . have reported thyA mutant S . aureus SCVs show hypermutator status [44] . These findings suggest that SCVs could potentiate the emergence of mucoid clones , which latter go on to dominate the populations . However , we did not detect in SCVs the mutations found in the mucoid clones , indicating a distinct molecular basis for the SCV phenotype , an issue that we will investigate in future work . Given that mucoidy is also frequently observed in certain infections [22] , [23] , our finding that this trait can rapidly emerge under the selective pressure of MΦ , may have implications not only for the understanding of host-microbe interactions but also for the treatment of bacterial infections . Interestingly mucoidy can also be selected by the pressure imposed by phages in different bacterial species [45] , [46] . Whether mucoid strains evolved to resist to phages also exhibit increased virulence remains to be established . Translocation of commensal E . coli from the gut can be associated with severe health complications ( e . g . sepsis ) , particularly in immunosuppressed hosts or after surgery [47] , [48] . Bacteria that reach the mesenteric lymph nodes or the peritoneal cavity ( extensively populated by MΦ ) and that are able to escape MΦ should have a fitness advantage and potentially cause more severe disease . Indeed , we found that increased ability to escape MΦ of in vitro evolved clones lead to increased pathogenesis in vivo , when tested in a mouse model . We also found that this pathoadaptative process was characterized by three main paths . Although distinct in the number and type of mutations , these share an initial mutation: an IS insertion upstream of yrfF , a gene which shares 84% sequence similarity at the protein level to IgaA of Salmonella enterica serovar Typhimurium . In S . Typhimurium it was shown that the stability and responsiveness of the RcsCDB system depends on IgaA [49] . The RcsCDB system controls the production of colanic acid , virulence in diverse pathogens [24] , [50] , [51] , [52] , [53] , modulates responses to environmental changes and is activated upon exposure to antimicrobial peptides [54] , [55] , [56] , [57] . IgaA represses the RcsCDB system [58] and mutations causing instability of IgaA activate the RcsCDB system , leading to overproduction of colanic acid capsule ( mucoid phenotype ) [58] . Given the repressive function of IgaA on RcsCDB , which controls many traits likely to be important for bacterial fitness , it is likely that the observed IS insertion upstream of yrfF is an adaptive mutation with pleiotropic effects . If so the adaptive path may proceed through the occurrence of new mutations , which may compensate for the pleiotropic effects of that first adaptive step . Interestingly , the same amino-acid substitution in fusA occurred in two independent lines . FusA is an elongation factor and is part of the str operon of E . coli , which has 3 other genes: rpsL , rpsG and tufA . Since the strain that we studied carries a mutation in rpsL that confers streptomycin resistance , which is costly in RPMI yet increases survival inside MΦ [59] , it is possible that the SNP in fusA could be compensatory to cost of the rpsL mutation in the milleu outside MΦ . One of the adaptive paths taken by E . coli included insertions into the coding regions of yiaW and potA or potD . While the function of yiaW is unknown , the later genes are involved in spermidine transport , which may affect E . coli interaction with MΦ . Spermidines are polyamines , polycationic molecules , which interact with nucleic acids and have been described as important in escape from phagolysosomes , biofilm formation and protection from oxidative and acidic stress amongst other traits important in bacterial pathogenesis [60] . The adaptive process was also marked by the occurrence of an IS186 insertion into the promoter region of the Lon protease . Such insertion was not only likely adaptive ( it was observed in two independent lineages and it increases mucoidy ) , but also likely leads to increased rates of transposition . Given that many of the adaptive mutations observed under the stresses imposed by MΦ were caused by ISs , these may constitute an example of Barbara McClintock proposal that transposable element movement under stress could aid organisms to adapt to new environments [61] . The mechanisms via which different mutations underlying E . coli pathoadaptation increase its virulence remain to be established . It is likely however , that such mechanisms would interfere with one or two host defense strategies against infections [62] . Presumably , by escaping MΦ killing pathoadaptation should provide MUC clones with a proliferative advantage , ultimately compromising host survival . This should be revealed by increased bacterial burden in the MUC infected hosts , as compared to hosts infected with non-evolved E . coli clones , revealing a compromise in host resistance [62] . An alternative , but not mutually exclusive , interpretation would be that pathoadaptation is associated with the induction of a immunopathologic response compromising host survival , irrespectively of pathogen burden . This should be revealed by similar bacterial burdens in the MUC infected host , as compared to hosts infected with non-evolved E . coli clones , revealing a compromise in host disease tolerance [62] . While critical to further understanding of the mechanism via which E . coli pathoadaptation increases its virulence , this is beyond the scope of the current study . In conclusion , we demonstrate that E . coli can adapt to better resist to MΦ within a few hundreds of generations and that clones with different morphologies and traits similar to those of pathogenic bacteria rapidly emerge . This pathoadaptive process and the complex dynamics of the evolved phenotypes can be reasonably described by a model of clonal interference , where distinct haplotypes , carrying new transposon insertions and other mutations , increase in frequency and compete for fixation .
All experiments involving animals were approved by the Institutional Ethics Committee at the Instituto Gulbenkian de Ciência ( project nr . A009/2010 with approval date 2010/10/15 ) , following the Portuguese legislation ( PORT 1005/92 ) which complies with the European Directive 86/609/EEC of the European Council . The RAW 264 . 7 murine macrophage cell line was maintained in an atmosphere containing 5% CO2 at 37°C in RPMI 1640 ( Gibco ) supplemented with 2 mM L-glutamine ( Invitrogen ) , 1 mM sodium pyruvate ( Invitrogen ) , 10 mM hepes ( Invitrogen ) , 100 U/ml penicillin/streptomycin ( Gibco ) , 50 µM 2-mercaptoethanol solution ( Gibco ) , 50 µg/ml gentamicin ( Sigma ) , with 10% heat-inactivated FCS ( standard RPMI complete medium ) . Before infection assays , MΦ were cultivated for 24 h in the same medium as before except for the three antibiotics which were now replaced by 100 µg/ml streptomycin antibiotic ( RPMI-Strep medium ) . All bacterial cultures were also done in RPMI-Strep medium , except if stated otherwise . The Escherichia coli strains used were MC4100-YFP and MC4100-CFP ( MC4100 , galK::CFP/YFP , AmpRStrepR ) which contain the yellow ( yfp ) and cyan ( cfp ) alleles of GFP integrated at the galK locus in MC4100 ( E . coli Genetic Stock Center #6152 ) and differ only by YFP/CFP locus that is constitutively expressed [63] . MC4100-CFP strain was used for the evolution experiment and MC4100-YFP as a reference strain for the fitness assays . Twelve populations were founded from a single MC4100-CFP clone and were therefore genetically uniform in the beginning of the experiments . All populations evolved in RPMI , 6 populations in the presence of the MΦ ( M1–M6 ) and the other 6 ( C1–C6 ) in the absence of MΦ . Before each infection cycle , MΦ ( 0 . 7×106 to 1 . 3×106/ml ) were centrifuged at 1200 rpm for 5 min , re-suspended in RPMI-Strep medium and activated with 2 µg/ml CpG-ODN 1826 ( 5′TCCATGACGTTCCTGACGTT 3′ - Sigma ) for 24 h [64] . Cells were then centrifuged ( 1000 rpm for 5 min ) , re-suspended in 3 ml of fresh RPMI-Strep medium and seeded in 12-well microtiter plates ( 0 . 8×106 to 1 . 6×106/ml ) . Subsequently , they were incubated at 37°C for 2 h , washed in RPMI-Strep and infected with a MOI of 1∶1 ( 106 bacteria to 106 MΦ ) . After 24 hours of infection , MΦ were detached with cell scraper and the whole culture was centrifuged at 4000 rpm for 10 min to pellet cells . This procedure lyses MΦ releasing intracellular bacteria . Then these were washed twice with phosphate-buffered saline ( PBS ) and counted by flow cytometry using a FACscan cytometer ( Becton Dickinson ) . Approximately 106 of recovered bacteria were used to infect new activated MΦ in the same manner as before . The same procedure was applied to control populations , except that 104 bacteria were transferred daily . This is because after 4 hours of infection with the MΦ bacteria numbers drop to 104 . This adjustment results in similar number of generations in both environments . In both treatments ( with and without MΦ ) , bacteria were allowed to propagate for approximately 15 generations per day . Generation time is estimated as: G = log2 ( Nf/Ni ) , where Ni is the initial number of bacteria and Nf is the final number of bacteria . Nf was approximately 6×108 in both treatments . Evolution occurred during approximately 450 generations in both environments . We note that in the context of a real infection repeated contact with macrophages will not likely occur with a similar period as the one in this experimental setup . To estimate competitive fitness of M1–M6 populations , after 285 and 450 generations of evolution , each evolved population was competed against MC4100-YFP reference strain in the same conditions as used in the evolution experiment . Both evolved and ancestral strains were grown separately in RPMI-Strep , 106 cells of each type were used to inoculate the competition plate . The initial and final ratios of both strains were determined by Flow cytometry . The fitness of each population was measured 3 times and the fitness of the ancestral strain 10 times to confirm the neutrality of the marker . A measure of relative fitness increase , expressed as selection coefficient , was estimated as:[65] where Scoeff is the selective advantage of the evolved strain e over the ancestral strain a , Nfe and Nfa are the numbers of evolved ( e ) and ancestral ( a ) bacteria after competition and Nia and Nie are the initial numbers , before the competition . Bacterial uptake was measured by the gentamicin protection assay as previously described [66] , with modifications , as follows . MΦ were infected at MOI 1∶1 as described above to determine the number of intracellular and extracellular bacteria after 3 h of incubation . The number of extracellular bacteria at 3 h of incubation was estimated by taking a sample of the culture medium ( without detaching the MΦ ) , centrifuging ( 4000 rpm for 10 min ) to pellet the cells and finally washing these in PBS prior to plating on LB agar plates . The number of intracellular bacteria was estimated by washing infected MΦ twice with PBS and adding fresh medium containing 100 µg of gentamicin/ml to kill extracellular bacteria . After incubation for an additional hour , the medium was removed , the monolayer of macrophages was washed 3 times with PBS , detached using a cell scraper and centrifuged ( 4000 rpm for 10 min ) to pellet the cells . These were further resuspended in PBS and the appropriate dilution was plated on LB agar plates to determine the number of intracellular bacteria . Relative abundance ( Rr ) of evolved clones to that of the ancestral in intracellular or extracellular environment of MΦ was estimated as:where N3he and N3ha are the numbers of evolved ( e ) and ancestral ( a ) bacteria at 3 hours post infection ( in the intracellular or extracellular niche of MΦ ) and Nia and Nie are the initial numbers of evolved ( e ) and ancestral ( a ) bacteria used for inoculation . To measure numbers of MΦ that are alive , the same infection protocol was performed . However , after 3 h of infection , MΦ were washed from extracellular bacteria twice with RPMI , detached and counted by Trypan blue exclusion test [67] ( see Fig . S3 ) . The method used to extract colanic acid was based on a procedure described previously [68] . Briefly , 50 ml of a bacterial cell culture was heated for 15 min at 100°C to denature EPS-degrading enzymes , cooled down and centrifuged at 13200 rpm at 4°C for 30 min . Then 40 ml of the supernatant was precipitated by addition of three volumes of ethanol . The mixture was maintained at 4°C overnight and centrifuged again at 13200 rpm at 4°C for 30 min . The resulting pellet was dissolved in 5 ml of distilled water , dialyzed for 48 h against distilled water ( membrane MWCO , 3500 Da ) and dried in SpeedVac . Residual polypeptides were removed by precipitation with 5 ml of 10% ( v/v ) trichloroacetic acid and centrifuged at 13200 rpm at 4°C for 30 min . The supernatant was dialyzed for five days against distilled water and dried . The resulting preparation was resuspended in 1 ml of distilled water . Quantification of colanic acid was carried out by measuring non-dialyzable methylpentose ( 6-deoxy-hexose ) , namely fucose , which is a specific component of this exopolysaccharide . 10 to 100 µl of the colanic acid preparation were diluted to 1 ml with distilled water , and mixed with 4 . 5 ml of H2SO4/H2O ( 6∶1; v/v ) . The mixture was prepared at room temperature , then heated at 100°C for 20 min , and finally cooled down to room temperature . For each sample , absorbance at 396 nm and 427 nm was measured either directly ( control sample ( A-co ) ) or after addition of 100 µl of 0 . 3% freshly prepared cysteine hydrochloride ( cysteine sample ( A-cy ) ) . The absorption due to the unspecific reaction with H2SO4 was subtracted from the total absorption of the sample: A396-co and A427-co were subtracted from A396-cy and A427-cy , respectively , to obtain ΔA396 and ΔA427 . Values of ( ΔA396–ΔA427 ) were directly correlated to methylpentose concentration by using a standard curve obtained with a fucose concentration ranging from 2 µg/ml to 100 µg/ml ( Fig . S2 ) . To determine the reversion frequency of SCV to the ancestral phenotype , single colonies grown on LB agar plates were resuspended in PBS , the appropriate dilution was plated on LB agar plates and incubated at 37°C . After 48 h small and large colonies were counted [21] . To test for the auxotrophy to hemin , individual SCV colonies were isolated , resuspended in PBS and plated on M9 minimal medium agar plates containing 2% glucose with and without hemin 50 µg/ml ( Sigma-Aldrich ) . After incubation at 37°C for 48 h , CFUs were counted to estimate the ratio between the number of cells able to grow in presence and in absence of hemin . Both the ancestral and 7 isolated MUC clones ( MUC3_d19 sampled from population M3 after 19 days of evolution with macrophages and MUC1 to MUC6d30 sampled from M1 to M6 pops after 30 days of evolution ) were grown overnight in 10 ml of RPMI-Strep at 37°C . DNA isolation from these cultures was done following a previously described protocol [69] . The DNA library construction as well as the sequencing procedure was carried out by BGI . Each sample was pair-end sequenced on an Illumina HiSeq 2000 . Standard procedures produced data sets of Illumina paired-end 90 bp read pairs with insert size ( including read length ) of ∼470 bp . Mutations in the two genomes were identified using the BRESEQ pipeline [70] . To detect potential duplication events we used SSAHA2 [71] and the paired-end information to map reads only to their best-match on the genome . Sequence coverage along the genome was assessed with a 250 bp window and corrected for GC% composition by normalizing by the mean coverage of regions with the same GC% . We then looked for regions with high differences ( >1 . 4 ) in coverage . We did not find any such difference between the ancestral and evolved clones . See Table 2 for the identity and precise location of mutations identified in the sequenced clones . All mutations were confirmed by direct target sequencing . In order to determine the frequency of the mutations in clones sampled along the experiment , DNA was amplified by PCR ( to identify IS insertions ) and sequencing PCR was performed ( to identify SNPs ) . DNA was amplified by PCR in a total volume of 50 µl containing 1 µl bacterial culture , 10 µM of each primer , 200 µM dNTPs , 0 . 5 U Taq polymerase and 1× Taq polymerase buffer . The amplification profile was 15 min at 95°C , followed by 35 cycles at 94°C for 30 s , 60°C for 90 s , 72°C for 2 min with a final extension at 72°C for 10 min . All gene fragments were amplified using these conditions and oligonucleotide primers ( Table S2 ) . The same primers were used for sequencing straight from the PCR product . We maintained male C57/BL6 mice , aged 8–10 weeks ( in house supplier , Instituto Gulbenkian de Ciência ) , on ad libitum food ( RM3A ( P ) ; Special Diet Services , UK ) and water , with a 12 hour light cycle , at 21°C . We initiated infections by intra-peritoneal inoculation of bacteria in 100 µl saline . Several groups of mice were injected with different bacterial strains at doses ranging from 2×105 to 3×108 ( sample sizes: ancestral – n = 46; control – n = 41 , mucoid – n = 50 ) . At doses 1×107 , 5×107 and 1×108 , we injected a minimum of 10 mice , in at least two independent experiments ( data from the same animals was used for the Kaplan-Meier curves in Fig . 3C ) . The inocula consisted of the following: a single clone for ancestral bacteria ( ANC ) , consisted of a mixture of equal numbers of the 6 sequenced clones from day 30 ( MUC1-MUC6; see Fig . 4 ) for the mucoids ( MUC ) and mixture of 6 independent clones evolved in the absence of macrophages ( CON ) . Furthermore , as a control , in each experimental block we injected a group of 2–3 mice with 100 µl of saline ( these animals did not display any signs of disease ) . We monitored mice for a period of 6–10 days ( twice a day for the first two days and daily for the remaining 8 days ) and measured weight and temperature . To estimate the LD50 values ( Fig . 3A–B , E ) , we fitted a binomial generalized linear model ( GLM ) for each morphotype , using survival as a response variable and log10 bacterial dose as explanatory variable ( following [72] ) . To analyze the temporal dynamics of mortality in mice infected with MUC or ANC at the MUC LD50 ( Fig . 3C ) , we used Kaplan-Meier curves followed by a log-rank test . Finally , we used GLMs to test whether the variation maximum reduction in temperature or weight could be explained by the infecting strain . The statistical analysis was performed using the R software: http://www . r-project . org/ .
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The selective pressure imposed by the host immune system is an important component of microbial adaptation from commensalism to pathogenicity . We used experimental evolution to study the initial steps of the adaptation of Escherichia coli to cells of the innate immune system , i . e . , macrophages . Our results demonstrate that bacteria can evolve remarkably fast , and acquire adaptations increasing survival inside macrophages and/or ability to escape engulfment . The mechanism underlying this pathoadaptive process involves the accumulation of mutations caused by transposon insertions , increasing pathogenicity in vivo . These findings reveal the remarkable fast pace at which bacteria can evolve to escape a central component of the host innate immunity , namely macrophages .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2013
|
The Genetic Basis of Escherichia coli Pathoadaptation to Macrophages
|
Loop-mediated isothermal amplification ( LAMP ) is at the forefront of the search for innovative diagnostics for human African trypanosomiasis ( HAT ) . Several simple endpoint detection methods have been developed for LAMP and here we compare four of these: ( i ) visualization of turbidity; ( ii ) addition of hydroxynaphthol blue before incubation; ( iii ) addition of calcein with MnCl2 before incubation and ( iv ) addition of Quant-iT PicoGreen after incubation . These four methods were applied to four LAMP assays for the detection of human African trypanosomiasis , including two Trypanozoon specific and two Trypanosoma brucei rhodesiense specific reactions using DNA extracted from cryo-preserved procyclic form T . b . rhodesiense . A multi-observer study was performed to assess inter-observer reliability of two of these methods: hydroxynapthol blue and calcein with MnCl2 , using DNA prepared from blood samples stored on Whatman FTA cards . Results showed that hydroxynaphthol blue was the best of the compared methods for easy , inexpensive , accurate and reliable interpretation of LAMP assays for HAT . Hydroxynapthol blue generates a violet to sky blue colour change that was easy to see and was consistently interpreted by independent observers . Visible turbidity detection is not possible for all currently available HAT LAMP reactions; Quant-iT PicoGreen is expensive and addition of calcein with MnCl2 adversely affects reaction sensitivity and was unpopular with several observers .
Loop-mediated isothermal amplification ( LAMP ) [1] is a DNA amplification technique whose advantages over traditional PCR have put it at the forefront of the search for innovative new diagnostics for infectious diseases such as human African trypanosomiasis ( HAT ) [2] . Rapid and unambiguous visual discrimination of test results is essential for diagnostics and several simple endpoint detection methods have been developed for the LAMP method to allow visual discrimination of positive samples . These methods vary in cost and technological details . The Trypanosoma brucei s . l . species complex includes both agents of HAT namely Trypanosoma brucei rhodesiense and Trypanosoma brucei gambiense . Two LAMP assays have recently been developed for the detection of all members of the sub genus Trypanozoon , which includes Trypanosoma brucei s . l . One of the assays targets the single copy paraflagellar rod protein A ( PfrA ) gene [3] and the other the mobile genetic element ( RIME ) [4] . Further , Thekisoe et al . have described a LAMP assay for the detection of T . b . gambiense which targets the 5 . 8S rRNA region [5] . This is controversial since the only widely accepted marker for specific identification of T . b . gambiense is the T . gambiense specific glycoprotein ( TgsGP ) gene [6] , [7] . A T . b . rhodesiense specific LAMP assays which targets the serum resistance associated ( SRA ) gene , has also been developed [8] . In addition a second LAMP assay for the SRA gene has been developed in our laboratory ( Wastling and Picozzi , unpublished observations ) . Positive LAMP reactions can be distinguished by visible turbidity in the reaction tube , corresponding to the production of magnesium pyrophosphate , a by-product of DNA amplification [9] . This method was reported to provide immediate visual discrimination of the LAMP output for the PfrA assay [3] but neither the RIME nor SRA1 assays generate visible turbidity [4] , [8] . Quant-iT PicoGreen [10] is a DNA intercalating dye that can be added to the LAMP reaction tube post-incubation . In the presence of LAMP amplification product the dye shows an orange to green colour change and fluoresces under UV light , however the requirement of tube opening to add the dye is a contamination prone step . In contrast calcein , manganese chloride ( MnCl2 ) , and hydroxynaphthol blue can be added before incubation . Calcein and hydroxynaphthol blue are metal ion indicators , which respond to alterations in the chemical composition of the reaction mix as DNA amplification proceeds . Calcein and MnCl2 are reported to produce an orange to green colour change , and to fluoresce under UV light [11] , while hydroxynaphthol blue produces a colour change , from violet to sky blue [12] . In the present work four LAMP assays for the detection of human-infective African trypanosomes were compared; the published PfrA , RIME and SRA assays as a well as a novel LAMP assay for the SRA gene developed in our laboratory . Henceforth the published and unpublished LAMP SRA assays will be referred to as SRA1 and SRA2 respectively . Firstly , purified trypanosome DNA was used to establish the ease of use and sensitivity of each method . Up to this point all endpoint interpretations were made by one observer . Since the performance of a subjective diagnostic method depends on sample variation in readers , as well as cases [13] , we performed a multi-observer study to investigate the reliability of the two metal ion indicator methods . These methods were chosen because they are cheap and offer a closed system format .
Four LAMP assays were used . The PfrA [3] and RIME LAMP [4] tests specific for the sub-genus Trypanozoon and the SRA LAMP [8] for T . b . rhodesiense , herewith referred to in this publication as SRA1 were performed following the published conditions . In addition a novel SRA LAMP test ( Table 1 ) targeting SRA sequence ( GenBank accession number AF097331 ) was developed and evaluated . The LAMP PfrA , RIME and SRA1 assays were performed as described [3] , [4] , [8] except where the reaction buffer detergent was altered between 0 . 1% Tween 20 and 0 . 1% Triton X-100 . Originally LAMP PfrA was run using Tween 20 , whereas RIME LAMP , SRA1 and SRA2 used Triton X-100 . For the SRA2 LAMP each 25 µl reaction contained 1µl template DNA , 8U Bst DNA polymerase ( New England Biolabs ) , 2 . 5µl Thermopol reaction buffer I ( New England Biolabs ) with additional MgSO4 to give 20 mM Tris-Cl , 10 mM KCl , 10 mM ( NH4 ) SO4 , 8 mM MgSO4 and 0 . 1% Triton X-100 , 0 . 8 M betaine , 1 . 4 mM dNTPs , 2 µM of both FIP and BIP , 0 . 2 µM of both F3 and B3 and 0 . 8 µM of both LF and LB . The reaction was carried out at 62°C for 1 hour , terminated at 80°C for 4 minutes and held indefinitely at 4°C . The four detection formats were first compared using T . b . rhodesiense DNA extracted from cryopreserved procyclic form trypanosomes using a QiaAMP DNA Blood Midi Kit ( Qiagen , UK ) . The concentration of the T . b . rhodesiense DNA sample was measured using a NanoDrop spectrophotometer . A 10-fold dilution series was made ranging from a 1 in10 to a 1 in 10 , 000 , 000 dilution to determine the detection limit for each format-assay combination . Turbidity was investigated using the LAMP PfrA and LAMP SRA2 assays , both of which were performed with Triton X-100 in the reaction buffer ( a minor modification to the published LAMP PfrA format ) . Visible turbidity detection is not possible for LAMP RIME or LAMP SRA1 . Each assay was performed once on the T . b . rhodesiense DNA dilution series described above . Post-reaction turbidity was assessed by eye . It was scored as positive or negative in comparison to a positive and negative control . The reaction products were then subjected to gel electrophoresis for approximately 15 min at 100 V using a 1% ( w/v ) agarose gel containing GelRed ( Biotium , UK ) . The positive LAMP reactions appear as a ladder of bands upon UV illumination . The turbidity and gel electrophoresis results were directly compared . All four assays were performed in duplicate using the T . b . rhodesiense DNA dilution series described above with Triton X- 100 in the reaction buffer ( a minor modification to the published LAMP PfrA format ) . After the full LAMP reaction incubation time 5 µl of the LAMP product was aliquoted for gel electrophoresis , as above . Then , 2 µl Quant-iT PicoGreen was added to one replicate and 5 µl Quant-iT PicoGreen was added to the second . The colour under indoor light was assessed by eye and recorded and flourescence under UV was also observed , before the colour , fluorescence and gel electrophoresis results were compared . The four assays were performed once on the T . b . rhodesiense DNA dilution series described above . with Triton X-100 in the reaction buffer ( a minor modification to the published LAMP PfrA format ) . Before incubation 120 µM hydroxynaphthol blue was added to each 25 µl reaction mix . Upon termination the colour was assessed by eye , under indoor light , before the LAMP products were assessed by gel electrophoresis . Colour and gel electrophoresis results were compared . All four assays were performed in triplicate with both reaction buffer compositions ( 0 . 1% Tween 20 and 0 . 1% Triton X-100 ) using the T . b . rhodesiense DNA dilution series described above . Before incubation 25 µM calcein and 0 . 5 mM MnCl2 were added to each 25 µl reaction mix . Upon termination the colour was assessed by eye , fluorescence under UV observed and the LAMP products were assessed by gel electrophoresis . Colour , flourescence and gel electrophoresis results were compared . Sixty blood samples from human sleeping sickness patients were spotted on to Whatman FTA cards and prepared as described above to obtain DNA eluate . Two 1 . 2 mm discs were taken from each blood spot and eluted into a final volume of 50 µl chelex solution . The SRA2 LAMP assay was performed , in duplicate , for each sample . The first reaction included hydroxynaphthol and the second included calcein and MnCl2 , and Tween- 20 instead of Triton X-100 . Five µl of product from each LAMP reaction was electrophoresed through a 1% ( w/v ) agarose gel , containing GelRed ( Biotium , UK ) at 100 V . When viewed under UV light , positive LAMP reactions were seen as a long DNA ladder . Thirty three observers each scored all 60 samples as positive or negative by comparison with a positive and negative control , after training on a small batch of 8 samples . The participants were not chosen according to any specific criteria but were asked about their work background , previous experience of colour change assays and their impressions of these tests in light of their previous experience . They were asked to rank how easy they found it to see the colour difference ( very easy , quite easy , quite difficult or very difficult ) , and how many of the samples were easy to rate as positive or negative ( all , most , some , very few or none ) , for each method . They were also asked which colour change method they found easiest to use . Finally they were given the opportunity to make any general comments . The results from each observer were compared to the status of each sample , as defined by gel electrophoresis and UV illumination . The agreement between each observer and the gel result was quantified using Cohen's kappa statistic ( κ ) [14] . The overall level of agreement between all observers was then quantified [15] for both colour change detection methods . Samples were collected as part of ongoing national sleeping sickness surveillance by the Ministry of Health in Uganda . Ethical permission was obtained from Uganda National Council for Science and Technology ( UNCST ) as well as the District Health Authorities of Uganda .
Positive LAMP PfrA reactions were detectable under visible turbidity up to and including a 1×10−4 dilution of the T . b . rhodesiense DNA . This was equal to the detection limit seen when the same reaction products were visualised by UV illumination after gel electrophoresis . Similarly , positive LAMP SRA2 reactions were detectable by visible turbidity up to and including a 1×10−3 dilution of the T . b . rhodesiense DNA . This was equal to the detection limit seen when the same reaction products were visualised by UV illumination after gel electrophoresis . Therefore the same results were seen with turbidity and gel electrophoresis for these assays . However visible turbidity detection is not possible for LAMP RIME or SRA1 . These results are shown alongside the detection limits with other endpoint readout formats in Table 2 . Upon the addition of either 2 or 5 µl of Quant-iT PicoGreen to the LAMP reaction products positive reactions could be detected by an orange to green colour change visible under normal light , or by fluorescence under UV light . This was true for each assay , whose detection limits with these formats are shown in Table 2 . For LAMP RIME , SRA1 and SRA2 colour and fluorescence based detection showed perfect agreement with gel electrophoresis regardless of the volume of Quant-iT PicoGreen . For LAMP PfrA with 2 µl Quant-iT PicoGreen colour and fluorescence based detection disagreed with results by gel in two instances . First , with the 1×10−4 dilution gel positive but colour change and flourescence negative endpoints were seen . Second , with the 1×10−5 dilution gel negative but colour change and fluorescence positive endpoints were seen . With 5 µl of this reagent colour change and fluorescence based detection agreed with gel electrophoresis for all dilutions . These results reflect the author's personal feeling that colour and fluorescence were more easily discerned with 5 µl , rather than 2 µl of Quant-iT PicoGreen . An example of the colour change seen with Quant-iT PicoGreen can be seen in Figure 1 . Hydroxynaphthol blue also allowed positive reactions to be discerned under ambient light for all four LAMP assays ( Table 2 ) . Hydroxynaphthol blue induced colour change showed perfect agreement with detection by gel for all assays except SRA2 when one gel positive assay ( corresponding to the 1×10−4 dilution ) appeared violet ( negative ) . No false positives were seen with hydroxynaphthol blue versus the gel . Also , for the LAMP PfrA , RIME and SRA2 assays , inclusion of hydroxynaphthol blue had no effect on overall detection limit . However the sensitivity of the LAMP SRA1 was reduced when hydroxynapthol blue was added compared to those results seen with Quant-iT PicoGreen ( Table 2 ) . An example of the colour change seen with hydroxynaphthol blue can be seen in Figure 1 . The calcein and MnCl2 method was found to give results of variable quality . No positive LAMP amplification was seen with the RIME assay , with either reaction buffer , despite running each assay in triplicate for the full dilution series . Neither were any LAMP positive endpoints seen for the LAMP PfrA or LAMP SRA1 reactions with 0 . 1% Triton X-100 containing LAMP buffer , despite running each assay in triplicate for the full dilution series . Amplification was seen when 0 . 1% Tween 20 was used . For LAMP PfrA with 0 . 1% Tween 20 consistent amplification could be seen from the 1×10−2 dilution when the products were assessed by gel , but the colour change and fluorescence were ambiguous . For LAMP SRA1 two replicates were performed with 0 . 1% Tween 20 , one showed no amplification , and one amplified from 1×10−1 , 1×10−2 , 1×10−3 and 1×10−5 dilutions when the products were assessed by gel . Colour change and flouresence were seen for the first three of these amplifications . For LAMP SRA2 amplification was seen with both types of buffer , although the detection limit was variable across the three replicates for each buffer . For one replicate colour change and fluorescence were visible and agreed with the results by gel . For the second replicate 2/4 and 3/4 gel positive endpoints were seen by colour change and fluorescence respectively . For the third replicate there was no obvious difference between gel positive and negative reactions by colour or fluorescence . Furthermore , the inclusion of calcein and MnCl2 seemed to reduce the absolute sensitivity of the assay as compared to results seen with turbidity or Quant-iT Picogreen , where extra reagents were not added to the reaction mix . Consistent amplification from the 1×10−3 dilution was seen by turbidity , Quant-iT PicoGreen and hydroxynapthol blue . With calcein and MnCl2 this was reduced to 1×10−1 . An example of the colour change seen with Quant-iT PicoGreen can be seen in Figure 1 . Thirty three volunteers participated in this study . There was a strong occupational bias towards the biomedical sciences . Eighteen participants self defined as biological scientists , six were veterinarians ( of which , three also described themselves as biologists ) and one was a medical doctor . Nine participants did not place themselves into any of the above categories . Nine participants reported some previous experience with diagnostic tests , which require a colour or colour change to be observed , although in most cases this experience was limited . Twenty one of the participants were female , twelve were male . Age was not surveyed . All observers said that they found the violet to blue colour change , seen with hydroxynaphthol blue , easier to use than the orange to green colour change seen with calcein and MnCl2 . When asked , ‘In your opinion , how easy is it to see the violet to sky blue colour/orange to green difference ? ’ , 73% of observers found the colour change with hydroxynaphthol blue quite easy to see , whereas 94% of the observers found the colour change with calcein and MnCl2 very difficult to see . When asked ‘In your opinion , using this colour change , how many of the samples were easy to rate as positive or negative ? ’ all participants found most or some of the samples easy to score with hydroxynaphthol blue . Equally all participants found very few or no samples easy to score with calcein and MnCl2 . Of those who had some previous experience interpreting diagnostic tests both methods were generally considered to be less easy to interpret than their previous experiences . Eight observers commented on a difference in turbidity between the positive and negative controls for the calcein and MnCl2 method . Two participants found it easier to score the samples using this turbidity difference than with the colour difference seen with hydroxynaphthol blue . The participants were not closely questioned as to how they made their decision; it is possible that several more made their judgements on a similar basis . Cohen's kappa statistic ( κ ) [14] was used to compare the agreement of observers with the results according to gel electrophoresis . For hydroxynaphthol blue agreement ranged from κ = 0 . 145 to κ = 0 . 870 , averaging κ = 0 . 602 , and for calcein and MnCl2 agreement ranged from κ<0 to κ = 0 . 930 , averaging κ = 0 . 407 . For 66% of observers the agreement between the hydroxynaphthol blue colour change and the results seen by gel electrophoresis were better than agreement between the calcein MnCl2 colour change and gel . The method of Fleiss , Levin et al . [15] was used to quantify the agreement of all observers for each method . For hydroxynaphthol blue , a kappa value of 0 . 693 was significantly different from zero - or no agreement , ( P<0 . 0001 ) . For calcein and MnCl2 a kappa value of 0 . 209 was seen . This was also significantly different from zero ( P<0 . 0001 ) . It is clear that the agreement between many observers was much higher for the hydroxynapthol blue method .
LAMP is advocated as a low technology diagnostic tool for resource poor settings [2]; simple visual discrimination of the test result is perceived as an important factor in promoting the method as a straightforward diagnostic [12] . However LAMP results can be read in a variety of ways . Complex sequence specific [16] and high technology , real time turbidimetry [17] approaches are useful during assay design and optimisation . LAMP products may also be visualised directly following gel electrophoresis by UV transillumination . More simply , turbidity is generated as a by-product of DNA amplification [9] . Several colour change methods for reading the result within the reaction tube have also been developed , including the DNA intercalating dyes: Quant-iT PicoGreen [10] , SYBR Green I [18] , [19] and propidium iodide [19] and the metal ion indicator methods: calcein alone [20] , calcein with MnCl2 [11] and , most recently , hydroxynaphthol blue [12] . The metal ion indicators have provided the simplest approach to date; they are added alongside the other reagents , before incubation , so that amplification and detection are combined in single processing step , within a closed tube system . The colour changes can be visualised by eye , without special lighting , and they are inexpensive . One important caveat must be acknowledged; LAMP endpoint detection is not sequence specific whether by gel electrophoresis , turbidity or colour change dyes . Unlike PCR it is not possible to deduce the identity of the amplicon by examining the ladder-like pattern on the gel . Rather , once a given LAMP assay has been developed it is assumed that all reaction products correspond to the intended target . Amplification specificity is considered extremely high because LAMP primers must bind six distinct regions on the target DNA [1] , [12] . Turbidity enabled sensitive and specific endpoint discrimination for LAMP PfrA and SRA2 but is not visible for the LAMP RIME or SRA1 assays using published protocols . Quant-iT PicoGreen enabled easy visual endpoint discrimination for all assays . It was easiest to see the colour change with 5 µl ( rather than 2 µl ) of this reagent , but the extra cost is significant for this expensive reagent . Simple , sensitive and specific endpoint discrimination was also possible for all assays using the hydroxynaphthol blue method . For LAMP RIME , PfrA and SRA2 inclusion of hydroxynapthol blue did not affect the detection limit of the assay , confirming previous work [12] that this reagent does not inhibit the LAMP reaction . However , a ten fold reduction in detection limit was seen for the LAMP SRA1 assay with hydroxynapthol blue compared to assays without any additional reagents in the reaction mix . Further replicates would need to be made to confirm this observation . Goto et al . [12] also reported Mn2+ inhibition of LAMP , which may explain the total inhibition of LAMP RIME , and partial inhibition of LAMP PfrA , SRA1 and SRA2 assays found in the present work when calcein and Mn2+ was added . This work demonstrates that each detection method should be validated for any given LAMP assay . This is particularly important for the turbidity and metal ion indicator approaches , which detect changes in the chemical composition of the reaction mix rather than amplified DNA . Hydroxynaphthol blue was found to be the better of the two metal ion indicator methods tested , while the calcein and MnCl2 method reduced LAMP reaction sensitivity . Even so , the reliability of hydroxynaphthol blue should be confirmed across a larger set of independent observers . Inter-reader variability is an issue that has been largely ignored in the LAMP literature to date , and must be addressed where subjective endpoints are advocated as useful tools . In previous work endpoint interpretations have been made by one observer and to our knowledge no large scale , multi-observer studies have been performed with any of the LAMP endpoint detection methods . As Sadatsafavi et al . have emphasised , ‘any attempt to generalize the performance of a subjective diagnostic method should take into account the sample variation in both cases and readers’ [13]; they further highlight the need for a large group of observers to be used . We have performed a multi- observer study with 33 participants to investigate the reliability of the two metal ion indicator methods used in LAMP diagnostics . These methods were chosen for their advantages as closed system methods and their low cost . By contrast Quant-iT PicoGreen is expensive , and breaks the closed system . Turbidity was excluded based on the author's perception that it required a ‘trained eye’ to detect , although this may be a false assumption , and because it is not possible with all of the currently available HAT LAMP assays . The agreement across all observers was better for hydroxynaphthol blue tests ( κ = 0 . 693 ) than for calcein with MnCl2 ( κ = 0 . 209 ) , and , for 66% of observers the hydroxynaphthol blue to gel agreement was better than calcein MnCl2 to gel . All observers said they found the hydroxynaphthol blue colour change easier to see . Several observers commented on a turbidity difference between positives and negatives for the calcein-MnCl2 method . Six of the 11 observers whose calcein-MnCl2 to gel agreement was better than that for hydroxynaphthol blue commented on this turbidity difference . Thus the orange to green colour change may be even less reliable than this report suggests . The present work has shown that hydroxynaphthol blue is the better of the two metal ion indicator methods tested . Not only is it easier to see , but it also shows better inter- reader reliability and more consistent agreement with the presence of the DNA amplicon , assessed by gel . However , Cohen's kappa statistic for the agreement between the colour change as interpreted by one observer and amplicon detection by electrophoresis with UV illumination , ranged from κ = 0 . 145 to κ = 0 . 870 . Therefore the agreement between hydroxynaphthol blue and gel electrophoresis is variable and imperfect across many observers . We conclude that hydroxynaphthol blue could be well suited as a tool to increase the efficiency of large scale screening and monitoring efforts but when more than one individual is involved in LAMP screening , training and quality control will be necessary to reduce inter-observer variation . This process might be aided by generating a colour swatch card against which assay results can be compared , along the lines of a litmus paper test for pH . Better still , a quantitative colour measure , using some kind of spectrophotometric device would remove this variability , although we must not forget that we are seeking to establish a LAMP endpoint detection format that is suitable in a low resource setting . This work also provided a snapshot on the running costs for each fo the four LAMP detection formats . The Quant-iT PicoGreen format was the most expensive of the methods used with a cost of $353 for 100 reactions . By sharp contrast the use of turbidity , hydroxynaphthol blue and calcein with MnCl2 were less than $0 . 01 for 100 reactions ( Table 3 . ) . Cost is a major factor in endemic areas . As such hydroxynaphthol blue seems the most prefereable method when cost and sensitivity are considered together . In conclusion , hydroxynaphthol blue was the best method for easy , inexpensive , accurate and reliable interpretation of LAMP assays for human African trypanosomiasis . The violet to sky blue colour change was easy to see and was more consistently interpreted by independent observers . A range of problems were seen with the other methods . Visible turbidity is not possible for all LAMP HAT assays . Quant-iT PicoGreen performed excellently , but opening the reaction tube exposes the laboratory to product contamination . It is also at least 15 times more expensive than the other methods . With calcein and MnCl2 the four assays showed a range of partial to total inhibition and the colour change was difficult to see leading to poor agreement between several independent observers . However , hydroxynaphthol blue is not perfect . We have shown that the agreement between amplicon detection by gel electrophoresis and UV illumination and colour change by hydroxynaphthol blue can be variable and imperfect for different observers . Therefore , while hydroxynaphthol blue is a promising method for low technology LAMP endpoint detection , further work is required to develop methods that will assist different observers to make consistent interpretations of the same colour change .
|
Human African trypanosomiasis ( HAT ) is a disease of the rural poor in sub-Saharan Africa where diagnostic laboratories are scarce and often ill equipped . Specific LAMP ( loop-mediated isothermal amplification ) tests for HAT have been developed and represent a significant step forward in the search for simple , sensitive and reliable diagnosis . Easy , accurate and reliable methods to read the results of these tests are critical and several simple methods have been developed . In this study , four methods were compared including three different colour change methods , and one in which the reaction turned from clear to cloudy . The hydroxynaphthol blue method involving a colour change , from violet to sky blue , was easy to see , the test is cheap to use and the results were largely agreed upon by 33 independent observers .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"evidence-based",
"healthcare/methods",
"for",
"diagnostic",
"and",
"therapeutic",
"studies",
"infectious",
"diseases/epidemiology",
"and",
"control",
"of",
"infectious",
"diseases"
] |
2010
|
LAMP for Human African Trypanosomiasis: A Comparative Study of Detection Formats
|
Genes involved in the same function tend to have similar evolutionary histories , in that their rates of evolution covary over time . This coevolutionary signature , termed Evolutionary Rate Covariation ( ERC ) , is calculated using only gene sequences from a set of closely related species and has demonstrated potential as a computational tool for inferring functional relationships between genes . To further define applications of ERC , we first established that roughly 55% of genetic diseases posses an ERC signature between their contributing genes . At a false discovery rate of 5% we report 40 such diseases including cancers , developmental disorders and mitochondrial diseases . Given these coevolutionary signatures between disease genes , we then assessed ERC's ability to prioritize known disease genes out of a list of unrelated candidates . We found that in the presence of an ERC signature , the true disease gene is effectively prioritized to the top 6% of candidates on average . We then apply this strategy to a melanoma-associated region on chromosome 1 and identify MCL1 as a potential causative gene . Furthermore , to gain global insight into disease mechanisms , we used ERC to predict molecular connections between 310 nominally distinct diseases . The resulting “disease map” network associates several diseases with related pathogenic mechanisms and unveils many novel relationships between clinically distinct diseases , such as between Hirschsprung's disease and melanoma . Taken together , these results demonstrate the utility of molecular evolution as a gene discovery platform and show that evolutionary signatures can be used to build informative gene-based networks .
Advances in sequencing technologies and collaborative , large-scale—omics and genome-wide association projects are providing investigators with overwhelming lists of candidate disease gene associations . In the past decade , nearly 2 , 000 genomic regions have been associated with over 300 complex traits , and open efforts such as The Cancer Genome Atlas have produced petabytes of genetic data to sift through [1 , 2] . To more effectively decipher and prove candidate genes' roles in disease processes , computational tools have been created to both prioritize and place candidate genes into some functional context for more effective experimental validation . As these candidate genes are validated and more genes become linked with functional processes , there is also an increased ability to generate multivariable genetic networks based on these observations [3 , 4] . Here , we show a first-of-its-kind approach to prioritize candidate disease genes and build instructive gene-based networks based on a signature of molecular co-evolution . Proteins do not exert their function in isolation , but rather exist within intricate networks of molecular relationships that can be revealed through high-throughput analyses of protein-protein interactions , tissue-specific expressivity and shared regulatory elements to name a few . The influx of data from these experiments has been utilized to build informative tools that aggregate and interpret these observations to place input proteins into predicted functionally related pathways [5–8] . Among many other uses , these tools have served as a catalyst for gene discovery , successfully giving functional relevance to disease gene candidates from sequencing studies and helping to validate and enhance mechanistic conclusions from high-output biological screens [9 , 10] . The primary methods used to create these networks rely on sophisticated algorithms that weigh certain biological features based on the query genes and sometimes user-dictated parameters . These parameters include Gene Ontology ( GO ) terms , genomic and proteomic study results ( yeast two-hybrid , ChIP-seq , physical interactome datasets , protein structure comparisons , subcellular localization , tissue specific expressivity , etc . ) and even literature mining techniques such as co-occurrence in PubMed abstracts [11] . In addition to giving functional insight to query genes , similar methods have been utilized to prioritize a list of candidate genes for further downstream study . These tools typically implement “guilt by association” strategies in which a user will have a pair of gene lists–one set of genes known in the literature to be involved in a particular pathway/disease of interest ( referred to as a “training set” ) and another list of candidate genes that the researcher has identified as possibly being related to the process in question . Generally , these two lists are entered into an online resource and then the candidate genes are ranked based on their relationships to the training set genes using similar databases and algorithms discussed previously [12] . Gene prioritization techniques have been effectively used in accelerating transitions from large datasets to solid biological insight [13–17] . As more data is acquired and as these tools continue to become more sophisticated and more widely used , the number of disease gene associations are increasing rapidly , mirrored by the exponential growth of entries in the Online Mendelian Inheritance in Man ( OMIM ) Database in the past decade [18] . This permits innovative strategies to not only focus on relationships at the molecular level , but to also implement a more expansive approach and aggregate these relationships to generate novel links between diseases and disease classes . Groups of diseases that are similar , or perhaps diseases thought to be distinct entities , may share pathogenic mechanisms between them that can be uncovered by multiscale , computational approaches [19–22] . These disease-disease relationships may lend themselves to clinically impactful drug repositioning possibilities [23 , 24] . Another field that has benefited greatly from this revolution in data acquisition is molecular evolution . A large number of sequenced genomes from closely related species now allows comparative and evolutionary methods to be applied across the genome . One such method , evolutionary rate covariation ( ERC ) , infers interactions between genes using only their branch-specific rates of sequence evolution in a collection of species [25 , 26] . Namely , genes with rates that statistically covary tend to participate in common functions or pathways . This statistical covariation results mainly from discrete pathways responding to evolutionary pressures as a single unit , thereby causing the evolutionary rates amongst genes in the pathway to fluctuate in tandem . This evolutionary signature of co-functionality , ERC , is measured as the correlation coefficient of gene-specific branch rates between a pair of genes , for which higher values approaching 1 indicate higher rate covariation . ERC has been demonstrated between functionally related genes in mammals , Drosophila , fungi , and prokaryotes [1 , 2 , 26–29] . In addition , statistically significant ERC signatures are found for functionally related genes within diverse functional pathways including meiosis and piRNA metabolism [28] , fertilization [30] , nuclear transport [29] , and more than 60% of annotated protein complexes [26] . Given the ubiquity of ERC signatures , they have even been used to discover novel genes in established genetic pathways , such as in reproductive interactions between female and male Drosophila [31] . Here , we introduce ERC signatures to study the genetic basis of human disease , showing that molecular evolution can serve as an innovative and complementary method for gene prioritization , functional annotation , and disease network generation . We show that , in several cases , genes associated with a particular disease show significantly elevated ERC values between them . Furthermore , ERC identifies target disease genes amongst many unrelated candidate genes based solely on shared ERC values between the candidates and a training set of known disease genes . Lastly , we demonstrate via a gene-based network approach that ERC values are elevated between diseases that share related pathogenic mechanisms and that co-evolutionary signatures can unearth novel relationships between diseases thought to be distinct .
To determine the strength of ERC signatures between disease genes we interrogated a set of 310 Disease Gene Groupings ( DGG ) , each containing at least 3 genes known to be associated with an OMIM-annotated disease . We then examined the ERC values between each pair of constituent genes in each DGG , while testing for statistically significant elevations in ERC as a group . We first provide an example for a single DGG , complement deficiency ( Fig . 1 ) , and then continue with analysis of all DGGs . We measured evolutionary rates for complement deficiency genes C1S and CFI along all branches in a phylogeny of mammalian species . Their rates varied greatly between branches , but their patterns of variation were remarkably similar ( Fig . 1A ) . We quantify this similarity with the Evolutionary Rate Covariation ( ERC ) metric , which is calculated as the correlation coefficient of their rates . Hence , the ERC value between C1S and CFI is 0 . 81 ( Fig . 1B ) . All gene pairs within complement deficiency were compared in this way ( Fig . 1C ) . Notably , the overwhelming majority of complement deficiency ERC values are positive ( 88% ) , whereas random gene sets of the same size yield positive correlation coefficients at a much lower rate ( mean = 59% , maximum of 1000 nulls = 73% ) . Second , gene pairs with very high ERC values were found for those whose protein products form functional complexes , such as those encoding the C1 complement subcomponent: C1Q , C1R , and C1S ( Fig . 1C , upper-left corner ) . An even higher ERC value was observed between members of the C8 component , C8A and C8B ( ERC = 0 . 79 ) . Overall , the mean ERC between all complement deficiency genes was 0 . 344 , which yielded a highly significant p-value ( permutation P < 0 . 00001 ) ( Table 1 ) . When similarly considering all 310 disease states through this analysis , 255 ( 82% ) had positive mean ERC values , indicating a shift toward rate covariation between genes in a common disease . In contrast , random gene sets size-matched to the DGGs had positive mean values in only 59% of cases on average . The maximum observed proportion of mean positive ERCs in 1000 random sets was 69% , which is far lower than that observed for the true DGGs ( 82% ) . Moreover , there was a strong enrichment of low p-values with 73 DGGs below a nominal p-value of 0 . 05—a 4 . 7-fold excess ( Fig . 2 ) . After correction for multiple testing , 40 DGGs were found to have elevated ERC values at a false discovery rate of 5% ( Table 1 ) [32] . From the false discovery rate analysis we also estimated that 55% of the 310 DGGs contain elevated ERC values ( proportion without ERC elevation , η0 = 45% ) . Those diseases with the strongest ERC signatures included cancers , autoimmune conditions , blood cell diseases , and developmental disorders among others ( Table 1 ) . Overall , the observed significant cases indicate that pathologically related genes tend to have more positive ERC values , likely due to their analogous functions in the cell . We sought to assess the power of ERC co-evolutionary signatures as a gene prioritization method . Using the 310 DGGs , we asked whether a known disease gene ( a “target gene” within an OMIM DGG ) was effectively prioritized among a set of chromosomal neighbors using an ERC “guilt by association” approach . More specifically , candidate genes were prioritized by their ERC values with a training set of genes known to influence that disease ( the remaining OMIM DGG members ) . Candidates with higher ERC values were more highly prioritized . To demonstrate one case , the gene DSC2 , which contributes to arrhythmogenic right ventricular dysplasia , was tested as a “target” and its chromosomal neighbors within a 1 Mb window were treated as additional candidates . The remaining 7 genes in that disease were designated as the training set . ERC values between the training set and the target DSC2 were 0 . 16 on average , which placed it in position 1 out of 31 total candidates ( the 96th percentile ) . This case was a successful prioritization . To produce a full statistical characterization of this strategy , the same procedure was repeated for all 2 , 416 OMIM disease genes in all 310 DGGs in our dataset , with a single training set gene being dropped from the training set and defined as the target gene iteratively . Of the 2 , 416 ERC prioritization tests , the 1 MB window surrounding the target gene contained a mean of 81 genes , a median of 62 genes ( lower quartile = 40 , upper quartile = 102 ) and had a range of 4 to 274 genes . On average , ERC gene prioritization placed the target gene in the 64th percentile of all candidate genes . However , the success of prioritization depended strongly on the strength of ERC within the training set ( Fig . 3 ) . When training set genes showed a significant ERC signature amongst themselves , the target gene was prioritized to a much higher position among candidates . Training sets with very strong ERC ( p-value < 10–4 ) placed the target gene in the 94th percentile on average ( median ) , and training sets with ERC p-values between 10–4 and 10–3 prioritized the target gene to the 87th percentile ( Fig . 3 ) . Because the strength of ERC in a training set can be determined before performing prioritization , it is a strong and practical indicator of confidence in ERC-based gene prioritization . In our scan of OMIM DGGs , small training sets ( N ≤ 20 genes ) prioritized target genes better than large training sets ( N > 20 genes ) . Although large sets demonstrated a similar relationship between training set p-value and prioritization percentile , the relationship was relatively noisy . This difference was likely due to the smaller number of DGGs in this category , which resulted in higher variance in estimates of disease gene rank . We also asked if ERC could prioritize candidate genes scattered throughout the genome instead of from a single chromosomal region . Such cases would be encountered if candidates were drawn from whole-exome sequencing data for example . ERC successfully prioritized these candidate lists as well , and almost identically to the chromosomal regions ( S1 Fig . ) . This prioritization also demonstrated a dependency on training set p-value as observed for chromosomal regions . While low training p-values ( p < 0 . 0001 ) placed the true disease gene in the 94th percentile on average ( median ) , that ranking decreased with increasing training set p-value . Overall , these tests demonstrate that ERC can be used to prioritize candidate genes from a chromosomal region or throughout the genome , especially if that disease has an ERC signature between its known genes , i . e . the training set . In the next section , we demonstrate an example application of this approach . To demonstrate ERC gene prioritization , we prioritized candidate genes from a melanoma-associated region . Melanoma was chosen because its 9 reported causative genes have a strong ERC signature ( mean ERC = 0 . 16 , p-value = 0 . 00313 ) ( Table 1 ) , thereby providing strong predictive power as demonstrated in the previous section . A recent genome-wide study by MacGregor et al . found an association between melanoma susceptibility and a 430 kb region of chromosome 1q21 . 3 [33] . Because the region contains 10 protein-coding genes it is not clear which is causative . We prioritized these 10 candidate genes using their mean ERC signature with the 9 known melanoma genes ( Table 2 ) . One gene , myeloid cell leukemia 1 ( MCL1 ) , was prioritized well above the other candidates with a mean ERC of 0 . 173; the next highest candidate was at 0 . 037 . The mean ERC for MCL1 was even greater than that between the genes in the training set ( 0 . 160 ) . Fittingly , MCL1 encodes a protein that regulates apoptosis and cellular differentiation , and hence is a strong candidate for involvement in melanoma susceptibility [34] . Having found robust ERC co-evolutionary signatures between genes within a disease , we next sought to draw links between diseases using the same signatures . We hypothesized that such links would cluster diseases with functionally related genes and potentially reveal unforeseen relationships between diseases . Specifically , we inferred a connection between a pair of diseases if the mean ERC value between their constituent genes was significantly elevated compared to random gene sets . To avoid an artificial inflation of the mean ERC value , any genes shared between DGG's were dropped from the calculation . Of the 48 , 205 disease-disease pairs 132 had significantly elevated ERC at a p-value of 5 × 10-4 or lower , which represents a 5 . 5-fold enrichment . Applying a stringent 5% false discovery rate , there were a total of 81 disease-disease connections , which formed 12 clusters of potentially related diseases ( Fig . 4 ) . The resulting “disease map” contained ERC-drawn clusters with strong tendencies to contain diseases with related pathogenic mechanisms . The largest cluster consisted of 34 diseases and could be broadly classified as blood-related disorders ( Fig . 4; light red network ) . The second largest cluster of 7 diseases ( light blue ) consisted of mitochondrial disorders and ciliopathies , and the third multi-gene cluster was composed of 4 heterogeneous disorders ( dark green ) that have some shared symptomology relationships . Finally , the 9 remaining clusters were pairs of diseases consisting of a heterogeneous collection of disorders . The significance of these relationships is fully addressed in the Discussion section .
In this study we demonstrate that the relationships between disease-associated genes are often reflected in evolutionary signatures encoded in their gene sequences . Using our metric , evolutionary rate covariation ( ERC ) , and the Online Mendelian Inheritance in Man ( OMIM ) database , we report 40 diverse diseases whose genes have elevated co-evolutionary signatures at a false discovery rate of 5% , with an additional 130 diseases that also contain elevated rates according to false discovery rate analysis . We found statistically significant elevations of ERC both between genes causing rare Mendelian disorders , such as Fanconi anemia , as well as more common diseases such as Alzheimer's disease , pancreatitis , deafness , colorectal cancer and renal cell carcinoma ( Supplemental S1 Table ) . The signatures we observe likely reflect the close functional relationships between the genes involved in a common pathogenic mechanism . We have observed similar signatures between functionally related genes in diverse biological processes and across different taxonomic groups ranging from single-celled organisms to mammals [26 , 28 , 29] . Ultimately , the signatures arise from shared fluctuations in evolutionary rates as the genes respond to changing selective pressures . These observations also suggest that these gene networks have been in tact throughout mammalian evolution and that they evolve together in response to shared evolutionary pressures . Overall , the strong signatures in many diseases led us to test ERC's ability to reveal novel genetic relationships in human diseases . ERC signatures can be calculated with existing genome sequences and are thus a practical tool to prioritize candidate genes or to infer the function of novel genes . To demonstrate the potential of ERC signatures to prioritize candidate genes for a given disease we again used the OMIM catalog . By treating each OMIM disease gene in turn as a hypothetically unknown disease gene , we examined its mean ERC value with the remaining known genes for its disease , i . e . the training set . Compared to its chromosomal neighbors from a 1-Mb window or to a set of randomly selected genes across the genome , the true disease gene scored higher on average , yet sometimes not high enough to reliably or efficiently prioritize experimental follow-up . However , for diseases with an ERC signature in their training set ( p-value < 0 . 05 ) , the disease gene was prioritized within the top 5 to 15% on average and in many cases was placed in the top position . To assess our prioritization method , we compared our results to a study that analyzed nine prioritization tools that largely rely on text mining , large-scale genomics , proteomics , expression and genetic association datasets [35] . For cases with a significant ERC signal in the training set , ERC performed on par with or exceeded the top methods ( Table 3 ) . The fact that ERC uses data that is completely independent of these methods raises the exciting possibility that their integration with ERC would further improve prioritization . There is a notable caveat that success in our method depends on significant ERC within the training set , but fortunately this is a simple calculation that can be performed before any data is gathered , and we estimate that approximately one-quarter of genetic diseases satisfy this requirement ( 72 of 310 diseases had ERC p-values < 0 . 05 ) . The potential for ERC to inform and guide experimental efforts in human disease research is mirrored by ERC's previous successes in model organisms [28 , 31] . Based on our results here , there are a number of practical guidelines we can prescribe for gene prioritization . Each of these steps can be performed on our public ERC webserver using the 'Gene Prioritization' function , which also provides other ERC-based analysis tools ( http://csb . pitt . edu/erc_analysis/ ) . The first step is to define a training set of genes already known to be involved in the disease in question . Notably , chances of success should be improved by predicting likely pathogenic mechanisms when possible from clinical data or cellular phenotypes and choosing the most appropriate genes . The next step is to test for an ERC signature within the chosen training set considering our results showed drastically improved prioritization for diseases with strong signatures—the effect was strong enough that we recommend proceeding only if the training set shows a significantly elevated mean ERC . Based on our survey of OMIM-curated diseases , this requirement should be met by approximately a quarter of diseases with a genetic component . However , a potentially larger proportion of diseases could be interrogated if experts choose discrete pathways with stronger ERC signatures as training sets , possibly through careful examination of molecular phenotypes and integration of other bioinformatics tools . The last step is to calculate the mean ERC value of each candidate with the training set . In our example , this set of steps identified the MCL1 gene from a melanoma-associated region as the most likely candidate . Our between-disease analysis of ERC produced a set of disease-disease associations based on evolutionary signatures ( Fig . 4 ) . Tight clusters within this disease map reproduced accepted associations between certain diseases; and perhaps more interestingly , ERC associations also uncovered novel evolutionary relationships between clinically distinct diseases . For example , ERC was able to cluster four mitochondrial diseases that were all intuitively related , some being subclasses of the other . Additionally , a triad of clinically related diseases referred to as skeletal ciliopathies—cranioectodermal dysplasia , asphyxiating thoracic dystrophy and short-rib polydactyly syndrome—was found to share significant ERC values not only amongst each other , but ERC also linked these diseases strongly to the mitochondrial disease network [36 , 37] . The relationship between mitochondrial disorders and ciliopathies is largely unaddressed in the literature , but there are reports that mitochondrial proteins may co-localize with ciliary proteins [38] and there is evidence of a mitochondrial protein deficiency ( XPNPEP3 ) that produces a , phenotypically speaking , ciliopathy-like syndrome [39] . Many two-disease clusters also showed compelling , non-intuitive relationships . A link between surfactant metabolism dysfunction and Bethlem myopathy was deemed significant by ERC values , despite these two diseases having very little in common with one another clinically . Bethlem myopathy is caused by a defect in the production of a specialized collagen that leads to debilitating muscle weakness , while inherited surfactant defects leads to severe respiratory deficits . However , recent evidence has interestingly suggested that surfactant proteins have essential collagen domains for surfactant homeostasis [40 , 41] . A rather dramatic pairing is the association between melanoma and Hirschsprung's disease , an embryologic defect of neural crest cell migration in which a portion of the intestinal nervous system lacks innervation , becomes immotile and causes gastrointestinal obstruction . Again , although these two diseases are clinically distinct , the association of the two using ERC suggests a shared mechanism between them . Digging into this relationship further , strikingly , nearly all genes associated with Hirschsprung's disease have had some evidence in melanoma pathogenesis . Variants in EDNRB have been loosely associated with increased melanoma risk in humans and are hypothesized to play a role in CNS melanoma metastases [42 , 43] . Additionally , if EDNRB is heterozygously deleted in a mouse transgenically expressing RET—another Hirschsprung disease gene—mice develop de novo melanoma lesions [44] . Moreover , yet another Hirschsprung-associated gene , EDN3 , has also been linked to melanoma invasiveness [45] . Lastly , a research group serendipitously produced a Hirschsprung's disease mouse model while attempting to create a UV-induced melanoma model by knocking out a DNA repair gene in melanocytes of mice , with that same gene now being proposed as a potential mediator of melanoma chemoresistance [46 , 47] . The relationship between these two diseases is largely unaddressed specifically in the literature , although there is one report of an inherited form of Hirschsprung's disease that had a suspicious pattern of melanoma and pigment abnormalities within the family ( Wildin and Eichmeyer , 2008 , ASHG , abstract ) . Melanocytes and enteric nerve cells are known to be both embryologically derived from neural crest cells , perhaps explaining at least in part why there may be an evolutionary link between the shared mechanisms of dysfunction that was uncovered by ERC . Another connection of interest included one made between Noonan syndrome and Diamond-Blackfan anemia . These two diseases have no obvious pathogenic connection; however , they were linked by ERC . Interestingly , the two share common features including neck webbing , micrognathia , low-set ears , specific cardiac abnormalities and epicanthus among many others [48 , 49] , suggesting ERC may be able to link diseases with shared symptomatology . The largest cluster consisted of a network of what could be broadly classified as blood-related disorders . With 34 diseases , this group consisted of 63 disease-disease connections . The more intuitive connections included ERC links between inherited disorders that produced erythrocyte structure defects—spherocytosis and eliptocytosis—and also statistically strong links between inherited disorders of hemostasis such as thrombophilia , dysfibrinogenemia and general coagulation cascade deficits . ERC also linked thyroid dyshormogenesis and hypertryosinemia , of note since tyrosine molecules are the synthetic precursors of thyroid hormones . Another particularly interesting connection within this network included a strong link between complement deficiency and systemic lupus erythematosus . Past research has shown a strong link between these two diseases , and here , we show shared evolutionary signatures further corroborating this observation [50 , 51] . Other intriguing observations can be made , such as a link between atypical uremic syndrome—caused by a loss of inhibitory factors within the complement cascade—and complement protein deficiencies . In summary , these associations imply that ERC can generate large-scale , informative gene-based networks . In this case , we were able to build logical disease networks and uncover potentially novel pathogenic relationships between disease-causing genes using a molecular evolution signature . Other recent studies have laid out disease associations into maps or networks using different approaches . A pioneering map by Goh et al . inferred links between Mendelian diseases based on shared contributing genes , and was able to form an expansive disease network [52] . While our evolution-based map explicitly ignored shared disease genes , it still exhibited a number of disease-disease associations in agreement with the Goh et al . map ( Supplemental S3 Table ) . Moreover , our map revealed a number of associations not found in theirs , suggesting that ERC can uniquely uncover linkages between diseases—an example being between melanoma and Hirschsprung's disease as discussed above . Another promising ERC disease map-specific example is a cluster of renal and pulmonary diseases that share solute transport imbalance as a central characteristic—iminoglycinuria , hyperglycinuria , pseudoaldosteronism , and bronchiectasis [53] . A disease map by Suthram et al . adopted a sophisticated strategy to discover disease relationships using both protein interaction modules and co-expression profiles [54] . This dual strategy allowed them to move beyond Mendelian diseases and map associations between multi-genic disorders . However , we were unable to compare the evolutionary map with theirs because we examined a different set of diseases . The most recent disease-disease association study departed from genetic data and used massive databases of patient phenotypes to infer relationships between both common diseases and rare Mendelian ones [4] . Most diseases in this map were not found in ours , but of those found in both studies , there was concordance . For example , both maps inferred an interconnected cluster of skin , blood , and immune-related diseases . Lastly , a future aim of ours is to integrate our approach with other tools currently available . ERC is a unique signature of co-functionality that is entirely derived from comparative sequence analysis . As such , it is expected to be independent and complementary to other established approaches , such as physical interaction datasets , co-expression analyses and literature mining algorithms [5 , 12 , 55] . Integrating these methods will allow investigators to capitalize on the strengths of each , enhancing our ability to prioritize and reveal valuable functional information regarding disease genes as well as to further contribute towards the recent trend of network-based studies of genes and diseases [4 , 56 , 57] . These efforts broadly begin to demonstrate the profound potential of utilizing a network-based understanding of molecular evolution to assist in gene prioritization , gene functional annotation and informative gene-based network generation . Our hope is that ERC will provide an alternative strategy for biomedical researchers to more efficiently transform gene candidates into actionable hypotheses .
ERC values were calculated between 17 , 486 pairs of human genes as described in previous publications [26 , 28] . In order to be included in the mammalian ERC analysis , gene ortholog presence was required in a minimum of 17 of the 33 species in the dataset . Of the 19 , 733 mammalian gene alignments considered , 17 , 487 met this threshold . Briefly , branch lengths based on amino acid divergence were created from protein coding mammalian sequences derived from the following species: Homo sapiens ( human ) , Pongo pygmaeus abelii ( orang-utan ) , Macaca mulatta ( rhesus macaque ) , Callithrix jacchus ( marmoset ) , Tarsius syrichta ( tarsier ) , Microcebus murinus ( mouse lemur ) , Otolemur garnettii ( bushbaby ) , Tupaia belangeri ( tree shrew ) , Cavia porcellus ( guinea pig ) , Dipodomys ordii ( kangaroo rat ) , Mus musculus ( mouse ) , Rattus norvegicus ( rat ) , Spermophilus tridecemlineatus ( squirrel ) , Oryctolagus cuniculus ( rabbit ) , Ochotona princeps ( pika ) , Vicugna pacos ( alpaca ) , Sorex araneus ( shrew ) , Bos taurus ( cow ) , Tursiops truncatus ( dolphin ) , Pteropus vampyrus ( megabat ) , Myotis lucifugus ( microbat ) , Erinaceus europaeus ( hedgehog ) , Equus caballus ( horse ) , Canis lupus familiaris ( dog ) , Felis catus ( cat ) , Choloepus hoffmanni ( sloth ) , Echinops telfairi ( tenrec ) , Loxodonta africana ( elephant ) , Procavia capensis ( rock hyrax ) , Dasypus novemcinctus ( armadillo ) , Monodelphis domestica ( opossum ) , Macropus eugenii ( wallaby ) , and Ornithorhynchus anatinus ( platypus ) . Branch lengths were estimated using the aaml program of the PAML package [58] . These lengths were normalized into relative rates using the projection operator method [59] , and correlation coefficients ( i . e . ERC values ) between these relative rates were calculated between every pair of genes using custom Perl programs . Data was downloaded from the OMIM website on June 4 , 2013 . Using the OMIM Morbid Map dataset , which is a list of diseases followed by single gene associations from published studies , a Perl script was written that grouped disease genes by character matching manually curated disease gene associations into respective disease gene groups . 310 Disease Gene Groupings ( DGGs ) were generated by broadly grouping all genes with matching disease names , effectively producing a list that consisted of each disease with multiple genes that have been associated with that particular disease . These groups can be found in Supplemental S2 Table , which lists all genes within each DGG along with their corresponding gene and phenotype MIM numbers . From this data , the average ERC value between all combinations of genes within each DGG from the 33 mammalian-species ERC dataset was calculated and then statistically compared to a null distribution of 100 , 000 random gene groups of the same size using a customized Perl script to determine any significant elevations in the mean ERC value . The analysis was limited to disease gene pairings that were present in the current ERC database ( 17 , 487 human genes & 133 , 416 , 393 ERC value pairs ) and DGGs that contained greater than 1 gene . The data was then sorted by p-value to determine diseases that most significantly harbored elevated ERC signatures . Lastly , a false discovery rate analysis was performed using the 'fdrtool' R package on the resulting p-values [60] . We assessed ERC's ability to prioritize genes by creating a benchmarking study that generated a list of all genes surrounding a “target” disease gene within a 10 MB region and grouped them into an aggregate “candidate” gene list . Using a “training set” of the remaining OMIM genes shown to be associated with the disease , the candidate genes were then prioritized based on ERC values . We attempted to prioritize the genes using two ERC ranking strategies . The first method ( GROUP ERC ) calculated the mean ERC value of each candidate gene with all genes in the training set and then ranked the candidates from highest mean ERC to lowest . The second method ( BEST ERC ) scanned ERC values between each gene in the training set with each candidate gene and used the maximum ERC value between any training set gene to rank the candidates . Ultimately , the GROUP ERC method was chosen for application in the prioritization tests . Disease-disease comparisons were made by calculating the mean ERC value between the genes in each of the two diseases and then comparing that value to that of 10 , 000 resampled pseudo-disease sets . If two DGG's shared genes , these genes were dropped from the ERC mean calculation to avoid an artificial enhancement of the value . The number of pseudo-datasets greater than or equal to the observed mean were tallied to calculate a permutation p-value . There were a total of 48 , 205 pairwise comparisons between all 310 Disease Gene Groupings . With the resulting p-values we performed false discovery rate analysis as before [60] and reported all disease-disease pairs significant at a false discovery rate of 5% ( Fig . 4 ) .
|
Molecular evolution has informed our understanding of gene function; however , classical methods have largely been static in their implementation , focusing on single genes . Here , we present and prove the utility of a dynamic , network-based understanding of molecular evolution to infer relationships between genes associated with human diseases . We have shown previously that groups of genes within functional niches tend to share similar evolutionary histories . Exploiting the availability of whole genomes from multiple species , these histories can be numerically scored and dynamically compared to one another using a sequence-based signature termed Evolutionary Rate Covariation ( ERC ) . To explore potential applications , we characterized ERC amongst disease genes and found that many diseases contain significant ERC signatures between their contributing genes . We show that ERC can also prioritize “true” disease genes amongst unrelated gene candidates . Lastly , these signatures can serve as a foundation for creating instructive gene-based networks , unveiling novel relationships between diseases thought to be clinically distinct . Our hope is that this study will add to the increasing evidence that advancing our understanding of molecular evolution can be a crucial asset in large-scale gene discovery pursuits ( Link to our webserver that provides intuitive ERC analysis tools: http://csb . pitt . edu/erc_analysis/ ) .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
Evolutionary Signatures amongst Disease Genes Permit Novel Methods for Gene Prioritization and Construction of Informative Gene-Based Networks
|
Ghana started its national programme to eliminate lymphatic filariasis ( LF ) in 2000 , with mass drug administration ( MDA ) with ivermectin and albendazole as main strategy . We review the progress towards elimination that was made by 2016 for all endemic districts of Ghana and analyze microfilaria ( mf ) prevalence from sentinel and spot-check sites in endemic districts . We reviewed district level data on the history of MDA and outcomes of transmission assessment surveys ( TAS ) . We further collated and analyzed mf prevalence data from sentinel and spot-check sites . MDA was initiated in 2001–2006 in all 98 endemic districts; by the end of 2016 , 81 had stopped MDA after passing TAS and after an average of 11 rounds of treatment ( range 8–14 rounds ) . The median reported coverage for the communities was 77–80% . Mf prevalence survey data were available for 430 communities from 78/98 endemic districts . Baseline mf prevalence data were available for 53 communities , with an average mf prevalence of 8 . 7% ( 0–45 . 7% ) . Repeated measurements were available for 78 communities , showing a steep decrease in mean mf prevalence in the first few years of MDA , followed by a gradual further decline . In the 2013 and 2014 surveys , 7 and 10 communities respectively were identified with mf prevalence still above 1% ( maximum 5 . 6% ) . Fifteen of the communities above threshold are all within districts where MDA was still ongoing by 2016 . The MDA programme of the Ghana Health Services has reduced mf prevalence in sentinel sites below the 1% threshold in 81/98 endemic districts in Ghana , yet 15 communities within 13 districts ( MDA ongoing by 2016 ) had higher prevalence than this threshold during the surveys in 2013 and 2014 . These districts may need to intensify interventions to achieve the WHO 2020 target .
Lymphatic filariasis ( LF ) , commonly known as elephantiasis , is a debilitating and disfiguring tropical disease caused by lymphatic-dwelling filarial parasites Wuchereria bancrofti , Brugia malayi and Brugia timori . The disease is transmitted by different species of mosquitoes depending on the geographical location , including Culex , Anopheles and Aedes species . About 90% of the worldwide cases are caused by W . bancrofti and 10% caused by B . malayi and B . timori . Based on re-assessment of the global prevalence and distribution of LF [1] , more than 120 million people were found to be infected and 40 million disfigured and incapacitated in the year 2000 [2] . In the same year , the Global Programme to Eliminate Lymphatic Filariasis ( GPELF ) was established , aiming to eliminate the disease as a public health problem by 2020 through annual mass drug administration ( MDA ) with albendazole in combination with diethylcarbamazine citrate ( DEC ) or ivermectin to all individuals at risk [3] . By the end of 2016 , 20 out of 73 countries originally listed by the World Health Organization ( WHO ) as being endemic for LF have stopped interventions after passing the first transmission assessment survey and are conducting surveillance to validate elimination as a public health problem . Additional 30 countries have delivered MDA at least once in all endemic areas and are also on track to achieve the 2020 target [4] . While many have passed the TAS , there are also reports of failure [5] and of ongoing transmission in spite of passing the TAS [5–7] . A national survey carried out in Ghana in 1994 showed that the microfilaraemia prevalence varied from 0–20% between regions [8] . In the highly-endemic Kassena Nankana district ( Upper East Region of Ghana ) , the prevalence of hydrocele was 30 . 8% and elephantiasis of the leg was 3 . 8% in the population aged 10 years and above [9 , 10]; 12% of extended families reported to have at least one family member with elephantiasis of the leg [10] . The extensive mapping of endemic communities [11] provided a database on areas in Ghana and neighboring countries that needed more efforts to eliminate the disease . The LF elimination programme in Ghana started in 2000 and gradually scaled up over the years and by 2006 all endemic districts were covered . The implementation and outcomes by district were described in two recent papers [12 , 13] . By 2016 , 81 of 98 initially endemic districts had reached an microfilaria ( mf ) prevalence <1% , had passed TAS survey and stopped MDA , while the remaining districts still had mf prevalence >1% [13] in spite of at least 10 years of MDA . The required duration of MDA turned out to be longer than the anticipated 5–6 years , which might be due to relatively high baseline mf prevalence levels . There were no major differences with other districts in reported coverage of MDA or long-lasting insecticide treated bednets [13] . Expected trends in infection during MDA will depend on multiple factors , including local baseline endemicity ( depending on local transmission conditions ) and the achieved coverage and compliance with MDA [7 , 14–16] . We aim to assess the impact of MDA on mf prevalence levels and to review progress towards LF elimination in Ghana from 2000–2016 . For this purpose , we analyze community-level data from mf prevalence surveys and transmission assessment surveys ( TAS ) from sentinel and spot-check sites for all endemic districts in Ghana .
The Ghana Filariasis Elimination Programme ( GFEP ) was established in June 2000 following the establishment of the Global Programme to Eliminate Lymphatic Filaraisis . Mapping of communities started in 2000 using the 50-km sample grid , rapid assessment procedure for antigenaemia in sample villages and spatial analysis to plot prevalence contours from 2000 to 2001 [11 , 17] . Forty nine districts out of 110 were initially identified as endemic and therefore selected for implementation of MDA . The GFEP implementation , programme outcomes , challenges and districts re-demarcation have been described in Biritwum et al . ( 2017a ) . Based on current demarcation , 98/216 districts ( 45% ) are endemic with LF in Ghana . The treatment implemented in Ghana was the combination of ivermectin ( 150 μg/kg ) and albendazole ( 400 mg ) given annually by the community-directed treatment approach [18] and implemented at the district level . MDA usually took place between March and June in all endemic communities across the country . Individuals eligible for treatment were those aged ≥5 years ( excluding pregnant women , lactating mothers and the sick ) , and selection was solely based on height ( ≥90 cm ) for those whose ages were not known . MDAs usually lasted for about 1–2 weeks per community . Individual treatment information ( whether treated , absent , pregnant , sick , etc ) was recorded in the community treatment book and summarized into treatment records by the Ghana Health Services ( GHS ) . Community-level treatment coverage data ( number treated out of total population at risk ) across the country were reviewed and summarized by the GHS . For the purpose of this study , summary reports were reviewed . There was no treatment offered in 2011 due to logistic and funding challenges; 2009 and 2012 treatment data were not available . The longitudinal parasitological and treatment data from 2000–2014 were collated along with background information from the GHS and updates on TAS results until early 2016 . Parasitological data comprised of number examined and number that were microfilarial positive in each community . Community mf prevalence was estimated as the number of microfilarial positives as a percentage of number examined in the community . Mf prevalence ( for the districts or country ) were calculated by estimating the percentage of total positive / total examined of communities included in the sampling for the district or country . Similarly , the treatment coverage ( for the districts or country ) were calculated by estimating the percentage of total treated / total population of communities included in the sampling for the district or country . We present data on community , district and country level . Ethical clearance was obtained from the Ghana Health Service Ethical Review Committee ( ID NO: GHS-ERC-10/0/06 ) and the Liverpool School of Tropical Medicine’s Research Ethics Committee’s Research Protocol Approval ( 06 . 47 ) . The study obtained oral informed consent from adult participants while parents and guardians orally consented for their children and wards to be part of this study . Due to the programmatic nature of the study with regular MDA and mf surveys done in many sites , participants in these communities were aware of the program . Given that the communities were mainly rural with study participants having minimal or no education and being suspicious of signing documents they did not well understand , oral consent was applied and noted as part of questionnaires during the surveys . Oral informed consent was approved by the Ghana Health Service and Liverpool School of Tropical Medicine’s ethical review committees .
MDA started between 2000 and 2001 in 10/98 districts selected from the northern and coastal regions of Ghana . In 2002 , 17 more districts were enrolled onto the MDA programme and in 2003 , 2004 , 2005 and 2006 a number of 27 , 16 , 25 and 3 more districts were enrolled onto the MDA programme respectively ( Figs 1 and 2 ) . All communities in each district were expected to be treated in the same year MDA started , thus geographical coverage within a district was expected to be 100% . The median reported treatment coverage in treated districts of Ghana seemed to be constant over time , around 77–80% between 2000–2010 , and the interquartile range and distribution of outliers were also similar over time ( Fig 3 ) . Although mean reported coverage per year seem to be high , there are large differences between communities . Community-level coverage estimates varied from 10 to 120% , with at least 7952/41265 ( 19 . 3% ) surveys having a coverage under 65% and 198/41265 ( 0 . 5% ) surveys over 100% , indicating wrong denominators . TAS was done in 5 districts in 2010 , and all passed . Another 65 , 9 and 2 districts had their first TAS in 2014 , 2015 and early 2016 , respectively and all passed . By the end of 2016 , 81 out of the 98 endemic districts had passed the TAS in Ghana and had stopped MDA ( Figs 1 and 2 ) . 17 are left , of whom 4 , 3 , 8 , 1 and 1 district have done 11 , 12 , 14 , 15 and 16 rounds of MDA , respectively ( see S2 Table , supplementary data for details ) . The average number of treatment rounds in districts that stopped MDA was 11 rounds , varying from 8–14 . TAS-2 was performed in 69 districts in 2012 or 2015 , and all districts passed . Details of the TAS surveillance in Ghana are given in S2 Table , supplementary data . Mf prevalence data were available from 613 community mf surveys ( datapoints ) , carried out between 2000 to 2014 in 430 communities ( 292 sentinel sites; 138 spot-check sites ) in 78 out of the 98 endemic districts ( within 8/10 regions of Ghana ) . Twenty districts were not represented in our compiled database , either because only antigenaemia data or TAS data were available or because no surveys had been done after re-demarcation of districts . 352 communities were measured only once and 78 measured multiple times ( sampled between 2–6 times ) . Out of those measured multiple times , 35 communities also had data including baseline . Most of the single time point surveys were observed after 2007 ( Fig 4A ) . Overall , the total number of individuals sampled per year ranged between 1 , 784–19 , 268 . Baseline parasitological surveys were carried out in the years 2000–2004 , before the start of MDA , examining 7 , 882 individuals from 53/430 communities within 21/98 districts . The number of individuals examined per community at baseline ranged between 52–441 ( mean 137 , median 112 ) . The average mf prevalence at baseline was 8 . 7% ( range 0–45 . 7% ) with the highest recorded in Gyahadze located in the central region of Ghana ( See supplementary S1 Table ) . Community-level mf prevalence data are presented by calendar year ( Fig 4A ) . The impact of MDA on mf prevalence cannot clearly be seen from this figure , due to the differences between communities in start year of MDA . In Fig 4B , therefore , the same data are presented by time since first treatment , while Fig 4C presents these data in boxplots to better visualize the distribution of the observed community-level mf prevalence . The variation in baseline prevalence was large ( ranged between 0–45 . 7% with interquartile range of 0 . 46–22 . 9% ) . The mean and median mf prevalence in surveyed communities declined strongly with increasing duration of MDA . Although 6–7 years after the onset of MDA the median prevalence had fallen below 1% , the variation was still large ( range = 0–20 . 8%; interquartile range = 0–1 . 5% ) between communities and many communities still had mf prevalence levels above 5% ( Fig 4C ) . Yet , we still see a continued decline in the maximum observed prevalence levels with increasing duration of MDA . In most communities with multiple measurement the mf prevalence steadily decreased over time , but 12 out of 78 ( 15% ) communities had at least once an increase between 2 time points ( Fig 4A & 4B ) . In the 2013 and 2014 surveys , 7 and 10 communities respectively were identified with mf prevalence still above 1% ( maximum 5 . 6% ) . Fifteen out of these 17 communities with threshold above 1% are within 13 districts ( out of 17 districts ) where MDA was still ongoing by 2016 . In 34 districts , one or more communities were surveyed at least twice during the period of MDA . Data for these districts are shown in supplementary file S1 Fig . When community data were aggregated at district level , there was a general decrease in average mf prevalence over time to approach zero in most districts ( S1 Fig , red line ) . In 4 districts ( Bongo , Jirapa , Lambussie-K and Lawra ) there were slight increases in mf prevalence after baseline before decreasing steadily . Almost all the districts we assessed , apart from two ( Lawra and Wa-West ) , showed mf prevalence less than 5% after 6 years of MDA ( S1 Fig , supplementary data ) . In 31 out of these 34 districts ( 91% ) , mf prevalence eventually fell below <1% after 6–14 rounds of treatment; this was not the case in three districts ( Bole , Jirapa and Wa-West ) where the mf prevalence was still ≥1% in 2013 or 2014 and MDA still ongoing by 2016 . 51 out of the 78 examined districts/IUs ( 65% ) needed more than 6 rounds of MDA to reach mf prevalence of <1% . There was a moderately positive correlation ( correlation coefficient = + 0 . 5 ) between baseline mf prevalence versus years of MDA required for each district ( limited to districts that have stopped MDA ) [S2 Fig , supplementary data] .
The Ghana Filariasis Elimination programme has had large impact , reducing mf prevalence <1% in 81/98 endemic districts . The remaining 17 districts still need MDA but also seem to be approaching this target . There was variation in the required treatment rounds between and within districts . Stopping MDA must be done with caution , taking into account the risk that communities with residual transmission remain which could present a source for the resurgence of infection after stopping MDA . Monitoring at the community level is required to be maintained to sustain the gains that have already been made towards elimination of LF in Ghana .
|
Lymphatic filariasis ( LF ) control in Ghana has relied on ivermectin and albendazole since the year 2000 when the Ghana Filariasis Elimination Programme started . We analyzed trends in microfilaraemia prevalence during MDA , reported coverage , and transmission assessment survey using data obtained from the Ghana Health Services ( GHS ) . The median reported treatment coverage varied between 77–80% over the years . Our results show that the treatment in Ghana made a significant impact in reducing infections <1% in majority of sentinel sites in endemic districts ( 81/98 ) by 2016 . In the remaining 17 districts , extra efforts may be needed to achieve the same goal . Some of the challenges could be low coverage in some communities , high baseline endemicity , programme logistical challenges etc . The required average rounds of MDA needed to reach mf prevalence < 1% was 11 , higher than that proposed by the Global Filariasis Elimination Programme . This article is relevant to LF control programmes in assessing the impact of MDA . It is important for programmes to monitor infections especially within communities where mf prevalence is still above the 1% threshold to ensure that the WHO 2020 target is achieved .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
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"oncology",
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"neglected",
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"vectors",
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"vectors",
"insects",
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"interactions",
"organisms"
] |
2019
|
Progress towards lymphatic filariasis elimination in Ghana from 2000-2016: Analysis of microfilaria prevalence data from 430 communities
|
The partitioning and subsequent inheritance of cellular factors like proteins and RNAs is a ubiquitous feature of cell division . However , direct quantitative measures of how such nongenetic inheritance affects subsequent changes in gene expression have been lacking . We tracked families of the yeast Saccharomyces cerevisiae as they switch between two semi-stable epigenetic states . We found that long after two cells have divided , they continued to switch in a synchronized manner , whereas individual cells have exponentially distributed switching times . By comparing these results to a Poisson process , we show that the time evolution of an epigenetic state depends initially on inherited factors , with stochastic processes requiring several generations to decorrelate closely related cells . Finally , a simple stochastic model demonstrates that a single fluctuating regulatory protein that is synthesized in large bursts can explain the bulk of our results .
Inheritance is more than the faithful copying and partitioning of genomic information . When cells divide , the mother cell passes numerous other cellular components to the freshly born daughter , including nucleosomes , transcription factors , mitochondria , and substantial fractions of its proteome and transcriptome . In this way , an entire pattern of gene expression can be passed from mother to daughter , a phenomenon known as epigenetic or non-Mendelian inheritance . Classic examples permeate the literature and include the sex-ratio disorder in Drosophila [1] , the yellow-tip phenotype in melons [2] , the telomere position effect in yeast [3] and mouse [4] , and prions such as Psi+ in yeast [5] . The time scale over which epigenetic phenotypes may persist spans many orders of magnitude and depends strongly on the physical mechanism used by the cell [6] . In general , however , epigenetic phenotypes are substantially less stable than chromosomally inherited ones are [6 , 7] , and can change reversibly in single cells [3 , 8 , 9] during development [10 , 11] , or in mature organisms [12] . Beginning with landmark studies on the lac operon in the 1950s , positive transcriptional feedback loops have emerged as a means to store cellular memory [13–15] . Such epigenetic inheritance systems are frequently described as “bistable , ” meaning that transcriptional activity of genes in the network tends to become fixed in single cells around one of two stable levels ( ON and OFF ) , each of which is able to stably persist for many generations [8 , 16 , 17] . Stochastic fluctuations in the creation or decay of the proteins involved [18–34] , or changes in external cues ( e . g . , a changing environment ) , are responsible for causing transitions between the two states [8 , 13 , 16 , 17] . This flexible strategy , which is present in both prokaryotes and eukaryotes , allows genetically identical cells to diversify their population , possibly allowing them to exploit new environmental niches or to survive in a fluctuating external environment [35] . Feedback-based cellular memories show an exceptional range of stability; depending on the strength of the feedbacks , cells may display memory of a previous expression state as short as a single generation to as far back as many thousands of generations [17] . However , quantitative measurements of phenotype stability , switching , and heritability are rare , both because detailed genealogical relationships are challenging to produce in single cells [36] and because reporters indicating degree of inheritance are not always available . To measure how a dynamic gene expression state is inherited , we focused on an engineered version of the galactose utilization ( GAL ) pathway in the yeast Saccharomyces cerevisiae ( Text S1 ) . We disrupted the pathway's major negative feedback loop and grew cells in conditions where only a single positive-feedback loop was operational ( see Materials and Methods ) . Under these conditions , cells stochastically transition between two distinct expression states even in the absence of an extracellular trigger . These infrequent switching events therefore likely arise from fluctuations in concentrations of regulatory proteins within the individual cells [37] . We are able to monitor transitions between ON and OFF using a fluorescent reporter ( see Materials and Methods , Figure S3 ) . Together , these attributes make our network an ideal model system that is well suited to study the heritability of an entire dynamic gene expression state . In this work , we find that not only is the epigenetic phenotype itself heritable , but that the stability of this phenotype is likewise a heritable quantity . In other words , when cells divide , the nascent daughter cell assumes both the expression state of the mother cell as well as its tendency to switch epigenetic states at a similar time in the future . This is surprising , especially considering that individual cells viewed outside their genealogical context appear to switch completely at random . We resolve this apparent contradiction using a simple stochastic model .
We first set out to quantify , using fluorescence microscopy , the infrequent switching events that occur at random times . All experiments began with a single cell confined between a cover slip and a thick agar pad . Over a period of about 920 min ( >15 h ) each cell grew and divided to eventually form a small colony of 50–100 cells . Throughout the measurement period , these cells diverged in behavior , with some increasing in fluorescence and others decreasing . We repeated this process with more than 100 progenitor cells , so in sum our data represent many thousand single-cell trajectories . We present two examples of the experimental procedure in Figure 1 . In Figure 1A , an initially bright cell develops into a small colony with distinct subpopulations . The dim cells in the lower subpopulation continue to diminish in fluorescence with each successive cell division as the remaining molecules of green fluorescent protein ( GFP ) dilute . In Figure 1B , an initially faint cell likewise gives rise to a variegated colony with cells both dim and bright . Together , these two processes generate a broad bimodal steady-state distribution . Narrowing our focus to initially OFF progenitor cells , we allowed each to grow , divide , and give birth to other initially OFF cells . We then recorded instances when cells switched into the ON state ( Figure 2A and Video S1 ) . Because cellular auto-fluorescence is uniformly small throughout the population of OFF cells , these fluorescing events were generally distinguished unambiguously from background fluctuations . Using these data , we generated for each colony a family tree where the detailed genealogical relationships and gene-expression histories of corresponding family members are shown ( Figure 2B ) . Because cells are continuously born throughout the experiment , we aligned them in silico so that their birth times were identical . In this context , it is natural to define the marginal switch time , τX , a parameter that describes the interval between the birth of a cell X and the moment it eventually becomes fluorescent ( Figure 2C ) . We normalized each measurement according to its expected likelihood of being observed ( see Figures S4 and S5 , Text S3 ) to account for any biases caused by the cells' exponentially dividing throughout our measurement period . The resulting data fit well to an exponential curve with an effective transition rate of 0 . 12 switches per generation ( Figure 3A , cyan line ) . The slight discrepancy between data and exponential fit is likely the result of some cells growing out of the focal plane . The reverse switching distribution , composed of ON cells switching into the OFF state , could not be obtained in this simple way , because in this scenario the long life of the fluorescent proteins makes it difficult to determine the exact moment cells cease production of yellow fluorescent protein ( YFP ) . This exponentially distributed switching pattern applies to cells chosen at random without regard to genealogy . However , measuring cells instead on the basis of their family history paints a very different picture . To demonstrate this difference , we asked how likely a mother and a daughter cell were to have both switched within a small window of time after the cells divided . We selected all daughter cells with marginal switch times below some value T , and then we measured what percent of their mothers had also switched at or before that time . The results , summarized in Figure 3B ( open circles ) , show that when a daughter switches shortly after cell division , its mother cell is overwhelmingly likely to do the same . For example , of the daughters who switch within 400 min of cell division ( about two generations ) , their mothers have approximately a 50% chance of switching in that same period . This represents a 2-fold increase in the switching rate for a typical unrelated cell . As T grows to encompass an ever-larger fraction of all daughter cells , the corresponding percent of switching mother cells asymptotically approaches the marginal switch distribution of Figure 3A ( reproduced in black ) , which represents the limit of no genealogical relationship . As in the marginal switch case above , we are careful to weigh each of these mother-daughter pairs according to how likely we were to experimentally observe them . To measure the underlying rates governing this process , we examined the possible switching events diagrammed in Figure 3A . In this simplified view , we assume cell pairs can either switch together into the ON state together at a rate c ( t ) , or independently of one another at a rate r − c ( t ) . In this way , the total switch rate for any given cell sums to r at all times , as required by the marginal switch distribution . We assume that the correlations decay with a rate , which is reminiscent of an Ornstein-Uhlenbeck process ( Figure 3A ) [16 , 28] . The fixed delay of 20 min is included to account for slow chromophore ( YFP ) maturation as observed in our data ( daughters that switch within the first 20 min after cell division have mothers that always switch ) . This model includes two free parameters: r , the overall switch rate , and Tc , the characteristic time for the correlation to decay . A global least-squares fit to both curves ( Figure 3B , red and blue curves ) simultaneously yields ( r = [7 . 0 ± 0 . 5] · 10−4 min−1 = 0 . 12 ± 0 . 01 gen−1 ) and ( Tc = 197 ± 54 min ) . This decorrelation rate is quite similar to the average cell doubling time of 177 min ( Text S2 and Figure S1 ) , and similar connections between doubling time and decorrelation have been found in other protein regulatory networks [28] . The above analysis suggests that when cell pairs do switch , they will do so in synchrony . To demonstrate that this is indeed the case , we turned our focus to the further subset of cell pairs where both cells are observed to switch during the experiment ( and therefore ignoring cases where only one cell in a pair switches ) . More specifically , we concentrated on three cell relationships: mothers with daughters ( henceforth M-D ) , grandmothers with granddaughters ( GM-GD ) , and older siblings with younger siblings ( S1-S2 ) . Instead of marginal switching times , which are measured relative to each individual cell's time of birth , we chose instead to compute the switch times of both cells relative to the moment when their two respective branches of the family tree first broke apart . Put another way , this quantifies the amount of time between a switching event and the last moment that these cell lines shared cytoplasm . The purpose of this approach is to allow us to compare cells that were born at very different times on equal footing , ensuring that switching events are measured relative to the same point for both cells . For M-D pairs , the time we use is simply the birth of the daughter; for GM-GD pairs , however , it is the birth of the intervening daughter; and for S1-S2 pairs , it is the older sibling's birth . Formally we define the conditional switch time , τX|Y , as the time elapsed between the fluorescing of cell X and the birth of cell Y . When X and Y both refer to the same cell , we recover the marginal switch time ( i . e . , τX|X = τX ) . Comparing M-D conditional switch times ( Figure 4A ) , we observe nearly synchronous switching that extends at least 300 min and yields a correlation coefficient of ρMD=0 . 87 ( p < 10−45 ) . GM-GD and S1-S2 pairs ( Figure 4B and 4C ) show somewhat lower correlation coefficients of ρGMGD = 0 . 74 ( p < 10−9 ) and ρSS = 0 . 60 ( p < 10−7 ) , respectively , although the total coefficient for all data combined remains a robust ρTOT = 0 . 8 ( p < 10−62 ) . The strength and duration of these correlations are surprising and were not found in bacterial [16 , 25] and mammalian [38] studies , except in context of morphological traits [39] . Like the marginal switch data , these scatter plots should be viewed in the context of finite experimental viewing times , giving weights to points that are inversely proportional to the number of experimental opportunities to have seen them ( Text S3 , Figures S6 and S7 ) . One dynamic measure for the randomness associated with the distribution is the average square difference of switch times for pairs of cells with comparable mean switch times ( Figure 4D , blue curve ) . This curve rises rapidly at first , but at longer times it flattens out . This flattening is likely due , at least in part , to the limited duration of our experiments ( on average 920 min ) , which constrains the scatter distribution to reside in the box shown in Figure 4A–4C . To understand what this means , it is helpful to compare our results to those obtained using a stochastic Poisson model [40] , where closely related cells are assumed to switch independently of one another and with constant probability in time ( Text S3 , Figures S2 and S8 ) . To compare directly with our data , we ran the simulation for the same duration as our experiment and included all cell-pair relationships , giving the more complicated curve shown in Figure 4D ( red curve ) . The ratio of the data's mean square variation to that of the Poisson simulation ( Figure 4E , green curve ) is a measure for how correlated cells remain after a given period of time has passed . Points below a value of one ( Figure 4E , dashed line ) represent correlated switching behavior , whereas points above it would signify anticorrelated behavior . For over 600 min , the distribution remains distinctly sub-Poissonian . Only for the longest measured times are there indications that the cells switch independently of their history , and even this is with large uncertainty . Put another way , pairs of cells often remain on approximately the same trajectory for several cell divisions , even though cell growth has diluted many of the relevant proteins to a fraction of their original level . To examine our results at a microscopic level , we constructed a simple model that allows us to probe how the rich correlated switching dynamics arise from a simple regulatory network . Specifically , we asked whether the stochastic fluctuations of a single regulatory protein in our system could simultaneously explain the observed Poisson switching behavior that is expected for randomly selected individuals and subsequent long–timescale correlations . One key protein , Gal80p , functions to regulate the expression of all other genes in the network ( Text S1 ) . When it is present in the nucleus , Gal80p binds in a highly cooperative manner to the transcription factor Gal4p and represses the expression of Gal2p , Gal3p , and YFP ( Volfson et al . , for example , assume a Hill number of 8 between Gal4p and transcription at the GAL1 promoter [33] ) . Such high levels of cooperativity frequently give rise to steep transfer functions , which can result in switch-like behavior . This means that even a small decrease in the concentration of Gal80p can cause the transcription rate of downstream genes to increase dramatically from a very small basal rate to a large maximal rate . Once the downstream protein , Gal3p , begins to be produced , it will lead to sequestering of Gal80p to the cytoplasm , completing the feedback loop and causing the cell to completely switch from the OFF to the ON state . We constructed a simple model that captures the essential properties of this process . In our cells , Gal80p is present in very low numbers , and we therefore account for the effects of stochastic production and degradation for this protein . Protein bursting invariably increases noise levels by amplifying rare events such as changes in promoter activation or mRNA creation and destruction [18 , 22 , 41] . We assumed that the burst-size distribution was exponential in shape with a mean consistent with the results of Bar-Even et al . , who found an average of 1 , 200 proteins per burst [30] . We further assumed that the decay rate of the protein is dominated by dilution and therefore set by the division time of the cell . Finally , we included in our model a nonzero chromophore maturation time of 20 min , as observed in our data . To account for the cooperativity between Gal80p and Gal4p , we assumed that when Gal80p levels drop below a threshold value a cell rapidly activates gene expression and enters the ON fluorescent state . In total , the model has only three parameters: ( 1 ) mean number of Gal80p molecules present per cell , ( 2 ) the switching threshold , and ( 3 ) the Gal80p burst size estimated from literature . We estimated the first two of these parameters by fitting the model to the marginal and conditional switching distributions shown in Figure 3B . Once the theoretical switching rates were fit to the experimental data , we asked if the model explained the highly correlated switching times observed between related cells . Without any additional fitting parameters , we predicted the mother ( τM|D ) and daughter ( τD|D ) conditional switching times ( Figure 5F , brown squares ) as well as their mean squared deviation ( Figure 4E , purple diamonds ) . These predictions matched remarkably well with the experimental data ( Figure 4E , green boxes; Figure 5F , gray circles ) . The model therefore predicts that related cells will remain highly correlated in their switching times even though switching events seem to occur in a Poisson manner . A robustness analysis ( Text S3 , Figure S9 ) suggested a narrow range of possible values with an optimum centered around ( average , threshold ) ∼ ( 2 , 400 proteins , 670 proteins ) . Bursting events in protein production are often associated with increases of noise in protein levels [18 , 22] . A counterintuitive aspect of our model is that the correlation observed in cell pairs comes as a consequence of stochastic bursting . As the burst size is ratcheted up from 12 to the experimentally observed value of 1200 , for example , keeping average protein level and switch rate constant , correlations begin to emerge in the cell-cell scatter plots ( see Figure 5 ) . The reason for this effect is that the periods between bursting events are dominated by dilution of proteins , a relatively low-noise process . As the burst size is increased , the time between bursts must increase commensurately , leading to long periods of correlated behavior between cells . Two cells that start with the same amount of protein will therefore dilute that protein at a similar rate and switch ON ( Figure 5C , black arrows ) at similar times . Decorrelations can arise when one of the cells experiences a burst of new protein during this decay period . However , the cell experiencing the burst has a greatly reduced probability of switching ON in a short period of time . In this event , the cell will generally not be observed to switch at all over the duration of the experiment and consequently does not appear as a significantly decorrelated time-point in the τM|M/τM|D scatter plot .
In recent years , cells within isogenic populations have become increasingly scrutinized as individuals , each with its own original behaviors and gene expression patterns . What make single cells distinctive , however , are not only the stochastic chemical reactions taking place within them but also their unique family histories . Here we have shown that a cell's decision to dramatically change expression states can hinge directly on this familial background . We have separated what , on its face , appears to be an exponentially distributed random process into stochastic and genealogically determined subcomponents . In addition , we show that protein or transcriptional bursting , which are processes that increase total noise in gene expression levels , can unexpectedly create correlated dynamic behavior between related cells , a phenomenon that would be lost in deterministic descriptions . In the engineered network we used in this study , there is no reason to suppose that the correlations we observe provide an evolutionary advantage to cells . However , we can speculate that cells might use similar mechanisms to those we describe to coordinate behavior between themselves without relying on complex sensory machinery or physical proximity . Cells might exploit these architectures to ensure that when a switching event does occur , several other cells will do the same , effectively achieving strength in numbers . For example , a group of infectious disease–causing cells seeking to confront a host immune system might hypothetically choose to switch together from a slowly growing latent phenotype into an active virulent phenotype in a coordinated but randomly timed attack , thus enhancing their likelihood of sustaining an infection . Likewise , cells that benefit from cooperative metabolization could similarly benefit from temporally coordinated cooperation . It will be interesting to see how far similar analysis can be taken in the future and how many other systems might be found to have behavior so strongly influenced by family lineage .
We used the well-characterized GAL network as our model genetic network ( see Text S1 ) . In wild-type cells , transitions between the ON ( galactose metabolizing ) and OFF ( unable to metabolize galactose ) states is largely determined by the levels of inducers ( e . g . , galactose ) or repressors ( e . g . , glucose ) in the surrounding environment . To generate a switching phenotype with large dynamic range , we destabilized this in two ways . First , we removed the negative-feedback loop altogether by replacing the endogenous GAL80 promoter with a weakly expressing , tetracycline-inducible one , PTETO2 . Second , we grew the cells in the absence of galactose , which fully eliminates the GAL2-mediated positive feedback and weakens the GAL3 feedback . Even in the absence of galactose , Gal3p has constitutive activity and , in sufficient quantities , can activate the network [42] . Considering the lower levels of Gal80p in our construct , this constitutive activity is likely a significant factor . Finally , the state of the network is read with PGAL1-YFP , with fluorescing cells considered ON . Cells engineered in this way transition between ON and OFF states in a seemingly stochastic fashion . Cells with this genotype exhibit an extremely broad steady-state expression histogram , with fluorescence values that span more than two orders of magnitude , and the histogram has peaks on both the high and low expression limits , suggesting a bistable system with relatively infrequent transitions between the two states . Before imaging , cells were grown at low optical density overnight in a 30 °C shaker in synthetic dropout media with 2% raffinose as the sole carbon source . This neutral sugar is thought to neither actively repress nor induce the GAL genes [43] . We grew our cells in the absence of tetracycline , so levels of Gal80p were determined by the basal expression level of PTETO2 . Approximately 12 h later , cells were harvested while still in exponential phase , spun down , and resuspended in synthetic defined ( SD ) media . Next , cells were transferred to a chamber consisting of a thick agar pad ( composed of the appropriate dropout media and 4% agarose ) sandwiched between a cover glass and slide . The high agarose density constrains cells to grow largely in a two-dimensional plane . Fluorescent and phase-contrast images of growing cells were taken at intervals of 20–35 min on 10 different days for over 100 initial progenitor cells . Image collection was performed at room temperature ( 22 °C ) using a Nikon TE-2000E inverted microscope with an automated state ( Prior Scientific; http://www . prior . com ) and a cooled back-thinned CCD camera ( Micromax , Roper Scientific; http://www . roperscientific . com ) . Acquisition was performed with Metamorph ( Universal Imaging; http://www . photomet . com ) .
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When cells divide , not only DNA but an entire pattern of gene expression can be passed from mother to daughter cell . Once cell division is complete , random processes cause this pattern to change , with closely related cells growing less similar over time . We measured inheritance of a dynamic gene-expression state in single yeast cells . We used an engineered network where individual cells switch between two semi-stable states ( ON and OFF ) , even in a constant environment . Several generations after cells have physically separated , many pairs of closely related cells switch in near synchrony . We quantified this effect by measuring how likely a mother cell is to have switched given that the daughter cell has already switched . This yields a conditional probability distribution that is very different from the exponential one found in the entire population of switching cells . We measured the extent to which this correlation between switching cells persists by comparing our results with a model Poisson process . Together , these findings demonstrate the inheritance of a dynamic gene expression state whose post-division changes include both random factors arising from noise as well as correlated factors that originate in two related cells' shared history . Finally , we constructed a model that demonstrates that our major findings can be explained by burst-like fluctuations in the levels of a single regulatory protein .
|
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"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"mathematics",
"yeast",
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2007
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Heritable Stochastic Switching Revealed by Single-Cell Genealogy
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Harnessing genetic differences between cancerous and noncancerous cells offers a strategy for the development of new therapies . Extrapolating from yeast genetic interaction data , we used cultured human cells and siRNA to construct and evaluate a synthetic lethal interaction network comprised of chromosome instability ( CIN ) genes that are frequently mutated in colorectal cancer . A small number of genes in this network were found to have synthetic lethal interactions with a large number of cancer CIN genes; these genes are thus attractive targets for anticancer therapeutic development . The protein product of one highly connected gene , the flap endonuclease FEN1 , was used as a target for small-molecule inhibitor screening using a newly developed fluorescence-based assay for enzyme activity . Thirteen initial hits identified through in vitro biochemical screening were tested in cells , and it was found that two compounds could selectively inhibit the proliferation of cultured cancer cells carrying inactivating mutations in CDC4 , a gene frequently mutated in a variety of cancers . Inhibition of flap endonuclease activity was also found to recapitulate a genetic interaction between FEN1 and MRE11A , another gene frequently mutated in colorectal cancers , and to lead to increased endogenous DNA damage . These chemical-genetic interactions in mammalian cells validate evolutionarily conserved synthetic lethal interactions and demonstrate that a cross-species candidate gene approach is successful in identifying small-molecule inhibitors that prove effective in a cell-based cancer model .
Cancerous cells carry somatic mutations that genotypically distinguish them from surrounding noncancerous cells , and this provides an opportunity that can be exploited for therapeutic development . One strategy for the specific targeting of cancer genotypes relative to nonmutated somatic cells is to exploit synthetic lethal interactions [1] . For example , breast cancer cells with mutations in BRCA1 or BRCA2 are extremely susceptible to knockdown or chemical inhibition of PARP1 , which encodes poly ( ADP ) ribose polymerase ( PARP ) [2] , [3] . While exploiting synthetic lethality has the potential to be an effective approach to treating tumors , a major challenge is the identification of clinically relevant small-molecule inhibitors . One approach , pioneered by the National Cancer Institute , is to screen many thousands of unknown potential therapeutics on cancer cell lines [4] . Compounds generate a “fingerprint” of activity against certain cell lines , which can then be deconvolved , usually by mutation sequencing , to yield novel gene-drug interactions , in a so-called “bottom-up” approach . Alternatively , a “top-down” approach applies compounds of known target or mode of action to known genotypes , again to identify new gene-drug interactions . Recently , two groups used such an approach to screen more than 100 compounds against hundreds of cancer cell lines whose mutational status was known [5] , [6] , observing that gene-drug interactions tended to be more significant for targeted therapies , such as compounds targeting the BCR-ABL fusion protein , than for generally cytotoxic drugs , such as DNA damaging agents or antimitotics [6] . Thus , screening for compounds targeting a specific genetic lesion is preferable to developing new cytotoxic agents . Such targeted compounds can then be deployed as first-line anticancer therapeutics either singly or in a combination regime that would lessen the likelihood of drug-resistant clones developing within the tumor cell population [7] , [8] . Many different cancer mutations lead to a limited repertoire of cancer phenotypes , such as chromosome instability , checkpoint dysfunction , and hyperplasia [9] . It is possible to identify a gene target that results in synthetic lethality with a large number of unlinked gene mutations by screening for targets that result in synthetic lethality with a common tumor phenotype . For example , chromosome instability ( CIN ) , an increase in the rate of gain or loss of whole or parts of chromosomes , is observed in the form of aneuploidy in more than 90% of solid tumors and over 75% of blood cancers [10] . As the maintenance of genomic stability is an essential cellular process , CIN represents a phenotype that could potentially be leveraged towards selective killing of cancerous cells relative to normal cells . A gene that is synthetic lethal with a large number of cancer-related CIN genes would be an attractive therapeutic target in a large fraction of tumors . Genetically tractable model organisms , such as the budding yeast Saccharomyces cerevisiae , facilitate the identification of human CIN genes , via identification and sequencing of their human orthologs . For example , identification of yeast CIN genes [11] ) led to the sequencing of the human homologs of 200 yeast CIN genes in human colorectal cancers , and it was discovered that human homologs of the yeast CIN genes SMC1 , SCC2 , BUB1 , PDS1 , MRE11 , and CDC4 collectively account for approximately 25% of the mutational spectrum of colorectal cancer [12]–[15] . Thus , if a common synthetic lethal interacting partner could be identified for all of these genes , and a highly potent and specific inhibitor of its activity could be developed , inhibition of this target would offer a potentially broad means of targeting CIN cancers . In yeast , technologies exist to screen for genome-wide synthetic lethal interactions with relative ease [16] , and identification of the synthetic lethal interaction network of the yeast orthologs of cancer-mutated genes has in previous cases revealed a small number of “hub” genes having synthetic lethal interactions with many yeast cancer-orthologs [17] . Previous studies have found a high degree of conservation between yeast and metazoan genetic interactions [18] , [19] , suggesting hub gene identification based on a yeast CIN gene synthetic lethal interaction network should yield broad-spectrum , second-site target genes applicable to human cancers . Here we present and validate a cross-species candidate-based approach to the identification of anticancer targets and the discovery of anticancer therapeutics . We show that a genetic interaction network comprised of colorectal cancer CIN genes is largely conserved between S . cerevisiae and a human cancer cell line . We develop an in vitro assay for the activity of the protein encoded by one such highly connected gene , FEN1 , and use this assay to screen for small-molecule inhibitors . Finally , we show that flap endonuclease inhibitors recapitulate conserved genetic interactions . These data demonstrate the effectiveness of a cross-species synthetic lethal approach to the discovery of potential anticancer therapeutics .
The human genes SMC1 , SMC3 , NIPBL , STAG3 , RNF20 , FBXW7/CDC4 , MRE11A , RAD54B and BLM have been found to be mutated in colorectal cancer , and together account for approximately 25% of the CIN mutational spectrum of this disease [13]–[15] , [20]–[22] . Protein BLAST was used to identify the budding yeast orthologs of these human genes ( Table 1 ) and we constructed a synthetic lethal interaction network ( Figure 1A ) , using literature and publicly available genetic interaction data ( BioGrid and the Saccharomyces Genome Database ) [18] , [23] . To investigate the conservation of this network between yeast and a human cell line , we used siRNA-mediated knockdown of potential synthetic lethal gene pairs in the cell line HCT116 . Knockdown efficiencies were evaluated by Western blots ( Figure S1A ) . All pair-wise combinations between the three “central” synthetic lethal partner genes , WDHD1 , FEN1 , and CHTF8 , and the ten outer cancer-mutated CIN genes were evaluated for synthetic lethality ( Figure 1B , 1C , 1D ) . ( CHTF8 was selected as a representative of the alternative RFCCHTF18 , comprised of Dcc1 , Ctf8 , and Ctf18 in S . cerevisiae ) . Of the 30 possible synthetic lethal interactions among the genes tested , 22 have been reported in yeast [18] , [23] . We found 16 of the predicted interactions ( 73% ) were conserved between yeast and human cells , and 6 predicted interactions did not appear conserved in our assay ( 27% ) . Furthermore , one interaction , between FEN1 and STAG1 , was not predicted based on yeast data; however , we detected a genetic interaction between these genes ( Figure 1F and Table S1 ) . No interactions were observed with STAG3 , which functions primarily in human meiosis [24] . As in yeast , all three central genes – WDHD1 , FEN1 , and CHTF8 – were highly connected to sister chromatid cohesion genes ( e . g . cohesin and/or cohesin loaders ) ( Figure 1F ) . As FEN1 encodes an enzyme , whereas WDHD1 and CHTF8 do not; it may be amenable to biochemical inhibitor screening . Thus , we sought to further validate genetic interactions between FEN1 and other genes in the network . To ensure that these observed interactions were not cell line-dependent , we attempted to recapitulate interactions between FEN1 and each of CDC4 , RAD54B , and RNF20 in the karyotypically stable , immortalized fibroblast cell line hTERT . As in HCT116 cells , genetic interactions were observed following knockdown of all three gene pairs ( Figure 1E , Table S2 , Figure S1C ) . We found that individual siRNAs could recapitulate the genetic interactions observed with the pooled siRNAs ( Table S3 ) . These data validate a subset of genetic interactions identified in the HCT116 cells and thus confirm FEN1 as a strong candidate therapeutic target . FEN1 ( Flap ENdonuclease 1 ) encodes an enzyme previously shown to be amenable to biochemical assay development in vitro [25] that has been implicated in almost all DNA transactions , including DNA repair and replication [26] ) . Adapting a previous radiolabel-based in vitro assay , we developed an in vitro assay for FEN1 activity based on fluorescence quenching [25] ) . In this assay , three oligonucleotides are annealed to generate the synthetic substrate , positioning a fluorophore and fluorescent quencher in close proximity . The flap endonuclease activity of FEN1 cleaves the 5′ flap to which the fluorophore is attached , allowing it to diffuse away from the quencher and fluoresce ( Figure 2 ) . Using a potent , previously described in vitro FEN1 inhibitor , compound 16 from Tumey , et al . [27] , we observed significant inhibition of flap endonuclease activity ( Figure 3 , upper left panel ) . A screen of 30 000 compounds , from libraries containing known and FDA-approved drugs , and the Canadian Chemical Biology Network library , yielded approximately 90 hits , following a counterscreen using a quencherless substrate to eliminate false positives caused by fluorescent compounds and fluorescent quenchers . Ultimately , 13 compounds were selected for further investigation based on structural diversity and having drug-like properties ( as described by Lipinski's “Rule of Five”; [28] ) . These compounds were found to have mid-nanomolar to low micromolar IC50s in vitro ( Figure 3 , remaining panels ) . We next sought to determine whether the flap endonuclease inhibitors we identified could recapitulate any of the genetic interactions found previously ( Figure 1F ) . We first targeted the interaction between FEN1 and CDC4 , owing to the fact that CDC4 has been shown to be a CIN gene mutated in many tumor types [11] , [29]–[32] . We took advantage of a matched pair of cell lines in which both copies of CDC4 had been inactivated in HCT116 cells [13] . siRNA-mediated knockdown of FEN1 in this cell pair resulted in selective proliferation inhibition ( Figure 4A ) . We applied the small-molecule hits from the screen to this matched pair of cell lines and found six compounds that selectively inhibited the proliferation of CDC4-knockout HCT116 cells relative to wild type cells ( Figure 4B and Figure S2B ) . To ensure that these results were not cell line-specific , we utilized another matched pair of cell lines with inactivated CDC4 , this time in DLD-1 cells . The six compounds showing selective proliferation inhibition of CDC4-knockout HCT116 cells were applied to CDC4-knockout and wild type DLD-1 cells [13] , and RF00974 and NSC645851 were found to selectively inhibit the proliferation of CDC4-knockout DLD-1 cells relative to wild type ( Figure 4C ) . To further test the idea that CDC4 activity is responsible for the observed effect , cells in which CDC4 had been inactivated in a heterozygous state were also treated with RF00974 and NSC645851 . As with homozygous CDC4−/− cells , heterozygous CDC4+/− cells displayed a statistically significant decrease in proliferation relative to wild type CDC4+/+ cells , albeit lesser in magnitude ( Figure 5 ) . We next attempted to recapitulate the interaction between FEN1 and MRE11A , as MRE11A has been shown to be mutated at a frequency of 4% in colorectal cancers [15] . We treated cells in which MRE11A had been depleted via siRNA with the more potent of the two flap endonuclease inhibitors described above , RF00974 , and found that MRE11A depletion sensitized cells to flap endonuclease inhibitor treatment ( Figure 6A ) . We also found that treatment with a previously-described small-molecule inhibitor of MRE11A , mirin [33] , was able to sensitize cells to treatment with RF00974 ( Figure 6B ) . Taken together , these data suggest that inhibition of flap endonuclease activity is sufficient to recapitulate evolutionarily conserved , colorectal cancer-relevant synthetic lethal genetic interactions . Finally , we wished to characterize the mechanism by which inhibition of flap endonuclease activity may lead to cell death . Given the role of FEN1 in DNA replication and repair , we asked whether endogenous DNA damage increases as a result of FEN1 inhibition . We used HCT116 cells in which 53BP1 had been stably tagged with mCherry to ask whether 53BP1 focus formation , indicative of DNA repair centers [34] , [35] , increased . We found a statistically significant ( p<0 . 05 ) increase in the frequency of cells with many 53BP1 foci following siRNA-mediated knockdown of FEN1 . Furthermore , we observed a similar increase ( p<0 . 05 ) following treatment with the flap endonuclease inhibitor RF00974 ( Figure 6C ) . We next measured the level of H2AX phosphorylation ( γ-H2AX ) , an independent indicator of DNA damage [36] , in HCT116 CDC4+/+ and CDC4−/− cells in response to RF00974 . We found that , similar to increasing 53BP1 focus formation , RF00974 treatment increased H2AX phosphorylation ( Figure S3 ) . H2AX phosphorylation was increased even in untreated HCT116 CDC4−/− cells , so no increase in phosphorylation was observed . In order to determine whether RF00974 leads to an increase in apoptosis in CDC4-deficient cells , we asked whether PARP cleavage , a marker of apoptosis [37] , is increased following RF00974 treatment . We found that RF00974 treatment did not increase PARP cleavage in either wild type or CDC4-deficient cells . Taken together , these results suggest that loss of FEN1 , or inhibition of flap endonuclease activity , lead to an increase in endogenous DNA damage that inhibits the proliferation of CDC4-deficient cells by non-apoptotic means .
In this study , we used a cross-species candidate approach to identify new anticancer therapeutic targets for small-molecule inhibition having a potentially broad spectrum of applicability . We found that a yeast CIN synthetic lethal interaction network is largely conserved between S . cerevisiae and a human tumor cell line . Based on this network , we screened for in vitro inhibitors of the highly connected enzyme FEN1 . Flap endonuclease inhibitors discovered in this screen recapitulated synthetic lethal interactions between FEN1 and each of CDC4 and MRE11A , demonstrating that evolutionarily conserved genetic interactions in a core cellular process , such as the maintenance of genomic stability , can be exploited as a means to inhibit the proliferation of tumor cells carrying specific and cancer-relevant mutations . The idea of using the unique genetic profile of tumor cells relative to somatic cells to selectively kill cancer has been applied by various groups , such as in the case of the chemical-genetic interaction between BRCA1/2 and PARP inhibitors [2] , [3] . Several studies have focused on DNA damage , usually by identifying inhibitors of DNA damage response proteins that either directly kill tumor cells , or that potentiate the effects of DNA damaging agents [38]–[42] . Recently , two large-scale studies examining chemical-genetic interactions between new or established anti-cancer treatments and cancer cell lines of known genotype demonstrated the promise of such top-down approaches by identifying previously unknown sensitivities of many cancer genotypes , such as between Ewing's sarcomas and PARP inhibitors [5] , [6] . An alternative means to construct genetic interaction networks for the discovery of therapeutic targets is to take a cross-species candidate approach in a genetically tractable model organism . In S . cerevisiae , defined genetic changes can be introduced and subsequently screened in a high-throughput manner [16] , [43] ( though mammalian genome editing technologies are advancing rapidly [44] , [45] ) . The nearly 75% ( 16/22 ) conservation of synthetic lethal interactions we found between yeast and human cells is similar to the degree of conservation of genetic interactions between S . cerevisiae and the model metazoan Caenorhabditis elegans in a related network , identified by our group and others [18] , [19] , [46] , and expands upon previous proof-of-principle work by our group [47] . Although we ultimately targeted the highly conserved flap endonuclease FEN1 in the current study , yeast genetic data has the potential to implicate biological processes , as opposed to specific proteins , as therapeutic targets; in this way , targets can be identified that are not conserved in S . cerevisiae . For example , we recently demonstrated that mutation of cohesin genes in yeast was synthetic lethal with mutation of proteins playing a role in replication fork stability . siRNA-mediated knockdown of cohesin genes was found to sensitize human cells to inhibition of PARP , a protein involved in replication fork progression , but without a known ortholog in yeast [19] . Thus , the versatility of yeast synthetic lethal networks to predict therapeutic targets makes our approach complementary to large-scale screening for gene-drug interactions [4]–[6] . Therapeutics that target a specific genotype , such as EGFR family inhibitors in the case of ERBB2 ( also known as HER2 ) amplification , produce more significant gene-drug interactions than more general cytotoxic agents [6]; however , the indications for such agents are limited to a handful of genotypes . FEN1 plays a critical role in nearly all DNA transactions , including DNA replication via Okazaki fragment maturation [48] , [49] , long-patch base excision repair [50] , [51] , the prevention of trinucleotide repeat expansions [25] , [52] , and restart of stalled replication forks [53] . Yeast RAD27 is one of the most highly genetically connected genes in the yeast genome ( Tables S5 and S6 ) ; many of these interactors are CIN genes [11] , and many of the corresponding human orthologs may prove to be mutated and cause CIN in tumours . Given that the majority of the genetic interactions were conserved in the CIN synthetic lethal interaction network interrogated here , FEN1 may be a widely applicable target in cancers harboring mutations in a variety of CIN genes . More generally , DNA repair and replication protein inhibitors are being actively developed as anticancer therapeutics [2] , [3] , [41] , [54] and the process of DNA replication forms a genetic hub in S . cerevisiae [16] , [23] , [43] , [55] . The critical role of FEN1 in DNA transactions is analogous to that of PARP , a protein playing a role in DNA repair and the protection of stalled DNA replication forks [56] , [57] . PARP is synthetic lethal with mutations in BRCA1/2 [2] , [3] , and its therapeutic range has been extended more recently to include cells with mutations in PTEN [38] and cohesins [19] . Thus , like PARP , FEN1 potentially represents a potent , broadly-applicable target for anticancer therapeutic development . In turn , the ideal anticancer therapeutic would have a broad spectrum , suggesting it would be more advantageous to target a phenotype common in cancer . CIN in the form of aneuploidy is seen in >90% of solid tumors [10] and represents a sub-lethal mutation in an otherwise essential process . Of relevance to the current work , moderate aneuploidy and CIN correlate with poor prognosis in cancer , but extreme aneuploidy correlates with improved patient outcomes [58] , [59] . Yeast RAD27 is a CIN gene [17] , and FEN1 mutation in various systems leads to CIN and has been associated with cancer [17] , [60]; thus , inhibition of FEN1 in cancers that already exhibit CIN could lead to a level of CIN incompatible with viability . In the present study , flap endonuclease inhibitors were found to recapitulate the synthetic lethal interactions between FEN1 and each of CDC4 and MRE11A [18] , [23] . We observed that both depletion and inhibition of flap endonuclease activity led to an increase in endogenous DNA damage . Recent reports have shown that γ-H2AX levels are not increased following FEN1 depletion [61]; however , we observed increases in DNA damage using two independent assays following two means of FEN1 inhibition , and attribute these results to cell background differences , such as the mismatch repair deficiency present in HCT116 cells . Furthermore , this increase in DNA damage led to a non-apoptotic inhibition of proliferation . Thus , one explanation for the lethality in combination with inactivation of CDC4 is that the cell is inappropriately driven through the cell cycle , owing to elevated levels of cyclin E [13] , when otherwise it would arrest to try to repair DNA damage . Likewise , increased endogenous DNA damage combined with loss of MRE11A , a protein playing a critical role in the first steps of the DNA damage response [62] , could lead to a level of DNA damage or mutation that is incompatible with proliferation . CDC4 has been reported to be mutated in a wide variety of tumor types , at frequencies ranging from 6% to >30% , depending on the tumor type [13] , [21] , [29] , [63] , [64] . Recently , it has been suggested that reduction of CDC4 activity to some level below that of wild type , but above complete abrogation of function , is optimal for tumor progression [63] . Thus , the fact that two flap endonuclease inhibitors described here were able to selectively inhibit the proliferation of both heterozygous and homozygous CDC4-knockout cell lines suggests that CDC4 loss , whether complete or partial , sensitizes cells to inhibition of flap endonuclease activity . As well , the fact that both genotypes were sensitive to inhibition of flap endonuclease activity adds weight to the suggestion that this response is specific to CDC4 activity , in the same way that changing response following alteration in dosage in biochemical screening is suggestive of target identity [65] . In summary , here we have presented a rational , cross-species approach to the identification of anticancer therapeutic targets by targeting CIN , a common cancer phenotype . The use of conserved synthetic lethal interaction networks to identify highly-connected second-site targets is an accessible alternative to large scale screens: it narrows down the number of synthetic lethal gene pairs to be directly retested from tens of thousands to dozens , and is based on strong synthetic lethal interactions discovered in yeast networks . We have demonstrated the potential of this approach to identify targets and therapeutics , such as FEN1 and the flap endonuclease inhibitors described here , having potentially broad applicability in the treatment of cancer .
HCT116 cells were purchased from ATCC . HCT116 derivatives , DLD-1 and DLD-1 derivatives were gifts of Dr . Bert Vogelstein ( Johns Hopkins University ) . ( Importantly , we observed that the deleted exon in CDC4 in these cell lines is not exon 5 , as previously reported [13] , but exon 8 . We attribute the difference to changing annotations in public sequence databases between 2004 and the present . ) 53BP1-mCherry HCT116 cells were a gift of Dr . Sam Aparicio ( UBC ) . These cells were grown in McCoy's 5A medium with 10% FBS . Immortalized ( telomerase ) BJ normal human skin fibroblasts , hTERT [66] , were generously provided by Dr . C . P . Case ( University of Bristol ) and were grown in DMEM containing 10% FBS . Mirin was purchased from Sigma-Aldrich . RF00974 was purchased from Maybridge , Ltd . Western blots were performed as detailed elsewhere [47] . Antibodies used for Western blots are described in Table S4 . Subconfluent and asynchronous cells were transiently transfected with siRNAs . HCT116 cells were transfected with ON-TARGETplus siRNA pools at a total siRNA concentration of 25 nM using DharmaFECT I ( Dharmacon ) . In dual siRNA experiments , the total siRNA concentration was 50 nM . Cultures were replenished with fresh medium 11 hours after transfection . hTERT cells were transfected with ON-TARGETplus siRNA pools , or independent duplexes , at a total siRNA concentration of 100 nM using RNAiMax ( Invitrogen ) . Cultures were replenished with fresh medium 24 hours after transfection . HCT116 cells were harvested 24 hours after siRNA transfection and re-plated in 96-well optical bottom plates . hTERT cells were transfected directly in 96-well plates . HCT116 cells were fixed four days after transfection , and hTERT cells were fixed seven days after transfection , in 4% paraformaldehyde/PBS . Nuclei were labelled with Hoechst 33342 . Stained nuclei were counted using a Cellomics Arrayscan VTI fluorescence imager as described previously [47] or a Zeiss AxioObserver Z1 equipped with an LED Colibri light source , a 20× plan apochromat dry lens ( numerical aperture = 0 . 8 ) and AxioVision v4 . 8 software . Images were analyzed using the Physiology Analyzer ( Assaybuilder ) option within the AxioVision software . Data were normalized to GAPDH-silenced controls and conventional statistics ( e . g . column statistics and Student's t-tests ) were performed . Experiments were performed twice; indicated numbers are averaged from at least 6 wells . To determine the presence of a synthetic lethal interaction , the proliferative defect was calculated , and is defined aswhere the predicted proliferation was the product of the proliferation of the two individual gene knockdowns , following a multiplicative model of genetic interactions [67] . Synthetic lethal interactions were scored as a proliferative defect of three times the average SEM of the experiment or greater . During compound incubation experiments , cells were incubated in compound of interest in 96-well optical bottom plates for approximately three days prior to fixation and analysis . Data ( from six independent wells ) were analyzed using a one-way ANOVA followed by a Tukey test . FEN1 was expressed in BL21 E . coli from pET28b ( + ) ( a generous gift from R . Bambara , University of Rochester ) using 1 mM IPTG . Bacteria were lysed in lysis buffer ( 50 mM NaH2PO4 , 300 mM NaCl , 10 mM imidazole , pH 8 . 0 containing 2× protease inhibitor ) via a French press at 10 000 psi . The lysate was clarified and passed through a 0 . 22 µM filter before being loaded onto a HisTrap FF column ( 1 mL , GE Healthcare ) in an ÄKTAFPLC P-920 system ( GE Healthcare ) . The column was washed in 10 volumes of wash buffer ( lysis buffer+20 mM imidazole ) , and FEN1 was eluted with 5 volumes of elution buffer ( lysis buffer+125 mM imidazole ) . The lysate was diluted with 9 volumes HI buffer ( 30 mM HEPES-KOH , 0 . 5% myo-inositol , pH 7 . 8 ) with 30 mM NaH2PO4 and concentrated in a protein concentrator ( Amicon ) . It was then loaded onto a hydroxyapatite resin ( HA Ultrogel , Pall Life Sciences ) . The hydroxyapatite resin was washed with 10 volumes of HI-30 mM PO4 , and FEN1 was eluted with 5 volumes of HI-200 mM PO4 . The eluate was diluted with 5 volumes HI-30 mM KCl prior to concentration , and then loaded onto a strong cation exchange column ( 1 mL HiTRAP SP FF FPLC , GE Healthcare Life Sciences ) . The column was washed with 10 volumes of HI-30 mM KCl , then 10 volumes of HI-200 mM KCl , and FEN1 was eluted with a gradient from HI-200 mM KCl to HI-500 mM KCl over 10 column volumes . Purified FEN1 was concentrated in FEN1 dilution buffer ( 30 mM HEPES-KOH , 5% glycerol , 0 . 1 mg/mL BSA , 0 . 01% NP-40 ) , and aliquots of known concentration were frozen at −80°C . Oligonucleotides used were as follows: “template” , 5′-GGTGGACGGGTGGATTGAAATTTAGGCTGGCACGGTCG-3′ , “upstream” , 5′-CGACCGTGCCAGCCTAAATTTCAATC-3′ , “downstream” , 5′-6-FAM-CCAAGGCCACCCGTCCAC-BHQ-1-3′ . ( 6-FAM is 6-carboxyfluorescein; BHQ-1 is black hole quencher 1 . ) The three oligonucleotides were annealed at equimolar amounts in annealing buffer ( 50 mM Tris , 50 mM NaCl , 1 mM DTT , pH 8 . 0 ) by heating to 94°C , cooling to 70°C , and gradually cooling to room temperature . FEN1 assays were carried out with 6 pmol FEN1 and 20 nM annealed substrate in FEN1 buffer ( 50 mM Tris pH 8 . 0 , 30 mM NaCl , 8 mM MgCl2 , 0 . 1 mg/mL BSA , 2 mM DTT ) . Assays were carried out at room temperature and kinetic reads were taken over approximately ten minutes in a Varioskan plate reader ( Thermo Fisher Scientific ) , using excitation and emission wavelengths of 492 nm and 517 nm , respectively . 53BP1-mCherry cells were grown on cover slips . Following desired treatment ( either two hours of bleomycin treatment at 5 µg/mL , four days following siRNA transfection , or after 24 hours of RF00974 treatment at 10 µM ) , cells were fixed for five minutes in 4% paraformaldehyde/PBS , mounted in Vectashield mounting medium containing DAPI ( 500 ng/mL ) , and imaged on a Zeiss Axioplan microscope with a Coolsnap HQ camera , using appropriate filters and controlled by Metamorph software . Cells were treated with RF00974 for 48 hours prior to harvesting of medium and cells in lysis buffer ( 50 mM Tris , 150 mM NaCl , 1% Triton-X-100 , pH 7 . 5 ) . Lysates were sonicated and clarified by centrifugation at 13 000 rpm for 15 minutes at 4°C . As a positive control , HCT116 cells were treated with 1 µM staurosporine prior to harvesting . Lysates were subjected to Western blotting as described above . Synthetic genetic array analysis of rad27Δ against a collection of yeast essential DAmP alleles [68] and temperature sensitive alleles [69] was carried out as described previously [11] , [19] .
|
Anticancer therapeutic discovery is a major challenge in cancer research . Because cancer is a disease caused by somatic genetic mutations , the search for anticancer therapeutics is often driven by the ability to exploit genetic differences specific to tumor cells . Recently , cancer therapeutic development has sought to exploit synthetic lethality , a situation in which the combination of two independently viable mutations results in lethality . If a compound can be found to selectively kill a specific genotype via inhibition of a specific gene product , this is known as a chemical-genetic interaction , and it mimics a synthetic lethal genetic interaction . The ideal therapeutic would be broad spectrum , that is , active against multiple cancer genotypes within a tumor type and/or across a variety of cancers . We have developed an approach , taking advantage of the evolutionary conservation of synthetic lethal interactions , to identify “second-site” targets in cancer: genes whose chemical inhibition leads to selective killing of tumor cells across a broad spectrum of cancer genotypes . We identified small-molecule inhibitors of one such target , FEN1 , and showed that these compounds were able to selectively kill human cells carrying cancer-relevant mutations . This approach will facilitate the development of anticancer therapeutics active against a variety of cancer genotypes .
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2013
|
An Evolutionarily Conserved Synthetic Lethal Interaction Network Identifies FEN1 as a Broad-Spectrum Target for Anticancer Therapeutic Development
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Effective transfer of genetic information during cell division requires a major reorganization of chromosome structure . This process is triggered by condensin , a conserved pentameric ATPase essential for chromosome condensation . How condensin harnesses the energy of ATP hydrolysis to promote chromatin reorganization is unknown . To address this issue , we performed a genetic screen specifically focused on the ATPase domain of Smc4 , a core subunit of condensin . Our screen identified mutational hotspots that impair condensin’s ability to condense chromosomes to various degrees . These mutations have distinct effects on viability , genome stability , and chromosome morphology , revealing unique thresholds for condensin enzymatic activity in the execution of its cellular functions . Biochemical analyses indicate that inactivation of Smc4 ATPase activity can result in cell lethality because it favors a specific configuration of condensin that locks ATP in the enzyme . Together , our results provide critical insights into the mechanism used by condensin to harness the energy of ATP hydrolysis for the compaction of chromatin .
Chromosomes must undergo a major structural reorganization to allow the efficient and error-free segregation of genetic information during mitosis . This process is known as chromosome condensation and is promoted by the condensin complex [1–3] . The condensation of chromosomes has been described more than a century ago , and yet the molecular mechanism by which amorphous chromatin is reorganized into highly compact chromosomes is still poorly understood . The main effector of this reorganization is the condensin enzyme , a nuclear complex composed of two structural maintenance of chromosomes ( SMC ) proteins ( Smc2 and Smc4 ) , a kleisin subunit ( Brn1/chromosome-associated protein H [CAP-H] ) , and two HEAT repeat–containing subunits ( Ycg1/CAP-G and Ycs4/CAP-D ) ( reviewed in [4] ) . Condensin is a multifunctional enzyme that can bind and reanneal nucleic acids , as well as constrain knots and supercoils in the primary structure of DNA , which likely affect the topological configuration of chromosomes [5–8] . Importantly , the role of condensin during chromosome condensation depends largely on its mechanochemical properties and inherent ATPase activity . The catalytic core of the condensin complex is formed by two SMC family subunits , a group of proteins involved in sister chromatid cohesion , DNA repair , and chromosome condensation ( reviewed in [4 , 9] ) . SMC proteins are highly conserved in eukaryotes , and they are also found in other domains of life . The domain organization of this group of proteins is essential for function and is characterized by three globular domains ( an N-terminal ATPase , a central hinge , and a C-terminal ATPase ) separated by two alpha-helical segments [4] . To allow interactions between the terminal globular regions and create a functional ATPase “head domain , ” individual SMC proteins fold back on themselves into a structure resembling a twisted hairpin , as suggested in early studies from the Earnshaw laboratory [10 , 11] and later confirmed by electron microscopy imaging [12] . Within condensin , interactions between the head domains of Smc2 and Smc4 create a bipartite ATPase unit that shares significant similarity with the extensively studied ATP-binding cassette ( ABC ) transporters [11] . In the case of ABC transporters , global structural rearrangements associated with the hydrolysis of ATP allow the translocation of small molecules through membranes . Despite the deep similarities between the ATPase domains of SMC proteins and ABC-type transporters , it has been difficult to extrapolate how condensin ATPase activity might promote chromosome compaction from our knowledge of the mode-of-action of ABC transporters . The functional relevance of ATPase activity has also been studied in other SMC complexes , most notably in cohesin . This complex , composed of a catalytic core of Smc1 and Smc3 proteins , is required to hold the sister chromatids together during mitosis and meiosis [4] . In cohesin , ATP and its hydrolysis are necessary for DNA entrapment [13 , 14] and for tethering cohesin to chromosomes [15 , 16] . More recently , it has been shown that Smc3 can sense the presence of DNA and induce cohesin ring opening thanks to its ATPase activity [13] . In addition , Çamdere and colleagues have suggested that Smc1 and Smc3 ATPase head domains may work in a cooperative manner at different steps of the cohesion process [16] . The distinct molecular functions associated with different ATPase head domains could explain some of the asymmetry observed among SMC proteins within their cognate complexes , as previously observed with ABC transporters [17] . With respect to condensin , it is known that ATP hydrolysis plays an essential role in its function , as the abrogation of this activity leads to cell inviability [18 , 19] . Moreover , condensin activity is required even after chromosome assembly is apparently completed [20] , suggesting that its ATPase activity must be continually active throughout mitosis to maintain chromosome structure . With respect to the mechanism of action , condensin binding to DNA is ATP independent [5 , 18] , whereas its ability to constrain positive superhelical tension in double-stranded DNA ( dsDNA ) requires ATP [7] . Based on these observations , it has been suggested that ATP binding and hydrolysis may promote conformational changes in condensin that would allow DNA entrapment within its structure [19 , 21] . Ultimately , a key question for chromosome biology is how condensin uses the energy of ATP hydrolysis to convert amorphous chromatin into highly compacted chromosome in vivo . Since all ATPase-specific mutants tested so far in condensin are inviable ( e . g . , mutations in Walker A/B and C-motifs required for nucleotide binding and hydrolysis ) , it has been difficult to delineate the specific contribution of condensin ATP hydrolysis to the process of chromosome condensation . To address this important question , it is necessary to identify weakened or hypomorphic alleles of condensin that , despite being catalytically active , are defective enough to stabilize intermediate states in the process of chromosome compaction . To achieve this goal , we report here the characterization of an extensive collection of smc4 mutants specifically defective in condensin ATPase activity .
The ABC-type ATPase domain is a characteristic feature of SMC proteins [11] . This family of ATPase domain is relatively uncommon in eukaryotes and is defined by the presence of 6 unique sequence motifs ( A- , R- , Q- , D- , H-loops , and C-motif ) in addition to the Walker A ( P-loop ) and Walker B consensus sequences found in most ATPases [22–26] ( Fig 1A ) . Also conserved in ABC-type ATPase are the Pro-loop and C-helix regions adjacent to the Walker B and C-motifs , respectively [24 , 27] . The latter two regions have recently been connected to the hydrolysis of nucleotides and thus appear to be bona fide ATPase motifs . The ABC-type ATPase domain is most frequently found in membrane transporters , and an alignment of the amino acid sequence of typical human ABC transporters ( MDR1 and CFTR1 ) with the ATPase domains of condensin SMC subunits reveals a strong evolutionary conservation of the ABC-specific sequence motifs in SMC proteins from various eukaryotes ( Fig 1B ) . In fact , only the region encoding the H-loop and adjacent C-terminal sequence of the domain appears to diverge significantly in primary sequence among ABC-family ATPases ( S1 Fig ) . The role of most ABC-specific ATPase motifs has not been extensively characterized in SMC proteins . In contrast , the specific contribution of these motifs to the mode of action of ABC transporters is well documented ( reviewed in [29] ) . These studies showed that the D-loop and the signature motif of one ATPase “head domain”/monomer cooperates with the active sites of the opposite ATPase head domain to allow the hydrolysis of ATP [31–34] . With respect to the role of each motif , the A- , P- , and H-loops contribute to the position and binding of ATP and cofactors required for catalysis , whereas the Walker B , D-loop , Q-loop , and C-motif are directly involved in ATP hydrolysis [29] . These analyses , together with the strong sequence conservation in ABC motifs , allowed us to predict the positions of Smc4 residues involved in ATP hydrolysis , as described schematically in Fig 1C . To further validate the putative nucleotide–enzyme interactions , we also modeled Smc4 ATPase domain using crystallographic information from S . cerevisiae Smc1 ( ScSmc1 ) ( Fig 1D ) [30 , 35] . ScSmc1 was used as a template because it shows substantial sequence identity to Smc4 ( 38% identity over their ATPase domains ) , and a high-resolution structure is available for its ATPase head domain . Overlap of known SMC structures from ScSmc1 and P . furiosus Smc ( PfSmc ) illustrates a high degree of conservation in structural organization within this family of proteins , even when primary sequence identity is weaker than that shared by Smc1 and Smc4 ( i . e . , 31% sequence identity in ScSmc1 and PfSmc ATPase head domain; S2 Fig ) . Importantly , our in silico model of Smc4 head domain points to a number of key residues that might interfere with ATP binding and/or hydrolysis when mutated ( Fig 1D ) . Based on this information , we selected residues at 24 distinct positions within Smc4 ATPase domain for in-depth mutagenesis and phenotypic characterization ( Fig 1B; positions marked with dots ) . These positions encompass all canonical ABC-type motifs as well as other residues that are predicted to be involved in catalysis . Of these , 18 correspond to residues that were not previously analyzed in SMC proteins ( positions previously mutagenized are marked with a star in Fig 1B ) . We focused on residues that were highly conserved in each motif , as well as adjacent residues that are less conserved to avoid potential lethal mutations . We also mutated 2 regions not related to conserved ABC motifs , namely Ile1364-Val1365 and Gly1396 , both of which are conserved in Smc4 but not in Smc2 . Mutagenesis of Gly1396 was of particular interest because this residue is conserved in all eukaryotic Smc4s and prokaryotic SMCs but not in Smc2 nor ABC transporters ( see alignment in S1 Fig ) . It was previously suggested that SMC proteins can be classified in two different subgroups based on sequence similarity between Smc1/4 and Smc2/3 at their most C-terminal sequences [36] . Mutation of Gly1396 was designed to test this notion and the functional importance of the more divergent sequences at the extreme C-terminus of Smc4 ATPase head domain ( S1 Fig ) . Overall , this structural analysis provided a rationale for the creation of a total of 51 different mutations affecting Smc4 catalytic domain . We expected this collection of mutant alleles to cause defects of various severities in condensin ATPase activity , from mild to severe impairment in ATP hydrolysis in vivo . To facilitate the creation and analysis of a large collection of mutant alleles of SMC4 , we used a gene replacement strategy in S . cerevisiae . Specifically , we constructed a series of heterozygous ATPase mutants in diploid cells and uncovered the phenotype associated with these smc4 mutations after sporulation ( Fig 2 ) . Despite affecting residues highly conserved in ABC-type ATPases , most of the haploid mutants we generated were viable and showed normal growth at 23 °C . Only 18 out of the 51 mutants gave rise to tetrads with a 2:2 lethality phenotype cosegregating with smc4 mutations ( Fig 2 ) . As expected , we isolated fully inactivating and/or lethal mutations in all the conserved motifs analyzed , except for the C-helix and R-loop ( see Fig 3 for viable mutants ) . Much to our surprise , motifs proposed to be directly involved in ATP hydrolysis , such as the A- , R- , and Q-loops , could accommodate severe changes without strong effects . This tolerance was exemplified by Gln302 in the Q-loop , a position that tolerated mutation to negatively charged amino acids without detectable growth phenotype ( i . e . , smc4-Q302E/D; Fig 3 ) . Likewise , introducing uncharged amino acids ( Ala or Met ) at the position of the critical arginine in the R-loop did not give rise to noticeable phenotypes . Only the charge-reversal substitution of Arg to Asp at position 210 in the R-loop resulted in a mild growth phenotype ( Fig 3 ) . This tolerance to mutation was in contrast to many other positions whose mutation gave rise to more consequential effects of viability . For instance , the position encoding Gly1396 at the extreme C-terminus of Smc4 was very sensitive to changes in amino acid ( see lethality of smc4-G1396P/Q in Fig 2 ) . Interestingly , this conserved position is unique to Smc4 and bacterial homologs of SMC proteins , and our result revealed the critical importance of this position for cell viability . Together , these results substantially expanded the known repertoire of structural motifs important for Smc4 ATPase activity and viability in budding yeast . Next , we conducted a thorough analysis of the phenotype of viable mutants in our collection . First , we characterized the proliferative potential of viable Smc4 ATPase alleles using a 5-fold dilution series assay and monitored the ability of yeast to grow under progressively more challenging/suboptimal temperatures . From this collection of ATPase mutants , we identified 9 strains that showed thermosensitive growth behavior consistent with varying degrees of Smc4 inactivation ( Fig 3 ) . When compared to a known conditional allele , smc4-22 [37] , 3 ATPase mutants appeared to be acutely defective in Smc4 catalytic activity , as they showed severe proliferation defects even at optimal growth temperature ( smc4-L1335G; smc4-L1333A-L1335A and smc4-Q1377A-F1378A ) . Several other mutants showed defects only at high temperature ( 37 °C ) , including strong ( smc4-L1333I-L1335A; smc4-L1333V-L1335A and smc4-A1376S-Q1377A ) , intermediate ( smc4-L1335A and smc4-Q1377A ) , and mild defects ( most mutations affecting Smc4 R210 residue; Fig 3 ) . We noticed that some mutants exhibited stronger proliferation defects after spore germination ( Fig 2 ) when compared to normal vegetative growth conditions ( e . g . , smc4-Q302E; Fig 3 ) . This likely reflects the fact that germination and normal vegetative growth are physiologically different states in yeast , and germination has unique genetic requirements relative to normal growth [38] . Among the viable mutants , we were unable to identify clear hypomorphic alleles affecting the Pro-loop and D-loop , despite the fact that these motifs are known to be important for ABC transporter and SMC protein functions [16 , 39] . Interestingly , all the thermosensitive mutations that showed intermediate or strong phenotypes are located in the C-terminal globular domain , in proximity to Leu1335 or Gln1377 , key residues in the H-loop and C-helix motifs . A number of mutants in our ATPase collection also showed cold-sensitive growth at 18 °C , including smc4-Q302E , smc4-A1376S-Q1377A , and smc4-N1386D , but these phenotypes were relatively mild . Surprisingly , we found alleles that were cold-resistant , including all those containing mutations affecting the Pro-loop , as well as others scattered in various ABC motifs ( Fig 3 ) . Taken together , this structure-guided collection of mutations in the H-loop and C-helix motifs of Smc4 created many conditional alleles consistent with mutational hotspot regions in the protein . We then used this allelic series to explore the role of Smc4 ATPase activity in vivo . What is the contribution of Smc4 ATPase activity to chromosome condensation ? Previous studies have shown that full inactivation of condensin ATPase activity is incompatible with cell viability [18] . However , it is not clear whether the lethality associated with these mutations is a consequence of chromosome condensation defects or defects affecting other essential processes carried out by condensin . To address this issue , we took advantage of the fact that thermosensitive alleles of condensin can be inactivated specifically in mitosis , allowing one to delineate the effect of Smc4 ATPase mutations on chromosome condensation without interference from other processes . To achieve this , we monitored the ability of conditional ATPase mutants ( smc4-R210D; smc4-L1335A; smc4-L1333I-L1335A; smc4-L1333V-L1335A; smc4-Q1377A; and smc4-A1376S-Q1377A ) to condense the ribosomal DNA ( rDNA ) locus in a nocodazole-induced early-mitotic arrest using fluorescence in situ hybridization ( FISH ) . The phenotype of these cells was compared to that of wild-type SMC4 and cells carrying smc4-7A and smc4-22 , moderate and strong mutants of condensin , respectively [37] . The analysis of rDNA morphology by FISH is a sensitive approach to monitor chromosome condensation in S . cerevisiae , since the conformation of this locus changes dramatically during the cell cycle [40] . While wild-type SMC4 cells synchronized in metaphase could condense DNA efficiently , as shown by the typical condensed “loop” configuration of the rDNA locus at 37 °C , smc4-22 and smc4-7A presented uncondensed “puff” rDNA signal at restrictive temperature , as expected . With respect to the ATPase mutants , we observed condensation defects in all mutants except for the smc4-R210D allele ( Fig 4A and 4B ) . The condensation-defective alleles can be classified into mild ( smc4-L1335A and smc4-Q1377A ) and moderate mutants ( smc4-L1333I-L1335A; smc4-L1333V-L1335A and smc4-A1376S-Q1377A ) according to the fraction of cells that exhibited uncondensed “puff” rDNA morphology ( i . e . , less or more than a threshold of 50% uncondensed rDNA; Fig 4A and 4B and S1 Table ) . However , none of the Smc4 ATPase alleles were as defective in rDNA loop formation as smc4-22 ( i . e . , 75 . 7% “puff” phenotype ) , which was expected because their proliferation defect was weaker than smc4-22 at nonpermissive temperature ( Fig 3 ) . These results , together with the cell proliferation assay , highlight the existence of a clear correlation between the severity of the thermosensitive phenotype and the condensation defects in smc4 ATPase mutants . To investigate the biological relevance and contribution of the ATPase activity of condensin to genome stability , we conducted experiments to assess chromosome segregation fidelity in smc4 mutants . We took advantage of a nonessential chromosome that carries a dominant suppressor ( SUP11 ) as an assay to monitor chromosome loss during cell division . Specifically , cells that efficiently segregate this chromosome during mitosis maintain a white colony color due to the suppression of the ade2-1 allele in the strain background , whereas cells that lose the chromosome generate red pigments/sectors in yeast colonies [41] ( see inset in Fig 4C ) . As expected , many of the ATPase-deficient strains of smc4 showed significant increases in red sector formation/chromosome loss during cell proliferation ( Fig 4D ) . In particular , mutants that showed the strongest defects in chromosome condensation—such as smc4-L1333I-L1335A , smc4-L1333V-L1335A , and smc4-A1376S-Q1377A—were also the most defective in the fidelity of chromosome segregation during mitosis . These results demonstrated that the ultimate biological consequence associated with the impairment of condensin ATPase activity is an inability to effectively segregate chromosomes during cell division . Since condensin complex formation is essential for viability [42] , we wished to investigate whether the defects we observed in cells expressing ATPase mutants of Smc4 were caused by a loss of ATPase activity per se or were a reflection of indirect effects on protein stability and/or complex formation in vivo . Purification of the ATPase mutants of Smc4 showed that they assemble with all other subunits of the enzyme into condensin holocomplexes ( see S4 Fig below ) . In contrast , immunoblot analysis revealed that many of the smc4 ATPase alleles exhibited reduced protein levels compared to wild-type Smc4 when cells were shifted from 23 °C to 37 °C ( Fig 5A ) . This result prompted us to assess whether Smc4 levels might underpin the chromosome condensation defect of condensin ATPase mutants . To address this issue , we down-regulated wild-type Smc4 abundance using an auxin inducible degron ( AID ) allele ( i . e . , Smc4-AID ) and monitored chromosome condensation in those cells . When Smc4 levels were reduced to the same extent as that of the most severe ATPase mutants we created , we did not detect any defect in chromosome condensation ( i . e . , no increase in the rDNA “puff” phenotype; Fig 5B ) . Taken together , these results indicate that the condensation defect observed in ATPase mutants is not the sole consequence of reduced Smc4 protein levels at restrictive temperature and instead reflects an additional loss of function in ATPase activity . These results are consistent with the previous observation that cells express an excess of Smc4 protein under normal conditions and that reduction of Smc4 levels to under 10% wild-type levels does not affect cell proliferation [37] . Finally , we investigated if the localization of ATPase mutants of condensin was altered in cells . Analysis of Smc4 signals on chromatin spreads revealed that all but one ATPase mutant of Smc4 were properly localized on yeast chromosomes ( Fig 5C ) . Specifically , only the Smc4-L1335A mutant appeared to be present at lower levels on chromatin spread relative to wild-type Smc4 . Taken together , these results indicated that the phenotype of ATPase-defective mutants of Smc4 was not a consequence of condensin mislocalization in vivo . Among the collection of Smc4 ATPase mutants we isolated , a subgroup showed very severe growth defects , even at permissive temperature , and were completely inviable at 37 °C ( i . e . , smc4-L1335G , smc4-L1333A-L1335A , and smc4-Q1377A-F1378A; Fig 3 ) . Analysis of rDNA morphology in these three ATPase mutants revealed an rDNA FISH signal that was significantly less intense than that of wild-type cells under the same conditions ( Fig 6A ) . The probe we used to detect the budding yeast rDNA locus recognizes the 100–200 repeats of a 9 . 1 kb sequence ( RDN1 ) carried on chromosome XII [44] . Since FISH signal at this locus directly correlates with rDNA copy number [45] , one explanation for the weaker rDNA signal we detected in ATPase mutants might be that they have lost some of their rDNA repeats . Previous work has shown that condensin interacts directly with the rDNA locus [46] and that its activity is necessary to maintain copy number stability in the rDNA array [45] . In light of this , we examined whether severe mutations in Smc4 ATPase domain led to rDNA array contraction at permissive temperature . We used quantitative PCR to assess the number of rDNA repeats in mutant strains relative to wild-type cells . As shown in Fig 6B , the three smc4 mutants with the lowest FISH signal exhibited a reduction of more than 70% in rDNA copy number when compared to wild-type cells . Consistent with this estimation , the ATPase mutants also contained more RDN1 copies than a control strain that was engineered to carry approximately 25 copies of the rDNA repeat [47] . We confirmed that a strain carrying approximately 25 copies of RDN1 has normal kinetics of cell cycle progression , as previously published [47] , and showed that the proliferation capacity of our three ATPase mutants is significantly impaired even at permissive temperature ( Fig 6C ) . These results indicate that strains expressing ATPase-defective Smc4 experience additional defects in chromosome morphology and/or stability . These defects may act separately or in a synthetic manner with the rDNA array contraction to cause slow growth at a permissive temperature . The conditional lethal mutants identified above are viable at permissive temperature , suggesting that the encoded proteins are not completely defective in condensin ATPase activity . It is also conceivable that some enzymatic activity is retained at nonpermissive temperature or that the mutant proteins act as dominant negatives , thus hiding the true phenotype associated with complete loss of ATPase function in condensin complexes . To reveal the full phenotype associated with abrogation of condensin enzymatic activity , we expressed an “ATPase-dead” allele of SMC4 , smc4-K191M , in the background of a strong temperature-sensitive mutant of the same gene ( smc4-22; inviable at approximately 30–32 °C; Fig 3 ) . An analogous mutation in other SMC proteins is known to fully abrogate ATP hydrolysis in vitro [19 , 39] . The smc4-22 mutant was allowed to establish chromosome condensation at permissive temperature ( 23 °C ) and was subsequently inactivated by shifting to 30 °C ( Fig 7A ) . This experimental regimen impaired rDNA condensation after the temperature shift in the smc4-22 mutant but not in cells expressing wild-type SMC4 ( Fig 7B ) . In contrast , expression of the ATPase-dead smc4-K191M allele in this context did not alleviate the condensation defect and inviability of the smc4-22 mutant at 30 °C ( Fig 7B and 7C ) . We did notice a very slight suppression of the cell growth defect of smc4-22 mutants when expressing smc4-K191M ( Fig 7C ) , perhaps reflecting a degree of interallelic complementation between smc4 alleles , as previously reported for mutations affecting the cohesin complex [48] . Together , our experiments demonstrate that condensin ATPase activity is essential for effective chromosome condensation and maintenance of cell viability . We next wished to determine whether the phenotype of the ATPase mutants that we created could be attributed to impaired ATPase activity in condensin . To achieve this , we purified condensin complexes containing wild-type Smc4 , the 5 ATPase mutants showing significant condensation defects and , as a negative control , a previously described ATPase-dead mutant , Smc4-K191M [19 , 39] . We used a rapid overexpression approach to purify these complexes from yeast in order to bypass low viability issues associated with the expression of ATPase-defective alleles of SMC4 [49] . We then assayed the ATPase activity of these complexes using a luminescence assay that monitors production of ADP upon ATP hydrolysis . In this assay , wild-type condensin hydrolyzed 1 . 69 mol of ATP per mol of enzyme per minute , which is very similar to the value previously reported for the holocomplex using radioactive ATP [50] . On the other hand , the ATPase-dead control ( Smc4-K191M ) was severely reduced for ATPase activity ( 0 . 24 mol ATP hydrolyzed per mol of condensin per minute ) . When we compared ATPase motif mutants of condensin with controls , all exhibited far less ATP hydrolysis than the wild-type complex , and the more severe mutants exhibited enzymatic activity similar to that of condensin complexes containing Smc4-K191M ( Fig 8A ) . Interestingly , while a general correlation was observed between the extent of ATPase dysfunction and the severity of the condensation defects in H-loop mutants , no such correlation was observed with the C-helix mutants . Specifically , complexes containing Smc4-L1335A and Smc4-A1376S-Q1377A mutants showed ATPase activity comparable to that of the ATPase-dead control while maintaining cell viability ( Figs 3 and 8A ) . One possible explanation for this result is that different mutations in the C-helix of Smc4 acted as separation-of-function alleles or , alternatively , that the C-helix mutants had a minimal threshold of ATPase activity required to maintain cell viability ( whereas Smc4-K191M did not meet this threshold ) . It is also possible that the Smc4-K191M ATPase-dead allele gained a novel dominant activity that impaired cell viability independently of its catalytic defect per se . To explore the nature of this conundrum , we used a UV cross-linking approach [19] to monitor the ATP-binding capacity of condensin complexes containing normal ( Smc4 ) , moderate ( Smc4-Q1377A ) , and low ( Smc4-L1335A ) ATPase activity . This approach revealed that Smc2 and Smc4 bound equally well to radiolabeled ATP in wild-type condensin , as expected ( S3 Fig; long exposure ) . In contrast , complexes containing Smc4-L1335A and Smc4-Q1377A showed a marked reduction in Smc4 nucleotide binding compared to Smc2 , consistent with their reduced ATPase activity . Surprisingly , Smc2 showed a large—and somewhat paradoxical—increase in ATP retention in condensin complexes containing the ATPase-dead mutant Smc4-K191M ( S3 Fig; short exposure ) . This result suggested that the lethality of smc4-K191M allele could be due to condensin complexes being dominantly and/or constitutively locked in a deleterious conformation that traps ATP in Smc2 . This interpretation would be in line with the fact that yeast carrying the smc4-L1335A allele were viable ( Fig 3 ) , whereas those carrying smc4-K191M were not ( Fig 2 ) , despite both mutants showing comparable ATP hydrolysis defects ( Fig 8A ) . This result raised the possibility that the ATPase activity of condensin may impact or otherwise regulate other biochemical activities of the complex . One activity that is likely to be regulated by ATP hydrolysis is the ability of condensin to bind DNA and chromosomes . To test this possibility , we conducted fluorescence anisotropy experiments to monitor the affinity of condensin complexes for short DNA substrates . First , with respect to the control complexes , we observed that both the wild-type condensin and the ATPase-dead control ( Smc4-K191M ) were able to bind 60-nt single-stranded DNA ( ssDNA ) ( Fig 8B ) . However , the affinity of the ATPase-dead control for short ssDNA was significantly higher ( Kd ≈ 76 nM ) than that of the wild-type complex ( Kd ≈ 251 nM ) ( Fig 8B and S4 Fig ) . Likewise , when we analyzed the DNA-binding capacity of condensin complexes containing our ATPase mutants , we observed that all mutant proteins were able to bind DNA effectively . Moreover , all ATPase mutants showed an affinity for DNA that was slightly higher ( Smc4-L1335A , Kd ≈ 193 nM; Smc4-L1333V-L1335A , Kd ≈ 147 nM; Smc4-Q1377A , Kd ≈ 215 nM; Smc4-A1376S-Q1377A , Kd ≈ 171 nM ) or much higher ( Smc4-L1333I-L1335A , Kd ≈ 92 nM ) than that shown by wild-type condensin ( Fig 8B and S4 Fig ) . Since all Smc4 mutants tested could bind to DNA , we next asked whether the binding was reversible . To monitor this property , we conducted competition experiments in which condensin complexes prebound to labeled ssDNA were incubated in the presence of an excess of nonlabeled ssDNA . Under this condition , all condensin complexes were able to release prebound-labeled ssDNA ( S4B Fig ) . Interestingly , condensin complexes containing the Smc4-K191M exchanged ssDNA at lower concentration than the wild-type complex , consistent with the affinity displayed in binding assays . Taken together , our biochemical analyses indicated that the mutations we introduced in the H-loop and C-helix of Smc4 decreased the rate of condensin ATP hydrolysis without compromising the ability to bind DNA tightly . Importantly , the effects we observed were not due to major changes in subunit stoichiometry in the mutant complexes ( S4C Fig ) .
SMC complexes have two defining structural characteristics , a ring-like architecture and the presence of a bipartite ATPase domain of the ABC family . In this study , we show that mutations in most of the ABC-type ATPase motifs of Smc4—with the notable exception of the R-loop and C-helix—can severely compromise budding yeast viability , thereby demonstrating their relevance to the mechanism of ATP hydrolysis by condensin . Interestingly , our results also show that in spite of the highly conserved nature of the ABC-type motifs in Smc4 ATPase domain , the importance of specific conserved residues for nucleotide catalysis does not appear to be equivalent in all cases . For instance , the D-loop and its characteristic Asp1358 residue represent a good example of conservation in both structure and function . This position has been shown to form the ATP hydrolysis site in ABC transporters and also in cohesin SMC subunits [16 , 52] . We observed that mutation of this residue in Smc4 had dramatic consequence for yeast viability . On the other hand , the conserved Arg210 in the R-loop is an example of structural but not functional conservation . This position has been implicated in the stimulation of ATP hydrolysis by DNA in PfSmc and Rad50 complexes [23 , 32] . However , introducing multiple different residues at this position in yeast Smc4 did not significantly decrease cell viability . Since the ATPase activity of condensin is also enhanced in the presence of DNA , we envision that the stimulatory effect of DNA is mediated by a different region/motif in the protein . An alternative explanation for the lack of effect of specific mutations could be that Smc4 is structurally resilient and adaptable , thereby buffering protein activity from otherwise deleterious mutations . It is also possible that evolutionary specialization in the function and mode of action of SMC proteins leads to different specific roles for the ABC-type motifs in their cognate complexes . For instance , while PfSmc R-loop is critical for ATPase activation in the presence of DNA ( 14-fold stimulation ) [23] , the stimulation of yeast condensin ATPase by DNA is more modest ( 4-fold stimulation ) [5 , 50 , 53] . The fact that some ATPase motifs appear to be very resilient to mutations ( e . g . , the previously mentioned R-loop or the Pro-loop ) , whereas others are highly sensitive to mutagenesis ( e . g . , the Walker B or the C-motif ) , may reflect the catalytic mechanism of the enzyme . For instance , the capacity of some ATPase motifs to tolerate mutations may indicate that they are not intimately involved in catalysis per se , but they may instead fulfill ancillary or supporting roles in catalysis ( i . e . , analogous to the C- and R-spines in kinase domains; [54] ) . An unexpected group of yeast mutants identified in our allelic series are those that confer cold-resistant growth properties . This type of allele is rarely reported , partly because cellular proliferation at cold temperatures is not frequently tested , and the molecular determinants that lead to this type of phenotype are still poorly understood . It is conceivable that cold-resistant mutations give rise to faster growth kinetics by stimulating enzyme catalysis or stabilizing protein–protein interactions via hydrophobic interfaces , two processes that are negatively impacted by lower temperatures . The molecular basis of cold-resistant growth in condensin mutants remains to be determined . The main goal of our structure-guided genetic analysis was to investigate for the first time the relative importance and contribution of ABC-type ATPase motifs to the mode-of-action and cellular functions of condensin . Importantly , our study identified 5 new residues in Smc4 ATPase head domain that play important roles in the establishment of chromosome morphology . These residues are located in 2 motifs of the SMC ATPase domain that have been poorly studied and show a remarkable resilience to mutation , namely the C-helix ( Leu1333 and Leu1335 ) and H-loop ( Ala1376 , Gln1377 and Phe1378 ) . With respect to the C-helix , we observed a correlation between the length of the hydrophobic lateral chain of Leu1333/1335 and the severity of the DNA condensation defects . When we reduced the length of the side chain at these positions , we observed an increase in severity of the phenotype . Such correlation could reflect a reduction or even loss of intramolecular interactions involving these positions , for example , by perturbing the position of the C-motif within the ATPase domain [52] ( S5A Fig ) . Indeed , when we tested this notion with more dramatic changes that replaced both Leu1333 and Leu1335 for Ala , the resulting mutant was extremely sick and contracted its rDNA array . Likewise , mutations in the H-loop—in particular , at the positions encoding Gln1377 and its adjacent residues—also had major consequences for mitotic chromosome organization . Again , we observed a positive correlation between thermosensitive phenotypes associated with these alleles and the degree to which they affected chromosome condensation . In this case , the phenotypes are more severe when the typical polarity of this region is removed ( S5B Fig ) . In both C-helix and H-loop , alanine mutations resulted in a contraction of the rDNA array and extremely sick mutants . Importantly , in vitro characterization of the purified condensin complexes carrying these mutations confirmed that their condensation defects are associated with decreased ATPase activity . Interestingly , while the correlation between the compaction and ATPase defects is maintained in H-loop mutants , this is not the case with C-helix mutants , since strains carrying the Smc4-L1335A mutation are less thermosensitive than stains bearing the double L1333I-L1335A mutations , and yet the latter shows higher ATPase activity than the former . This loss of correlation likely indicates that the final phenotype of C-helix mutants may be the result of changes in other properties of condensin , for example , DNA binding . In particular , when Leu1335 residue is mutated , introduction of isoleucine at 1333 seem to act as a “second-site suppressor” in biochemical terms but not in genetic terms . This reverse relationship is consistent with the proposed catalytic mechanism of SMC proteins . Indeed , recent studies have shown that the ATPase activity of SMC proteins is a multistep process that involves structural changes upon binding of ATP to the SMC ATPase heads , followed by further changes in conformation after ATP hydrolysis and ADP release from the enzyme [9] . It is therefore likely that the L1333I “second-site suppressor” mutation relieved some of the structural constraints imposed by the L1335A mutation and improved ATPase activity by moving forward the enzymatic reaction past its initial stage ( as observed in in vitro experiments in Fig 8A ) . However , the resulting enzyme might not be able to complete its catalytic cycle and may be trapped in a nonproductive conformation that would not allow effective compaction of chromatin in cells ( thus explaining the more severe in vivo phenotype observed in Fig 3 ) . This rationale predicts that the Smc4-L1333I-L1335A mutant has gained a novel biochemical property relative to the L1335A mutant and wild-type protein . Consistent with this view , we observed that mutation of both Leu1333 and Leu1335 significantly increased the affinity of the complex for ssDNA relative to the wild-type enzyme . A similar effect was also observed in the ATPase-dead mutant Smc4-K191M , suggesting that some mutations in the ATPase head domain might lock the enzyme in a specific configuration that promotes tight binding to DNA . Persistent binding to DNA might interfere significantly with condensin function in vivo and explain why the phenotype of Smc4-K191M and Smc4-L1333I-L1335A mutants is more severe than predicted solely by their defects in ATPase activity . It is interesting to note that Smc2 binds more avidly to ATP in condensin complexes containing the Smc4-K191M mutant , consistent with condensin adopting a specific configuration incompatible with viability in cells expressing this mutant . This interpretation is further supported by the observation that the C-motif of ABC-type ATPases contributes to ATP hydrolysis by different ATPase head domains in trans [55] and that loss of ATP hydrolysis in Smc4 may stabilize ATP molecules on Smc2 and result ultimately in the stabilization of a specific configuration of the ATPase head domains of Smc2 and Smc4 in condensin . The ATP-binding result we obtained with condensin complexes containing the Smc4-K191M mutant may appear surprising at first glance because a similar mutation in B . subtilis SMC resulted in complete abrogation of ATP binding [19] . However , bacterial SMC complexes are homodimeric , and mutations that prevent ATP binding on one SMC subunit will have similar effects on the other subunit . This will not be the case for SMC subunits of eukaryotic condensins , because they are heterodimeric , and it is conceivable , if not likely , that ATPase mutations in one SMC subunit could stabilize an ATP molecule on the other SMC member of the complex . Further structural work will be necessary to address this possibility and the nature of its functional consequences on condensin activity . Our study reveals that cells contain an excess of condensin activity relative to the levels required for normal chromosome condensation during mitosis . In fact , protein down-regulation experiments show that Smc4 protein levels—and by extension , total condensin activity in cells—can be artificially reduced by more than 90% , and neither rDNA condensation nor viability is affected if the ATPase activity of the complex remains unperturbed [37] . This result is consistent with previous observations that cohesin activity can be reduced significantly without impairing its sister-chromatid cohesion activity in vivo [56] . Likewise , a recent study taking advantage of a rapid-degradation allele of SMC2 has shown that metazoan condensins can be depleted to approximately 5% wild-type levels without major disruption of chromatin compaction in vivo [57] . It thus appears the notion that condensin ATPase activity is in excess in relationship to many of its cellular functions holds true over large evolutionary distances in eukaryotes ( at least under optimal growth conditions ) . However , when condensin ATPase activity is fully compromised , chromosome condensation becomes completely defective in mitosis , and cells die , thus revealing different minimal thresholds of ATP activity required to execute different cellular functions . Comparative analysis of ATP hydrolysis rate in ATP-dead mutants ( smc4-K191M ) and other condensin ATPase mutants provides crucial insight into the contribution of ATP hydrolysis to the process of chromosome condensation . Indeed , mutation of the key lysine in the P-loop/Walker A motif is known to impair ATP binding [19] , which explains the inviability of cells expressing this mutant . However , 2 of the mutants generated in this study ( smc4-L1335A and smc4-A1377S-Q1378A ) show an ATP hydrolysis rate similar to the ATPase-dead allele and yet are capable of sustaining cell viability . This suggests that the smc4-K191M mutant leads to a dominant ( or semidominant ) condensation defect because this specific mutation traps the enzyme in a deleterious configuration during the process of ATP hydrolysis . This hypothesis is consistent with the lethality we observed for transition state mutants like smc4-E1352D/Q , which is known to prevent disengagement of the head–head interactions [58] . These observations do not mean , however , that all constitutive lethal mutations that we identified in SMC4 act in a dominant manner , since it appears likely that many mutants will be inviable as a result of a recessive defect in chromosome condensation and/or segregation . Beyond these mechanistic considerations , our observation that condensin ATP hydrolysis rate can be reduced significantly while maintaining viability suggests a model in which cells require minimal condensin ATPase activity to compact chromosomes under normal circumstances , independently of the total number of condensin molecules . In closing , we note that the results of our study may have a number of implications for cancer treatment . Interrogation of the Catalogue of Somatic Mutations in Cancer ( COSMIC ) database [59] revealed that partial deletions of the ATPase domain of human Smc4 have been observed in a number of cancer patients ( S6A and S6B Fig ) . We have introduced 1 cancer-specific deletion mutation at the homologous position of SMC4 ( i . e . , Arg1384* ) in diploid yeast and showed that this leads to lethality after sporulation ( S6B Fig ) . A more modest deletion of the C-terminus of yeast Smc4 ( i . e . , Lys1410*; similar to a truncation made in Geobacillus stearothermophilus SMC [60] ) did lead to a viable but sick yeast strain , suggesting that a partial loss of sequence at the C-terminal ATPase domain is possible and functionally impairs condensin activity . The COSMIC database also revealed that human SMC4 is frequently overexpressed in cancers ( Fig 6C and 6D ) . This , together with our observation that normal cells express an excess of condensin activity ( relative to their normal need for chromosome condensation ) , suggests that targeting condensin with inhibitors of its ATPase activity might be an effective therapeutic approach to kill cancer cells without harming normal cells . Moreover , many cancer cells suffer from severe aneuploidy [61] , a condition that we predict would impose an additional burden on the chromosome condensation machinery . In light of this , we propose that moderate inhibition of condensin ATPase activity might selectively kill aneuploid cells without affecting normal cells , thus providing a unique therapeutic window to target cancer cells . Consistent with this notion , it has been recently shown that Smc4 protein levels correlate with an aggressive phenotype in tumor cells , establishing a link between condensin levels and tumor progression in humans [62 , 63] . Assessment of condensin inhibition in a therapeutic setting will require the identification of inhibitors of the ATPase activity of this essential enzyme .
All yeast strains are derivatives of W303 ( K699 ) [64] , and their genotypes are summarized in S3 Table . Yeast culture conditions , sporulation , and dissection were performed by standard procedures . Synchronization of cultures in metaphase was performed using nocodazole ( 30 μg/ml for 150 min ) . To induce the degradation of Smc4-3xSTII-AID , cells were treated with indole-3-acetic acid ( IAA ) according to published procedures [65] . Mutations were introduced in Smc4 subunit using QuikChange Multi Mutagenesis kit ( Stratagene ) in the plasmid YCplac111-PSMC4-SMC4-Linker-3xStrepTagII::TADH1::HIS3MX6 . Mutant alleles carry smc4-F164A; smc4-[164–167]A; smc4-K165M; smc4-K165A; smc4-Y167C; smc4-K191M; smc4-R210A; smc4-R210M; smc4-R210K; smc4-R210D; smc4-R210P; smc4-R210A-Q211A; smc4-R210D-Q211E; smc4-Q302E; smc4-Q302K; smc4-Q302L; smc4-Q302D; smc4-S1324R; smc4-S1324N; smc4-K1328A; smc4-L1333A; smc4-L1335A; smc4-L1333A-L1335A; smc4-L1335G; smc4-L1333V-L1335A; smc4-L1333I-L1335A; smc4-P1346G; smc4-P1346T; smc4-P1346L; smc4-P1344G-P1346G; smc4-D1351A; smc4-D1351N; smc4-E1352Q; smc4-E1352D; smc4-D1358H; smc4-D1358N; smc4-D1358E; smc4-I1364N; smc4-V1365M; smc4-V1365F; smc4-Q1377A; smc4-Q1377R; smc4-A1376S-Q1377A; smc4-Q1377A- F1378A; smc4-L1383A; smc4-L1383I; smc4-L1383V; smc4-N1386H; smc4-N1386D; smc4-G1396Q; smc4-G1396P . Mutant alleles of SMC4 were introduced at the endogenous locus by transformation of digested plasmids , thus releasing a fragment containing SMC4 ORF with its selection marker . The backbone of the plasmid was lost during the process . All the mutations were confirmed by sequencing the SMC4 locus . For the overexpression of condensin subunits ( wild-type and smc4 ATPase alleles ) , PGAL1-10 or PGAL7-driven subunits ( PGAL7-SMC4-3xStrepTagII , PGAL10-SMC2 , PGAL1-BRN1-3xHA-12xHIS , PGAL10-YCS4 , and PGAL1-YCG1-3xFLAG-9xHIS ) were subcloned in tandem on 2 multicopy plasmids ( i . e . , 2μ TRP1 leu2-d PGAL-YCS4-YCG1 and 2μ URA3 leu2-d PGAL-SMC4-SMC2-BRN1 ) . Cells were fixed and harvested in 0 . 1M KPO4 buffer pH 6 . 4 containing 3 . 7% formaldehyde for 2 hours at 23 °C . The procedure to analyze the morphology of the rDNA locus is based on published data [40 , 66] . The probe was obtained and digoxigenin-labeled according to published procedures [37] . The digoxigenin-labeled DNA was detected using mouse anti-DIG antibody from Roche and FITC-conjugated goat anti-mouse IgG ( Jackson Immunoresearch ) and Alexa Fluor 488-conjugated rabbit anti-goat IgG antibodies ( Jackson Immunoresearch ) . All 3 antibodies were diluted 1:250 using 10% horse serum prior to use . Nuclei were stained with propidium iodide ( PI; Sigma ) in 5 mg/ml of p-phenylenediamine ( Sigma ) . Visualization of rDNA morphology was performed on DeltaVision microscope using the softWoRx software ( Applied Precision ) . The microscope was equipped with a 100×/NA 1 . 4 Plan APO objective ( Olympus ) and a CoolSnap HQ2 camera ( Photometrics ) . Images were acquired at 1 × 1 binning . Final images represent the maximum intensity projections of images taken at 0 . 2 μm intervals . Whole cell extracts for immunoblot analysis were prepared using TCA/glass bead method [67] . Smc4 was separated by SDS-PAGE containing 8% acrylamide ( BioRad ) . All gels were transferred using the iBlot system ( Invitrogen ) . Membranes were probed with mouse monoclonal anti-StrepTagII ( from Qiagen; at 1:1 , 000 dilution ) and mouse monoclonal 22C5D8 ( from Abcam; 1:10 , 000 dilution ) in 2% milk and 1% BSA . The secondary antibody used was HRP-conjugated anti-mouse antibodies ( 10 , 000 , Amersham/GE Healthcare ) . Protein–antibody conjugates were revealed by chemiluminescence ( Western Lightning Plus-ECL; Perkin-Elmer ) . Proteins were overexpressed in yeast strain D1074 as described before , with few modifications [49] . Condensin complexes were overexpressed using p484 and variants of p490 containing the different SMC4 ATPase alleles . Cells were grown in rich medium with 1% lactic acid and 3% glycerol , and protein overexpression was induced for 4 hours with galactose ( 2% final ) in 4 liters of culture at 30 °C . Cells were resuspended in lysis buffer ( 10% glycerol , 500 mM NaCl , 10 mM Tris-HCl pH 8 . 0 supplemented with 10 μM E64 , 1 mM AESBF , 10 μM pepstatin A ) , and the extract was prepared by grinding cells in liquid nitrogen in a SPEX CentriPrep 6850 Freezer Mill [68] . After sonication ( 3 pulses of 10 seconds at level 3 using a Misonix Sonicator 3000 ) and centrifugation , the extract was incubated with Ni-NTA agarose matrix ( Quiagen ) , washed with 20 mM imidazole , and eluted with 500 mM imidazole . The elution from Ni-NTA is then applied on a Strep-Trap HP column ( GE Healthcare ) . After a wash ( 50 mM Tris , 500 mM NaCl , 2 mM β-mercaptoethanol [β-ME] , 1 mM EDTA . 0 . 7% Triton X-100 , 10% Glycerol ) , condensin was eluted with buffer containing 20 mM desthiobiotin . Eluted fractions were concentrated by ultrafiltration using Vivaspin 20 ( Sartorius ) and then purified by size exclusion on Superose 6 10/300 ( GE Healthcare ) in FP buffer ( 200 mM NaCl , 50 mM Tris-HCl pH 8 . 0 ) . Final fractions containing condensin were concentrated with Ultracell 100K ( Millipore ) . Quantification of condensin subunit abundance was performed using Image J analysis of Coomassie-stained bands on SDS-polyacrylamide gels . ATPase assay was performed as previously published , with minor modifications [69] . Reaction mixture contained 10 nM HEPES pH 7 . 8 , 120 mM NaCl , 12 mM Tris pH 7 . 8 , 2 mM β-ME , 0 . 5 mM ATP , 100 μg/mL , and 1 . 5 μmol of condensin . The mixture was incubated at 30 °C for 1 hour , and the ADP production was measured with the ADP-Glo luminescence kit ( Promega ) with a microplate reader ( BioTek ) . Binding of ATP to yeast condensin was performed using the UV cross-linking procedure as previously described [70] , with few modifications . First , 2 . 5 μmol of each condensin complex was prepared in 12 . 5 μl reaction buffer containing 10 mM HEPES ( pH 8 . 0 ) , 1 mM MgCl2 , 1 mM β-ME , 45 nM [ϒ-P32]ATP ( 3 , 000 Ci/mmol ) . The mixtures were transferred to a parafilm wrap on ice and were exposed to UV radiation ( 254 nm ) for 10 minutes to induce cross-link . After cross-linking , protein samples were separated by SDS-PAGE , and the radioactive signal was detected using a Typhoon FLA-9500 Bio-Image Analyzer . The ATP-bound signal detected was normalized to the protein signal detected in the Coomassie-stained gels . Fluorescence anisotropy experiments were carried out at 10 nM 6-FAM ssDNA and variable concentrations of protein ( 0 . 001–6 μM ) . Anisotropy was measured 20 minutes after the incubation of the binding reaction at 30 °C in a microplate reader ( BioTek ) at room temperature ( λ absorbance 485 nm and λ emission 525 nm ) . Normalized fluorescence anisotropy ( “FA” ) was calculated according to FA=rn-rormax-ro , in which rn is the anisotropy for each protein concentration , rmax is the anisotropy at the highest protein concentration , and r0 is the anisotropy without protein . To calculate the equilibrium association constant ( Kd ) , the normalized fluorescence anisotropy was represented as a function of protein concentration , and a curve was fit with nonlinear regression algorithm using Prism . The DNA–protein binding constant was calculated in 3 experiments performed with the same batch of purified protein . To analyze the DNA binding reversibility , we performed a DNA competition assay . First , 10 nM of 6-FAM DNA was incubated with 500 nM of each condensin complex for 20 minutes at 30 °C . Then , the complexes were incubated with increasing concentrations of unlabeled DNA ( 10 nM–3 μM ssDNA ) for 20 more minutes at 30 °C . Anisotropy was measured in a microplate reader ( BioTek ) at room temperature ( λ absorbance 485 nm and λ emission 525 nm ) . Normalized fluorescence anisotropy ( FA ) for the DNA competition assay was calculated according to FA=rn-rormax-ro , in which rn is the anisotropy for each DNA concentration , rmax is the anisotropy at the lowest ssDNA concentration , and r0 is the anisotropy with the same ration of 6-FAM ssDNA and not labeled DNA . rDNA/RDN1 copy number in yeast strains was determined by qPCR analysis of the amplification signal obtained from genomic DNA isolated from a wild-type strain ( D4107 ) , the 25xRDN1 strain ( D419 ) , and the various condensin mutants generated in this study . The RDN1 amplification signal was standardized relative to SMC4 as a gene with a single copy number using the formula RDN1amount=2 ( ΔCtSMC4 ( strain-WT ) -ΔCtRDN1 ( strain-WT ) ) The chromosome loss assay was performed by monitoring the loss of a chromosome III fragment ( CFIII ) carrying HIS3 and SUP11 , as previously described [41] . In brief , loss of CFIII leads to the formation of a red colony sector after growth on solid medium because of the failure of yeast cells to suppress the ade2-1 mutation . The wild-type yeast strain and condensin mutants were grown in minimal liquid medium without histidine until mid/log phase at 23 °C and then incubated for 3 hours at 37°C . The cells were then diluted and plated in YPD medium without adenine supplementation . Cells were grown 4 days at 23 °C and then kept at 4 °C for 3 extra days to allow red color development . The number of colonies counted for each strain is as follows: SMC4 ( N = 4; 19 , 790 colonies ) , smc4-22 ( N = 5; 7 , 614 ) , smc4-L1335A ( N = 4; 13 , 697 colonies ) , smc4-L1333I-L1335A ( N = 5; 12 , 021 colonies ) , smc4-L1333I-L1335V ( N = 5; 7 , 129 colonies ) , smc4-Q1377A ( N = 4; 15 , 323 colonies ) , and smc4-A1376-Q1377A ( N = 3; 6 , 223 colonies ) . Chromatin spreads were performed as described previously [43 , 71] . In brief , 5 OD595nm equivalent of cells synchronized with nocodazole at 32 °C were washed in 1 ml of solution-1 ( 0 . 1 M KPO4 pH7 . 4 , 0 . 5 mM MgCl2 , 1 . 2 M sorbitol ) and resuspended in solution-1 with 1 M DTT . Cells were spheroplasted by the addition of Zymolase ( 10 mg/ml ) and incubated at 30 °C with rotation . Next , cells were washed with solution-2 ( 0 . 1 M MES pH 6 . 4 , 0 . 5 mM MgCl2 , 1 mM EDTA , 1 M sorbitol ) . Cells were applied to a glass slide , fixed , and lysed by the addition of a fixative solution ( 3 . 4% paraformaldehyde , 3 . 4% sucrose ) and 1% NP-40 . Immediately after , cells were spread using a plastic pipette rolled from one end of the slide to the other and then left to dry overnight . Next , slides were washed with 1× PBS and blocked with PBS + 10% BSA . Finally , slides were incubated with mouse monoclonal anti-Myc 9E10 antibody for 2 hours and Alexa Fluor 488-conjugated goat anti-mouse antibody for 2 hours . Nuclei were counterstained with DAPI ( 4’ , 6-diamidino-2-phenylindole ) . Images were acquired using a Zeiss LSM700 confocal microscope with an oil immersion 63× objective . Fluorescence intensity of Smc4 was measured using ImageJ software . The modeling of Smc4 ATPase head has been carried out using the SWISS-MODEL website [72] and 1W1W [30] as a template . Regions encoding the Smc1 ATPase domain were aligned with the analogous regions of Smc4 using ClustalW algorithm with a gap opening penalty of 10 and a gap extension penalty of 0 . 1 [73] . Only the conserved regions were modeled by homology . All the representations have been performed using UCSF Chimera [74] . All numerical values , sample size , and statistics reported in Figs 4B , 4D , 5B , 5C , 6B , 7B , 8A , 8B , S3 , S4A , S4B , S6C and S6D are described in S1 Data .
|
In eukaryotes , the deletion of a single copy of most genes shows little or no detectable phenotype under standard proliferative conditions . This implies that a large reduction in the level of a gene product can be tolerated by eukaryotic organisms and that a “reserve capacity” is built in the protein machinery that drives most cellular processes . Here , we test if the main effector of chromosome condensation—the condensin complex—operates with a reserve enzymatic capacity in the execution of its multiple functions in vivo . To achieve this , we created an allelic series of mutations that selectively inactivate condensin ATPase activity in a graded manner . We show that many core functions of condensin can be maintained even at low levels of ATPase activity . Our data also reveal the existence of various thresholds of ATPase activity that are necessary and sufficient for the execution of different cellular functions by condensin . Notably , loss of genome stability at repetitive DNA is only observed when condensin ATPase activity is severely impaired . Taken together , our results reveal key insights into the process of ATP hydrolysis by condensin and how the energy it releases promotes genome remodeling and stability during cell division .
|
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"Abstract",
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2018
|
Condensin ATPase motifs contribute differentially to the maintenance of chromosome morphology and genome stability
|
Skin-penetrating parasitic nematodes infect approximately one billion people worldwide and are responsible for some of the most common neglected tropical diseases . The infective larvae of skin-penetrating nematodes are thought to search for hosts using sensory cues , yet their host-seeking behavior is poorly understood . We conducted an in-depth analysis of host seeking in the skin-penetrating human parasite Strongyloides stercoralis , and compared its behavior to that of other parasitic nematodes . We found that Str . stercoralis is highly mobile relative to other parasitic nematodes and uses a cruising strategy for finding hosts . Str . stercoralis shows robust attraction to a diverse array of human skin and sweat odorants , most of which are known mosquito attractants . Olfactory preferences of Str . stercoralis vary across life stages , suggesting a mechanism by which host seeking is limited to infective larvae . A comparison of odor-driven behavior in Str . stercoralis and six other nematode species revealed that parasite olfactory preferences reflect host specificity rather than phylogeny , suggesting an important role for olfaction in host selection . Our results may enable the development of new strategies for combating harmful nematode infections .
Skin-penetrating nematodes such as the threadworm Str . stercoralis and the hookworms Ancylostoma duodenale and Necator americanus ( Figure 1A ) are intestinal parasites that infect approximately 1 billion people worldwide . Infection with skin-penetrating worms can cause chronic gastrointestinal distress as well as stunted growth and long-term cognitive impairment in children . Moreover , Str . stercoralis infection can be fatal for immunocompromised individuals and infants [1] . Str . stercoralis is endemic in tropical and sub-tropical regions throughout the world , including the United States , and is estimated to infect 30–100 million people worldwide [2] . Infection rates in rural and semi-rural areas are often high , particularly among children . For example , a recent study found that 25% of school children in semi-rural Cambodia were infected with Str . stercoralis [3] . A better understanding of how skin-penetrating worms target human hosts could lead to new strategies for preventing infections . Skin-penetrating nematodes are infective only during a particular stage of their life cycle called the infective juvenile ( IJ ) , a developmentally arrested third larval stage analogous to the C . elegans dauer [4] . IJs inhabit the soil and infect by skin penetration , often through the skin between the toes . Inside the host , IJs migrate through the circulatory system to the lungs , are coughed up and swallowed , and develop to adulthood in the intestine [1] . IJs may also reach the intestine using other migratory routes [5] . Adult nematodes reproduce in the intestine , and eggs or young larvae are excreted in feces . In the case of hookworms , young larvae develop into IJs , which find and infect new hosts ( Figure 1B ) . In the case of Strongyloides species , some larvae develop into IJs and others develop into free-living adults . In the human parasite Str . stercoralis and the rat parasite Str . ratti , which are subjects of this study , all progeny of free-living adults develop into IJs ( Figure 1C ) . Some species of Strongyloides , such as the dog and cat parasite Str . planiceps , can undergo a limited number of sequential free-living generations [6] . Thus , Strongyloides can develop through at least one free-living generation outside the host . Str . stercoralis can also cycle through multiple parasitic generations in the same host , resulting in a potentially fatal disseminated infection [1] . Little is known about the process by which skin-penetrating nematodes find hosts [7] . IJs of some skin-penetrating species respond to heat and sodium chloride [8]–[12] , suggesting a role for thermosensation and gustation in host seeking . In addition , Str . stercoralis is attracted to human blood serum and sweat [10] , [12] , while Str . ratti is attracted to mammalian blood serum [13] . It has long been speculated that olfaction may be important for host seeking , since animals emit unique odor blends that could confer species-specificity [7] . However , the only specific odorant that has so far been found to elicit a response from a skin-penetrating nematode is urocanic acid , a component of mammalian skin that attracts Str . stercoralis [14] . Thus , the extent to which skin-penetrating nematodes use olfactory cues to locate hosts is unclear . Here we examined the host-seeking strategies and sensory behaviors of the human parasite Str . stercoralis as well as two other species of skin-penetrating nematodes , the rat parasites Str . ratti and Nippostrongylus brasiliensis ( Figure 1A , D ) . We compared their behaviors to those of five other nematode species with diverse lifestyles and ecological niches: the passively ingested ruminant-parasitic nematode Haemonchus contortus; the entomopathogenic nematodes ( EPNs ) Heterorhabditis bacteriophora , Steinernema glaseri , and Steinernema carpocapsae; and the free-living nematode Caenorhabditis elegans ( Figures 1A , D ) . This across-species analysis was used to fit the behaviors of skin-penetrating nematodes into an ecological framework , and to identify species-specific behavioral differences that reflect differences in phylogeny , host range , or infection route . We found that different species of mammalian-parasitic nematodes employ diverse host-seeking strategies , with the human parasite Str . stercoralis being a cruiser that actively seeks out hosts . We found that Str . stercoralis and the other skin-penetrating nematodes are attracted to skin and sweat odorants , while the passively ingested ruminant parasite Ha . contortus is attracted to the smell of grass . By comparing odor response profiles across species , we found that olfactory preferences reflect host specificity rather than phylogeny , suggesting a critical role for olfaction in the process of host finding and appropriate host selection . Our results provide insight into how skin-penetrating nematodes locate hosts to infect .
To gain insight into the host-seeking strategies used by mammalian-parasitic nematodes , we first examined their movement patterns in the absence of chemosensory stimuli . We compared their movement patterns to those of EPNs , which use well-characterized host-seeking strategies: some are “cruisers” that actively search for hosts , some are “ambushers” that wait for passing hosts , and some use an intermediate strategy [9] , [15] . We first examined motility using an assay in which IJs were allowed to distribute on an agar plate in the absence of chemosensory stimuli for one hour and the location of IJs on the plate was recorded . We found that the motility of skin-penetrating IJs resembled that of EPN cruisers , with the human parasite Str . stercoralis being the most active ( Figure 2A ) . By contrast , the motility of Ha . contortus resembled that of the ambushing EPN Ste . carpocapsae ( Figure 2A ) . Thus , skin-penetrating IJs appear to be more active than passively ingested IJs . To investigate the host-seeking strategies of skin-penetrating nematodes in more detail , we examined unstimulated movement of IJs using automated worm tracking [16] . We found that parasitic IJs vary dramatically in their crawling speeds , with the human parasite Str . stercoralis moving more rapidly than the other species tested ( Figure S1A ) . The mean speeds of the skin-penetrating rat parasites were comparable to that of the most active EPN , Ste . glaseri , while the mean speed of Ha . contortus resembled that of the less active EPNs ( Figure S1A ) . Turn probability also varied among species but did not correlate with speed ( Figure S1B ) . Some but not all species crawled significantly faster following mechanical stimulation , and in fact the maximum speeds attained by Str . stercoralis , Str . ratti , and Ste . glaseri following mechanical stimulation were similar ( Figure S1C–D , Movies S1 and S2 ) . Thus , at least some of the differences in basal crawling speeds among species reflect differences in movement strategy rather than differences in the inherent speeds at which the IJs are capable of crawling . The fact that Str . stercoralis has a higher basal speed than Str . ratti and N . brasiliensis is consistent with the possibility that host-seeking strategy evolved independently in these species to accommodate host behavior and ecology . Str . ratti and N . brasiliensis are parasites of nesting rodents , which are highly focal with circumscribed resting places . Since parasite transmission likely occurs within the confines of the nest , rapid mobility may not provide an adaptive advantage for these parasites . By contrast , Str . stercoralis is a parasite of humans , primates , and dogs , all of which are highly mobile . Rapid mobility may be necessary for Str . stercoralis to accommodate the mobility of its hosts . Heat is emitted by all mammals and is a known sensory cue for some mammalian-parasitic nematodes , including Str . stercoralis [11] . We therefore examined the responses of the mammalian-parasitic IJs to a 37°C heat stimulus . We found that the skin-penetrating nematodes increased their crawling speed in response to thermal stimulation , while the passively ingested nematode Ha . contortus did not ( Figure 2B ) . Skin-penetrating nematodes may increase their speed in response to heat to maximize the likelihood of encountering host skin . A comparison of IJ movement patterns at room temperature versus 37°C revealed that skin-penetrating IJs show dramatically different movement patterns at the different temperatures . The trajectories of individual IJs were relatively straight at room temperature but highly curved at 37°C ( Figure 2C ) . To quantify these differences , we calculated a distance ratio consisting of the total distance travelled divided by the maximum displacement achieved . We found that all three species of skin-penetrating nematodes showed greater distance ratios at 37°C compared to room temperature ( Figure 2D ) . These results suggest that heat may act as a cue that signifies host proximity and stimulates local searching . However , we note that the temperature at the surface of human skin is 32–35°C [17] , and IJ movement within this temperature range remains to be examined . An important component of host-seeking strategy for many parasitic nematodes is nictation , a behavior in which the worm stands on its tail and waves its head to facilitate attachment to passing hosts [9] . We examined the nictation behavior of mammalian-parasitic nematodes by performing nictation assays on an “artificial dirt” substrate consisting of dense agar with near-microscopic pillars [18] , since IJs are not capable of standing on standard agar plates due to the high surface tension on the plates [18] . We found that nictation frequencies varied among species . N . brasiliensis showed a high nictation frequency comparable to that of the ambushing EPN Ste . carpocapsae ( Figure 2E and Movie S3 ) , suggesting that it spends most of its foraging time nictating . By contrast , the Strongyloides species showed much lower rates of nictation ( Figure 2E and Movie S4 ) , suggesting they spend most of their foraging time crawling . Ha . contortus did not nictate on the artificial dirt substrate or any other substrate tested ( see Materials and Methods ) , suggesting it may not be capable of nictating . Taken together , our results suggest that mammalian-parasitic nematodes employ diverse host-seeking strategies . The skin-penetrating Strongyloides species appear to be cruisers that are highly mobile and tend to crawl rather than nictate . By contrast , the passively ingested nematode Ha . contortus appears to be an ambusher that displays little unstimulated movement . N . brasiliensis can exhibit rapid , prolonged movement comparable to that of the cruisers but tends to nictate rather than crawl , suggesting it is also an ambusher . However , we note that foraging strategy is in some cases substrate-dependent , and different strains of a species can exhibit different host-seeking behaviors [19] , [20] . Thus , we cannot exclude the possibility that the host-seeking strategies of these species may vary under conditions not tested here . EPNs have been shown to use a diverse array of insect volatiles and herbivore-induced plant volatiles for host finding [21]–[30] . By contrast , only one odorant has so far been identified as an attractant for Str . stercoralis [14] . We therefore tested the extent to which Str . stercoralis displays directed movement in response to human-emitted volatiles . We examined the responses of Str . stercoralis IJs to a large panel of odorants , most of which are known to be emitted by human skin , sweat , and skin microbiota ( Table S5 ) . Responses were examined using a chemotaxis assay ( Figures S2 and S3 ) [21] , [22] . We found that Str . stercoralis was strongly attracted to a number of these odorants ( Figure 3A ) . Nearly all of the attractants we identified for Str . stercoralis also attract anthropophilic mosquitoes ( Figure 3A ) , suggesting that nematodes and mosquitoes target humans using many of the same olfactory cues . While many of the human-emitted odorants that attracted Str . stercoralis are also emitted by other mammals , 7-octenoic acid is thought to be human-specific [31] and may be used by Str . stercoralis to target humans . Str . stercoralis and disease-causing mosquitoes are co-endemic throughout the world [2] , and our results raise the possibility of designing traps that are effective against both parasites . We also examined responses to carbon dioxide ( CO2 ) , which is emitted by aerobic organisms in exhaled breath and is an attractant for many parasites , including EPNs [9] , [21] , [22] . We found that Str . stercoralis was repelled by CO2 at high concentrations and neutral to CO2 at low concentrations , suggesting that CO2 is not a host attractant ( Figure 3A and Figure S4A ) . These results are consistent with the fact that Str . stercoralis infects by skin penetration , and only low levels of CO2 are emitted from skin [32] . However , some EPNs respond synergistically to mixtures of CO2 and other odorants [33] , and we cannot exclude the possibility that Str . stercoralis is attracted to CO2 in mixtures or under conditions not tested here . The fact that Str . stercoralis responds to human-emitted odorants suggests that olfaction plays an important role in host finding . However , the extent to which Str . stercoralis or any other mammalian-parasitic nematode uses olfactory cues for host selection is not known . To gain insight into whether olfaction contributes to host choice , we compared the olfactory responses of Str . stercoralis to those of six other species: Str . ratti , N . brasiliensis , Ha . contortus , He . bacteriophora , Ste . carpocapsae , and C . elegans . We found that all species responded to a wide array of odorants , indicating that as is the case for EPNs [21] , [22] , even ambushers are capable of robust chemotaxis ( Figure 3B and Figure S4 ) . Moreover , each species exhibited a unique odor response profile , indicating that olfactory responses are species-specific even among closely related species such as Str . stercoralis and Str . ratti ( Figure 3B ) . CO2 response varied greatly among species . Like Str . stercoralis , Str . ratti and N . brasiliensis were repelled by CO2 at high concentrations and neutral to CO2 at low concentrations ( Figure 3B and Figure S4B–C ) . By contrast , Ha . contortus IJs , like EPN IJs and C . elegans dauers [21] , [22] , were attracted to CO2 ( Figure 3B and Figure S4D ) . To confirm that the observed responses to odorants were olfactory rather than gustatory , we examined the responses of Str . stercoralis and Str . ratti to a subset of odorants in a modified chemotaxis assay in which odorants were placed on the plate lid rather than the plate surface . We found that attractive responses were still observed when the odorants were placed on the plate lid , although the response of Str . stercoralis to one odorant was slightly reduced ( Figure S5 ) . Thus , the observed behavioral responses are primarily olfactory , but in some cases may include a gustatory component . The olfactory preferences of the passively ingested mammalian parasite , Ha . contortus , are consistent with its known ecology . Ha . contortus IJs migrate from the feces of their ruminant hosts to grass blades , where they are ingested by grazing ruminants [34] . The fact that 5% CO2 , which approximates the concentration found in exhaled breath [35] , was strongly attractive to Ha . contortus ( Figure S4D ) suggests that Ha . contortus may use exhaled CO2 to migrate toward the mouths of potential hosts . By contrast , Ha . contortus was repelled by many of the skin and sweat odorants tested ( Figure 3B ) , consistent with a lack of attraction to mammalian skin . Of the few attractive odorants we identified for Ha . contortus , two – methyl myristate and myristic acid – are known constituents of cow and goat milk [36]–[38] and may be used by Ha . contortus to migrate toward cows and goats . To test whether Ha . contortus also responds to plant-emitted odorants , we examined responses to freshly cut grass . We found that Ha . contortus is attracted to the smell of grass , while Str . stercoralis and Ste . carpocapsae are not ( Figure 3C ) . These results suggest that Ha . contortus uses CO2 in combination with other ruminant-emitted odorants and grass odorants to position itself for passive ingestion . We then quantitatively compared odor response profiles across species , and found that species with similar hosts responded more similarly to odorants despite their phylogenetic distance ( Figure 3D ) . For example , the distantly related rat parasites Str . ratti and N . brasiliensis responded similarly to odorants , as did the distantly related insect parasites He . bacteriophora and Ste . carpocapsae . The three skin-penetrating species responded more similarly to each other than to the other species tested , while the passively ingested mammalian parasite Ha . contortus responded very differently from all of the other species tested ( Figure 3D ) . These results indicate that olfactory preferences reflect host specificity and infection mode rather than phylogeny , consistent with a key role for olfaction in host selection . Skin-penetrating nematodes exit from hosts in feces as eggs or young larvae and subsequently develop into infective larvae outside the host . Thus , both infective and non-infective life stages are present in the environment ( Figure 1B–C ) . This raises the question of whether host attraction is specific to the infective stage . We compared olfactory responses of free-living larvae , free-living adults , and IJs for both Str . stercoralis and Str . ratti in response to a subset of host odorants . We found that all three life stages were robustly attracted to host odorants , suggesting that host attraction is not downregulated in non-infective life stages ( Figure 4 ) . The free-living life stages of skin-penetrating worms are thought to reside primarily on host fecal matter , where they feed on bacteria present in the feces [39] . We therefore compared the responses of free-living larvae , free-living adults , and IJs to host feces . We found that responses differed dramatically across life stages: free-living larvae and adults were strongly attracted to feces , while IJs were neutral to host feces ( Figure 4 ) . Moreover , while Str . ratti IJs were neutral to both host and non-host feces , Str . stercoralis IJs were neutral to host feces but repelled by non-host feces ( Figure 4 ) . Our results suggest a model in which all life stages are attracted to host skin odor , but strong attraction to host fecal odor by the free-living life stages causes them to remain on feces . Attraction to fecal odor is downregulated at the infective stage , enabling the IJs to migrate away from the feces in search of hosts . Repulsion of Str . stercoralis IJs from non-host feces may serve as an additional mechanism to prevent foraging in close proximity to non-hosts . To gain insight into the individual odorants that confer changes in sensitivity to feces , we examined responses to two components of fecal odor , skatole and indole [40] . We found that the free-living stages of Str . ratti were highly attracted to both skatole and indole , while the IJs were neutral to both odorants ( Figure S6A ) . Thus , altered sensitivity to these odorants may contribute to the developmental change in the response to fecal odor . By contrast , Str . stercoralis IJs were more attracted to skatole than the free-living life stages and all three life stages were relatively unresponsive to indole ( Figure S6B ) , suggesting that other as yet unidentified odorants mediate the sensitivity of Str . stercoralis to fecal odor . Str . stercoralis infection is a worldwide cause of chronic morbidity and mortality . Current drugs used to treat nematode infections are inadequate for nematode control: some are toxic , drug resistance is a growing concern , and reinfection rates are high [41] . Our data suggest that Str . stercoralis IJs are fast-moving cruisers that actively search for hosts using a chemically diverse array of human-emitted odorants . The identification of odorants that attract or repel Str . stercoralis and other parasitic nematodes lays a foundation for the design of targeted traps or repellents , which could have broad implications for nematode control .
Gerbils were used for host passage of Str . stercoralis , and rats were used for host passage of Str . ratti and N . brasiliensis . All protocols and procedures were approved by the UCLA Office of Animal Research Oversight ( Protocol No . 2011-060-03B ) , which adheres to the AAALAC standards for laboratory animal use , and were in strict accordance with the Guide for the Care and Use of Laboratory Animals . Strongyloides stercoralis UPD strain and Strongyloides ratti ED321 strain were provided by Dr . James Lok ( University of Pennsylvania ) . Nippostrongylus brasiliensis was provided by Dr . Edward Platzer ( University of California , Riverside ) . Haemonchus contortus was provided by Dr . Adrian Wolstenholme and Mr . Bob Storey ( University of Georgia ) . Heterorhabditis bacteriophora Oswego strain and Steinernema glaseri VS strain were provided by David Shapiro-Ilan ( USDA ) . Steinernema carpocapsae were from the ALL strain [21] , [22] , [42] . C . elegans dauers were from the wild isolate CB4856 ( “Hawaii” ) . Male Mongolian gerbils for culturing Str . stercoralis were obtained from Charles River Laboratories . Male or female Long-Evans or Sprague Dawley rats for culturing Str . ratti and N . brasiliensis were obtained either from Harlan Laboratories or second-hand from other investigators at UCLA through the UCLA Internal Animal Transfer supply system for surplus animals . Galleria mellonella larvae for culturing EPNs were obtained from American Cricket Ranch ( Lakeside , CA ) . Str . stercoralis was serially passaged in gerbils and maintained on fecal-charcoal plates . Inoculation of gerbils with Str . stercoralis was performed essentially as previously described [43] . Briefly , Str . stercoralis IJs were isolated from fecal-charcoal plates using a Baermann apparatus [43] . Each gerbil was subcutaneously injected with 2000 IJs in 200 µl sterile PBS . Gerbils became patent ( as defined by the presence of nematodes in gerbil feces ) on day 12 post-inoculation and remained patent for approximately 70 days . At 28 and 35 days post-inoculation , each gerbil received 2 mg methylprednisolone ( Depo-Medrol , Pfizer ) subcutaneously to induce an auto-infective cycle . To harvest infested feces , gerbils were housed overnight in cages containing a wire rack on the bottom of the cage . Fecal pellets fell below the rack onto damp cardboard and were collected the following morning . Feces were mixed with dH2O and autoclaved charcoal ( bone char from Ebonex Corp . , Cat # EBO . 58BC . 04 ) in an approximately 1∶1 ratio of charcoal to feces . The fecal-charcoal mixtures were poured into Petri dishes ( 10 cm diameter , 20 mm height ) lined with wet filter paper , and were stored at 23°C until use . Nematodes used for behavioral analysis were isolated from fecal-charcoal plates using a Baermann apparatus [43] or from plate lids . To obtain free-living larvae ( primarily post-parasitic L2s ) for chemotaxis assays , nematodes were collected from fecal-charcoal plates after approximately 18 hrs . To obtain free-living adults for chemotaxis assays , nematodes were collected from fecal-charcoal plates after 48 hrs . To obtain IJs , nematodes were collected from fecal-charcoal plates starting at day 5 post-collection . IJs were used for behavioral assays within 2 weeks of fecal collection . Str . ratti was serially passaged in rats and maintained on fecal-charcoal plates . Inoculation of rats with Str . ratti was performed essentially as previously described [44] . Briefly , Str . ratti IJs were isolated from fecal-charcoal plates using a Baermann apparatus . Each rat was subcutaneously injected with 700 IJs in 300 µl sterile PBS . Rats became patent on day 6 post-inoculation and remained patent for up to 28 days post-inoculation . To harvest infested feces , rats were housed overnight in cages containing a wire rack on the bottom of the cage . Fecal pellets fell below the rack onto damp cardboard and were collected the following morning . Fecal-charcoal plates were prepared as described above for Str . stercoralis and stored at 23°C until use . Nematodes used for behavioral analysis were isolated from fecal-charcoal plates using a Baermann apparatus [43] or from plate lids . Free-living larvae , adults , and IJs were obtained from fecal-charcoal plates as described above for Str . stercoralis . N . brasiliensis was serially passaged in rats and maintained on fecal-charcoal plates . To inoculate rats , N . brasiliensis IJs were isolated from fecal-charcoal plates using a Baermann apparatus . Each rat was subcutaneously injected with 4000 IJs in 300 µl sterile PBS . Rats became patent on day 6 post-inoculation and remained patent for up to 14 days . Infested feces were collected as described above for Str . ratti . Fecal-charcoal plates were prepared as described above for Str . stercoralis , except that vermiculite ( Fisher catalog # S17729 ) was added to the feces and charcoal in an approximately 1∶1∶1 ratio of vermiculite to charcoal to feces . Plates were stored at 23°C until use . In some cases , either Nystatin ( Sigma catalog # N6261 ) at a concentration of 200 U/ml or Fungizone ( Gibco catalog #15290-018 ) at a concentration of 1 µg/ml was added to the filter paper on the bottom of the plate to inhibit mold growth . Nematodes used for behavioral analysis were isolated from fecal-charcoal plates using a Baermann apparatus [43] or from plate lids . To obtain IJs , nematodes were collected from fecal-charcoal plates starting at day 7 post-collection . IJs were used for behavioral assays within 2 weeks of fecal collection . Ha . contortus was stored in dH2O at 8°C prior to use . IJs were tested within 6 months of collection . No differences in IJ movement or behavior were observed in freshly collected versus 6 month old IJs . IJ behavior declined after 6 months , so IJs older than 6 months were not tested . EPNs were cultured as previously described [21] . Briefly , 5 last instar Galleria mellonella larvae were placed in a 5 cm Petri dish with a 55 mm Whatman 1 filter paper acting as a pseudo-soil substrate in the bottom of the dish . Approximately 250 µl containing 500–1000 IJs suspended in water was evenly distributed on the filter paper . After 7–10 days the insect cadavers were placed on White traps [45] . Emerging IJs were collected from the White trap , rinsed 3 times with dH2O , and stored in dH2O until use . Ste . carpocapsae and He . bacteriophora were maintained at 25°C , while Ste . glaseri was maintained at room temperature . IJs were used for behavioral assays within 7 days of collection from the White trap . C . elegans was cultured on NGM plates seeded with E . coli OP50 according to standard methods [46] . Dauer larvae were collected from the lids of plates from which the nematodes had consumed all of the OP50 and stored in dH2O at room temperature prior to use . Dauer larvae were used for behavioral assays within 2 weeks of collection from plate lids . 30–100 IJs were placed in the center of a chemotaxis plate [47] . IJs were allowed to distribute over the agar surface for 1 hr , after which the percentage of IJs in the outer zone ( Zone 2 ) was determined . Zone 1 was a 4 cm diameter circle centered in the middle of the plate . Zone 2 consisted of the rest of the plate and included the edges of the plate , which acted as a trap since IJs that crawled onto the plate edge desiccated and could not return to the agar surface . Recordings of worm movement were obtained with an Olympus E-PM1 digital camera attached to a Leica S6 D microscope . To quantify unstimulated movement , 4–5 IJs were placed in the center of a chemotaxis plate [47] and allowed to acclimate for 10 min . 20 s recordings were then obtained . Worms that either did not move , that stopped moving during the recording , or that crawled off the assay plate during the recording were excluded from the analysis . To quantify movement before and after mechanical stimulation , IJs were placed on chemotaxis plates and allowed to acclimate for 10 min . prior to tracking . Baseline movement was recorded for approximately 15 s . The plate lid was then removed , the IJ was gently agitated using a worm pick , and post-agitation movement was recorded for approximately 30 s . 5 s recording clips directly following agitation were used to calculate the maximum speeds shown in Figure S1D , and 5 s recording clips directly preceding and following agitation were used to generate the sample tracks shown in Figure S1C . Maximum speeds were calculated in WormAnalyzer ( see below ) based on changes in worm position over a seven frame ( or 0 . 23 second ) window . To quantify movement following thermal stimulation , assays were performed in a 37°C warm room . Chemotaxis assay plates were kept in the warm room prior to use . Individual IJs were transported into the warm room , transferred to assay plates , and immediately recorded for 20 s . For the room temperature control , IJs were similarly transferred to assay plates and immediately recorded for 20 s . Locomotion was quantified using WormTracker and WormAnalyzer multi-worm tracker software ( Miriam Goodman lab , Stanford University ) [16] . The following WormTracker settings were adjusted from the default settings ( designed for C . elegans adults ) for analysis of IJ movement: min . single worm area = 20 pixels; max . size change by worm between successive frames = 250 pixels; shortest valid track = 30 frames; auto-thresholding correction factor = 0 . 001 . To calculate turn frequencies , the following WormAnalyzer settings were adjusted from the default settings for analysis of IJ speed: sliding window for smoothing track data = 30 frames; minimum run duration for pirouette identification = 2 . 9 s for Str . stercoralis , 5 . 3 s for Ste . glaseri , and 6 s for all other species ( to compensate for differences in speed among species ) . All turns were confirmed by visual observation of worm tracks; turns not confirmed by visual observation were not counted . For calculations of maximum displacement in Figure 2D , the distance between the worm's start point and the farthest point the worm reached during the 20 s recording was calculated in ImageJ . Nictation was quantified on “micro-dirt” agar chips cast from polydimethylsiloxane ( PDMS ) molds as previously described [18] , except that chips were made from 5% agar dissolved in dH2O and were incubated at 37°C for 2 hr and then room temperature for 1 hr before use . The micro-dirt chip consisted of agar with near-microscopic pillars covering its surface ( pillar height of 25 µm with a radius of 25 µm and an interval between pillars of 25 µm ) , which allowed IJs to nictate on top of the pillars . For each assay , 3–10 IJs were transferred to the micro-dirt chip and allowed to acclimate on the chip for 10 min . Each IJ was then monitored for 2 min . An IJ was scored as “nictating” if it raised its head off the surface of the chip for a period of at least 5 s during the 2 min assay period . Nictation behavior was also tested on sand . Sand nictation assays were performed essentially as previously described [21] , [48] . Sand ( silicon dioxide , >230 mesh , CAS 60676-86-0 ) was distributed onto the surface of a chemotaxis plate using a sieve . IJs were transferred to the plate surface and allowed to acclimate for 10 min . Nictation behavior was then observed for two minutes . In all cases , nictation behavior on sand was consistent with nictation behavior on micro-dirt chips . In the case of Ha . contortus , we also tested for nictation on grass and vermiculite; no nictation was observed on any substrate tested . To test for nictation on grass , grass samples were collected from a lawn seeded with UC Verde Buffalo grass and perennial rye grass ( the same lawn as for sample 1 below ) . The grass was cut into small chunks ( ∼2 . 5 mm×2 . 5 mm ) and distributed onto the surface of a chemotaxis plate . IJs were transferred onto the plate surface or directly onto blades of grass , and nictation was scored after a 10 min . acclimation period . Nictation was also scored after 20 , 30 , or 60 min . , or the next day . No nictation was observed with Ha . contortus at any time point . Odor chemotaxis assays were performed essentially as described [21] , [22] ( Figure S2 ) . Assays were performed on chemotaxis assay plates [47] . Scoring regions consisted of 2 cm diameter circles on each side of the plate along the diameter with the center of the circle 1 cm from the edge of the plate , as well as the rectangular region extending from the edges of the circle to the edge of the plate . Either 2 µl ( for mammalian-parasitic IJs ) or 1 µl ( for insect-parasitic IJs and C . elegans dauers ) of 5% sodium azide was placed in the scoring region as anesthetic . 5 µl of odorant was then placed on the surface of the assay plate in the center of one scoring region , and 5 µl of control ( paraffin oil , dH2O , or ethanol ) was placed on the surface of the assay plate in the center of the other scoring region . Approximately 200 worms were placed in the center of the assay plate and left undisturbed on a vibration-reducing platform for 3 hours at room temperature . A chemotaxis index ( CI ) was then calculated as: CI = ( # worms at odorant−# worms at control ) / ( # worms at odorant+control ) ( Figure S2 ) . A positive CI indicates attraction; a negative CI indicates repulsion . A 3 hour assay duration was used because 3 hour assays were found to be most effective for EPNs [21] , [49] . However , 1 hour assays were also performed with Str . ratti , and no significant differences were observed in 1 hour vs . 3 hour assays ( Table S6 ) . Two identical assays were always performed simultaneously with the odor gradient in opposite directions on the two plates to control for directional bias due to room vibration; assays were discarded if the difference in the CIs for the two plates was ≥0 . 9 or if fewer than 7 worms moved into the scoring regions on one or both of the plates . Liquid odorants were tested undiluted unless otherwise indicated . Solid odorants were prepared as follows: 1-dodecanol , methyl palmitate , and methyl myristate were diluted 0 . 05 g in 2 . 5 ml paraffin oil; palmitic acid was diluted 10 g in 200 ml ethanol; myristic acid , skatole , and indole were diluted 0 . 05 g in 2 . 5 ml ethanol; and L-lactic acid was diluted 0 . 05 g in 2 . 5 ml dH2O . Ammonia was purchased as a 2 M solution in ethanol . Solid odorants were tested at these concentrations unless otherwise indicated . For assays in which odorants were placed on the plate lid rather than the plate surface ( Figure S5 ) , filter paper squares of approximately 0 . 5 cm in width were attached to the plate lid using double-stick tape . Odorant or control was then pipetted onto the filter paper , and chemotaxis was examined as described above . CO2 chemotaxis assays were performed essentially as described [21] , [22] . Assays were performed on chemotaxis assay plates [47] , and scoring regions were as described above for odor chemotaxis assays ( Figure S2 ) . Gases were delivered at a rate of 0 . 5 ml/min through holes in the plate lids from gastight syringes filled with either a CO2 mixture containing the test concentration of CO2 , 10% O2 , and the balance N2 , or a control air mixture containing 10% O2 and 90% N2 . Certified gas mixtures were obtained from Air Liquide or Airgas . Assays were performed and scored as described above for odor chemotaxis assays , except that the assay duration was 1 hour . Fresh grass samples were collected from the campus of the University of California , Los Angeles . Sample 1 was collected from a lawn seeded with UC Verde Buffalo grass and perennial rye grass , and sample 2 was collected from a lawn seeded with a custom blend of annual ryegrass , Festuca , Bonsai dwarf fescue , Bermuda grass , and bluegrass . 200 µl of dH2O was added to 0 . 1 g grass . Grass was then ground in a small weigh boat , and 5 µl of the grass suspension was used in a chemotaxis assay with 5 µl dH2O as a control . Grass was either used immediately for chemotaxis assays or stored at 4°C for no more than 3 days . Uninfected rat or dog feces was collected from animals in the UCLA vivarium . Responses to feces were tested using a modified chemotaxis assay in which feces was placed on the plate lid rather than the plate surface . Filter paper squares of approximately 0 . 5 cm in width were attached to the plate lid using double-stick tape . Fecal matter was moistened with dH2O , smeared onto filter paper , and tested in a chemotaxis assay as described above for odor chemotaxis assays . We note that similar attraction to feces was observed when filter paper with feces was tested against filter paper with dH2O , and no attraction was observed to wet filter paper when wet filter paper was tested against dry filter paper ( data not shown ) . Statistical analysis was performed using either GraphPad Instat , GraphPad Prism , or PAST [50] . The heatmap was generated using Heatmap Builder [51] .
|
Parasitic worms are a significant public health problem . Skin-penetrating worms such as hookworms and the human threadworm Strongyloides stercoralis dwell in the soil before infecting their host . However , how they locate and identify appropriate hosts is not understood . Here we investigated the host-seeking behavior of Str . stercoralis . We found that Str . stercoralis moves quickly and actively searches for hosts to infect . We also found that Str . stercoralis is attracted to human skin and sweat odorants , including many that also attract mosquitoes . We then compared olfactory behavior across parasitic worm species and found that parasites with similar hosts respond similarly to odorants even when they are not closely related , suggesting parasitic worms use olfactory cues to select hosts . A better understanding of host seeking in skin-penetrating worms may lead to novel control strategies .
|
[
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"Methods"
] |
[
"ecology",
"biology",
"and",
"life",
"sciences",
"sensory",
"perception",
"neuroscience",
"parasitology",
"behavioral",
"ecology"
] |
2014
|
Diverse Host-Seeking Behaviors of Skin-Penetrating Nematodes
|
Oak galls are spectacular extended phenotypes of gallwasp genes in host oak tissues and have evolved complex morphologies that serve , in part , to exclude parasitoid natural enemies . Parasitoids and their insect herbivore hosts have coevolved to produce diverse communities comprising about a third of all animal species . The factors structuring these communities , however , remain poorly understood . An emerging theme in community ecology is the need to consider the effects of host traits , shaped by both natural selection and phylogenetic history , on associated communities of natural enemies . Here we examine the impact of host traits and phylogenetic relatedness on 48 ecologically closed and species-rich communities of parasitoids attacking gall-inducing wasps on oaks . Gallwasps induce the development of spectacular and structurally complex galls whose species- and generation-specific morphologies are the extended phenotypes of gallwasp genes . All the associated natural enemies attack their concealed hosts through gall tissues , and several structural gall traits have been shown to enhance defence against parasitoid attack . Here we explore the significance of these and other host traits in predicting variation in parasitoid community structure across gallwasp species . In particular , we test the “Enemy Hypothesis , ” which predicts that galls with similar morphology will exclude similar sets of parasitoids and therefore have similar parasitoid communities . Having controlled for phylogenetic patterning in host traits and communities , we found significant correlations between parasitoid community structure and several gall structural traits ( toughness , hairiness , stickiness ) , supporting the Enemy Hypothesis . Parasitoid community structure was also consistently predicted by components of the hosts' spatiotemporal niche , particularly host oak taxonomy and gall location ( e . g . , leaf versus bud versus seed ) . The combined explanatory power of structural and spatiotemporal traits on community structure can be high , reaching 62% in one analysis . The observed patterns derive mainly from partial niche specialisation of highly generalist parasitoids with broad host ranges ( >20 hosts ) , rather than strict separation of enemies with narrower host ranges , and so may contribute to maintenance of the richness of generalist parasitoids in gallwasp communities . Though evolutionary escape from parasitoids might most effectively be achieved via changes in host oak taxon , extreme conservatism in this trait for gallwasps suggests that selection is more likely to have acted on gall morphology and location . Any escape from parasitoids associated with evolutionary shifts in these traits has probably only been transient , however , due to subsequent recruitment of parasitoid species already attacking other host galls with similar trait combinations .
Identifying the processes that structure communities remains one of the fundamental challenges facing ecology [1]–[5] and greatly influences our ability to predict the effects of species invasions and extinctions [6]–[8] . However , the issues are complex , and recent reviews have emphasised the need for new approaches to understanding the ecology and evolution of communities [9]–[11] . Two important emerging themes are ( i ) the effects of adaptive trait variation at one trophic level upon other levels [1] , [2] , [5] , [12] , [13] and ( ii ) the roles of evolutionary history and phylogenetically conserved traits in determining current community structure [4] , [11] , [14] , [16] . Here we address these issues in a study that focuses on diverse , clearly defined communities of parasitoid wasps attacking insect herbivore hosts . Understanding the processes structuring host–parasitoid communities is important because parasitoid wasps and their insect hosts comprise about one-third of all animal species , and more than 50% of all terrestrial animal species [17] . Parasitoids also play a major role in regulating populations of their insect hosts , and the ecosystem service they provide in reducing losses to herbivores and stored product pests is estimated at billions of dollars annually [1] , [2] , [8] , [15] , [16] . Detailed studies of single insect host species have shown that variation in host traits , including feeding location , feeding mode , and host plant species [1] , [2] , [15] , [16] , influences the mortality imposed by parasitoids . However , much less is known of the effects of host trait evolution on parasitoid community composition [5] , [18] . Addressing this issue requires examination of patterns in parasitoid communities across host species , and because related host species may share both similar traits and parasitoid communities through shared common ancestry [19] , [20] , this in turn requires explicit consideration of host phylogeny . We studied the parasitoid wasp communities associated with a major radiation of herbivorous insects—cynipid gallwasps ( Hymenoptera , Cynipidae ) on oak trees ( Quercus species ) —a host taxon with some 1 , 000 herbivore species , distributed primarily in northern temperate regions [24] . These communities are excellent test subjects because they are diverse and well studied [21]–[29] , and the vast majority of the associated parasitoids attack only oak gallwasps [21] , [22] , [24] , [30] , [31] . The communities are thus ecologically “closed” and may meaningfully be considered in isolation . Oak gallwasps induce the development of spectacular galls ( Figures 1 and S2 ) , which though comprising plant tissues represent the extended phenotypes of gallwasp genes [23] , [32] . Parasitoids inflict high mortality on their gallwasp hosts [24] , [27] , [28] , [33] , and selection should favour adaptive host traits that reduce parasitism [27] , [34] . Since all parasitoid attack involves oviposition through gall tissues , gallwasp genes that induce gall structures that reduce parasitoid attack rates should be favoured by selection—a view encapsulated in the Enemy Hypothesis [1] , [2] , [27] , [33] . This hypothesis is supported by studies of the impact of variation in gall morphology within galler species , and is also compatible with demonstrated convergent evolution of several of the same traits ( Table 1 ) in gallwasps [23]–[26] . These defensive extended phenotypes drive reciprocal phenotype evolution [34] , [35] in parasitoid traits , such as ovipositor length , which may limit access to concealed gallwasp hosts [31] . While previous studies have examined the role of other host defences ( such as warning coloration and cuticular coatings of hair or spines ( e . g . , [5] ) and grooming behaviours [36] ) , we here examine the impact of diversity in extended phenotypes in predicting variation in parasitoid community structure among a closely related group of herbivores . Because the parasitoid communities attacking oak gallwasps comprise both specialists ( those attacking a small subset of available host gall types ) and generalists ( those attacking many host gall types ) [21] , [22] , [24] , [28] , [30] , [31] , we can also ask which of these groups drive any host-associated community structure . This is important because whereas changes in host traits influencing specialist enemies are likely to influence only a small number of species in these foodwebs , those influencing vulnerability to attack by generalists may have both major direct and indirect ( apparent competition [9] ) influences on many species in the web . Further , Askew [22] predicted that the richness of oak gallwasp communities would be maintained by partitioning of generalist parasitoids among different gall phenotypes . We emphasise two key phases in successful parasitoid attack—host detection and host exploitation [1] , [2] , [15] , [16] , [34] . For parasitoids of herbivorous insects , host detection requires searching the right part of the right plant at the right time , while exploitation involves overcoming any host defences and the ability to develop on the host resources available [15] , [16] , [34] . We can , in turn , divide host traits into three major groups ( Table 1 ) , each of which has been invoked repeatedly [1] , [2] , [5] , [15] , [16] , [19] , [21] , [22] , [27] , [30] , [34] as a key determinant of parasitoid community structure: ( i ) Spatiotemporal niche traits describe the distribution of hosts in space ( oak taxon galled , location of the gall on the oak ) and time ( season and duration of development ) , and determine the likelihood of detection by parasitoids . ( ii ) Resource traits represent the quality of the host resource per gall ( host size , number of hosts per gall ) potentially available to parasitoids . ( iii ) Gall morphology traits capture variation in the structure of gall tissues parasitoids must penetrate to access host resources , potentially acting as direct defences against particular natural enemies ( the Enemy Hypothesis [27] , [33] ) . These three groups of traits influence parasitoid success in host detection ( spatiotemporal niche ) and host exploitation ( resource , morphology ) , respectively . Here we compare the parasitoid communities induced by 40 gallwasp species ( Table S1 ) at five replicate sites across Hungary ( Figure S1 ) , a known ancient centre of oak cynipid diversity [37] , [38] . Oak gallwasp lifecycles involve obligate alternation between a spring sexual generation and a summer asexual generation [24] , [39] , each of which induces a gall with a characteristic morphology that develops on a characteristic plant organ ( e . g . , bud , leaf , flower , fruit , root ) of a specific oak taxon [23]–[26] . Exemplar gall phenotypes are shown in Figure 1 , and gall morphologies and character states for all species and generations are shown in Figure S2 and Table S2 . Sexual and asexual gallwasp generations have long been known to support different parasitoid communities [21] , [22] , [28] , [40] , a feature of gallwasp ecology that here we establish quantitatively in ancient refuge communities for the first time . Because the two generations of the gallwasp lifecycle also show independent evolution of morphological traits , gall locations and host oak associations [23] , [25] , [26] , [41] , we examine patterns in associated parasitoid communities in each generation separately . Specifically , we ask whether similar parasitoid communities evolve on hosts with similar gall morphology traits ( as predicted by the Enemy Hypothesis ) , on hosts occupying similar spatiotemporal niches , or on hosts providing similar levels of resource per gall . Further , we ask whether any host-associated community structure is driven by the preferences of generalist natural enemies , as predicted by Askew [22] . A key issue in analyses of patterns across species is the fact that host trait values ( both gall phenotypes and associated parasitoid communities ) cannot be regarded as statistically independent , but are linked by phylogenetic patterns of shared common ancestry [5] , [18] , [19] , [23] , [25] , [36] , [41] , [42] . To assess the strength of any phylogenetic patterning in variables of interest , we generated a molecular phylogeny of the host gallwasp species ( see Materials and Methods ) , and then used matrix correlation analyses ( MCA; see Materials and Methods ) to test the significance of correlations between pairwise genetic distance between species , and pairwise similarity in phenotypic and community traits . We then use two parallel approaches that control for phylogenetic nonindependence to examine patterns within each generation ( see Materials and Methods ) . First , we included host relatedness ( shown visually in Figure 2 ) as a covariate in MCA of host traits and parasitoid communities . Second , we controlled for phylogenetic nonindependence using phylogenetically independent contrasts in phylogenetic regression analysis ( PRA ) [42] . We present results separately for each of our five study sites , and for all five sites pooled ( see Materials and Methods ) . We predict that host traits with a key role in structuring parasitoid communities should have consistent significant effects across these different datasets .
We reared over 40 , 000 cynipid galls , resulting in >31 , 000 parasitoids belonging to 58 species ( Table S3 ) . Each gall type was attacked by between three and 30 parasitoid species , of varying host specificity . For the purposes of illustration ( and not for data analysis ) , we divide parasitoid species among the following categories . There were nine extreme specialist parasitoid species ( recorded from only one host gall type ) , 23 specialists ( two to 11 hosts ) , 16 generalists ( 11–21 hosts ) , and nine extreme generalists ( >20 hosts ) ( Table S3 ) . Our placement of parasitoid species into these categories closely matches previous work on Western Palaearctic oak gall communities [21] , [22] , [24] , [43] . To allow tests of correlations between host phenotypic traits and parasitoid community composition , we calculated the pairwise similarity in parasitoid community composition for all gall type pairs using Bray-Curtis scores . This common measure of similarity takes account of both the presence and the relative abundance of parasitoid species ( see Materials and Methods ) , and for our sampled communities ranged from 0% ( no parasitoid species in common ) to 80% ( great overlap of species ) . A striking feature of our data is the fact that though some parasitoids attack both asexual and sexual generation galls , communities associated with the same generation of different host species are significantly more similar than those associated with different generations of the same host species ( shown visually in Figure 3; ANOSIM of Bray-Curtis scores by generation significant for all sites: p<0 . 001 at Gödöllõ , Mátrafüred , and Sopron; p<0 . 02 at Varpalota; p<0 . 05 at Szentkut ) . This replicates Askew's findings for younger and much less diverse postglacial oak gallwasp communities in the UK [21] , and is consistent with a fundamental role for season of development in determining parasitoid community composition ( see also [16] ) . Parasitoid community composition and all three aspects of host phenotypes ( spatiotemporal niche , host resource availability , gall morphology ) are correlated with host phylogeny ( Figures 2 and 3; Table 2 ) . More closely related gallwasp hosts harboured more similar parasitoid communities in both sexual ( p<0 . 05 , pooled sites ) and asexual generations ( p<0 . 001 , and significant at p<0 . 05 in four of five individual sites ) . Significant correlations were also always positive for spatiotemporal traits ( plant organ galled , oak taxon , and gall persistence ) and host resource availability ( host size , Table 2 ) . In contrast , signs of significant correlations varied among morphological traits; they were positive for toughness and gall size but negative for spininess , stickiness , and presence of an internal airspace . The negative correlations for the latter traits are consistent with previous analyses demonstrating their convergent evolution in gallwasps [23] , [26] . A greater number of significant correlations was found in the asexual generation galls ( Table 2 ) , in part reflecting the greater trait diversity present in this generation ( Figure S2; Table S2 ) . These results underline the need to control for phylogenetic nonindependence in testing correlations between host traits and parasitoid community composition , even in closely related hosts . Summaries of the significant variables retained in MCA and PRA analyses are shown in Tables 3 and 4 , respectively . While MCAs addressed overall similarity in community composition using untransformed Bray Curtis similarities , in PRAs we used multidimensional scaling ( MDS ) to identify three statistically independent axes of community variation ( see Materials and Methods ) . The results of the two analytical approaches are highly congruent and show parasitoid community composition to be influenced by host traits associated with each of gall morphology , spatiotemporal niche , and host resource ( Tables 3 and 4 ) . Explanatory power of these traits in combination can be high: in PRA , these groups of explanatory variables explained up to 62% of the deviance in a given MDS axis ( Table 4 ) . For some site and generation combinations , however , available host species allowed very few independent contrasts , limiting the degrees of freedom available for detection of patterns in the data ( see legend , Table 4 ) . Askew [22] proposed that structuring of rich cynipid-centred communities would be mediated by variation in the ability of generalist parasitoids to exploit different host phenotypes . Our data show that the relative dominance ( see Materials and Methods ) of the five most generalist species attacking each gallwasp generation varies with host traits ( Figure 4 ) . For example , in the sexual generation gall communities , the parasitoid Aulogymnus gallarum ( Eulophidae; 23 recorded host gall types ) was a dominant parasitoid of catkin galls ( 38 . 5% of all parasitoid emergence ) , but was rare ( <6% ) or absent in galls developing in any other location . Similarly , it was a dominant sexual generation parasitoid of hosts on section Quercus oaks ( 40 . 1% ) , but was much rarer in hosts on section Cerris . Reversals in the relative importance of parasitoid species pairs across sexual gall locations can also be seen for Megastigmus dorsalis ( Torymidae ) and Sycophila biguttata ( Eurytomidae ) . A similar and more pronounced pattern is seen in the asexual generation galls: each of the five gall locations represented in our sampling was dominated by a different generalist parasitoid ( Figure 4 ) , and the relative ranks of these parasitoids differed across hosts in the oak sections Cerris and Quercus . Where sampled gall types allow similar dominance analyses to be made in individual sites , the observed patterns are concordant with those across the pooled data . These results are consistent with Askew's hypothesis .
One initially counterintuitive finding of studies comparing parasitoid communities associated with different host feeding niches was that despite being apparently well defended by gall tissues , gall inducers usually support richer communities and suffer higher mortality than externally feeding herbivores ( [48] , [51]–[53]; but see [54] ) . This may be because , as proposed by Stireman and Singer [5] for tachinid fly parasitoid communities , well-defended hosts suffer lower mortality from attack by generalist vertebrate ( and possibly invertebrate ) predators , and so represent enemy-free space [61] for their specialist parasitoids . Whether the same applies to cynipid galls and their associated parasitoids or not , the Enemy Hypothesis predicts that the high host mortality imposed by parasitoids on most insect gall inducers should drive the evolution of gall phenotypes that reduce attack by , or exclude , at least a subset of them [1] , [2] , [27] , [33] , [52] , [62] . Though there is evidence that herbivore extended phenotypes do structure parasitoid assemblages across host species [18] , [63] , no previous studies have demonstrated an impact of the complex gall morphology traits that predict vulnerability to attack within gall-inducer species [27] , [52] , [53] . This led to the hypothesis that observed phenotypic diversity in some galler lineages represents the “ghost of parasitism past” [51] , whose efficacy in influencing parasitoid attack has been nullified by the evolution of effective parasitoid countermeasures . Our results show that in the rich , sympatric communities of cynipid gallwasps on oaks , the gall-associated extended phenotypes of gallwasp genes can structure parasitoid communities , and so provide support for the Enemy Hypothesis . Further , they show that traits expressed in two distinct stages of the gallwasp lifecycle can influence community structure , because while gall morphology is controlled by genes expressed in the gallwasp larva , oak taxon , gall location , and the grouping of hosts within gall structures are all determined by the oviposition behaviour of the adult female [23] , [25] , [41] . Although we have demonstrated that gall morphology influences parasitoid community composition , none of the gall morphologies we sampled were free of parasitoids , and so none represent true enemy-free space [61] . This suggests that any enemy-free space gained by novel gall morphologies is only transient [1] , [2] , [27] , [35] . The ability of all but seven of the parasitoid species in this study to attack multiple gall morphologies implies that parasitoids are able to circumvent some structural gall defences through behavioural or phenological plasticity [35] , and consequently the coevolution of host morphological defences with parasitoid attack mechanisms is probably diffuse [15] , [35] , [60] . For a gallwasp , our results suggest that the best way to escape its current parasitoid community in a given generation is to shift to a new oak taxon , or to a new location on its current oak host . Which of these routes has been exploited in the evolution of gallwasp communities will depend on the relative frequency of each kind of shift during gallwasp diversification . In contrast to other gall inducers [18] , oak gallwasps shift between oak lineages extremely rarely [41] , while changes in gall location and morphology are more frequent [23] , [25] , [26] . However , when evolutionary shifts in gall location occur , they will often be a case of “out of the frying pan and into the fire , ” because of subsequent detection and exploitation of novel hosts by parasitoid species already attacking other host galls resident in the same spatiotemporal niche [1] , [2] . Why then are similar impacts of gall morphology not seen in the parasitoid communities associated with species-rich radiations of hosts inducing structurally complex galls on other plants ( such as Asphondyllia gall midges on creosote bush , Larrea tridentata; [52] ) ? One possibility is that gall phenotypes in these radiations do represent the “ghost of parasitism past . ” An alternative is that relationships between gall morphology and associated communities may only become apparent when phylogenetic patterns are controlled for . Changes in parasitoid assemblages during evolutionary diversification of a host lineage represent the sum of phylogenetic correlation between the assemblages attacking related hosts , and the impacts of variation in any host traits influencing parasitoid attack . If phylogenetic correlations are strong ( as they are in a range of host–parasitoid systems [5] , [18] , [56] ) , significant impacts of gall morphology traits may only be revealed when host phylogeny is controlled for , as we have done here . Though patterns of evolution have been examined in Asphondyllia gall traits [64] , to our knowledge phylogenetically controlled analyses of associated parasitoid assemblages have yet to be made . In addition to their parasitoid natural enemies , some cynipid gallwasps are also attacked by opportunist vertebrate natural enemies , including insectivorous birds [43] . Studies on other gall-inducer systems [34] have shown that birds can impose directional selection on gall traits , and although available evidence shows that parasitoids inflict the vast majority of natural enemy-imposed mortality in cynipid galls [43] , it is possible that the traits we discuss here could also influence bird predation in this system . Species rich communities of insect herbivores often harbour multiple generalist parasitoids [15] , [16] , [44] , [50] , [65] , raising the question of how host species richness is maintained in the face of apparent competition [9] , [66]–[69] . Our results show that within each gallwasp generation , significant impacts of host traits on parasitoid community structure primarily involve generalist parasitoids ( Figure 4 ) . This argues against the existence of clearly defined tritrophic niches in gallwasp communities , in which hosts in specific niches are attacked by specific sets of natural enemies [1] , [2] . However , the fact that generalist parasitoids vary in the mortality they inflict in different spatiotemporal niches makes them less generalist than their host ranges would suggest . Though we have not explicitly examined it here , one possible consequence of this is a weakening of indirect interactions ( such as apparent competition ) between hosts mediated by shared enemies [25] , [65] , [68] , with potential contributions to food web stability [67] , [69] , [70] . Interaction networks can also be stabilised by switching of parasitoids between alternative hosts [66] , [67] . If such host switching occurs in oak gall parasitoids , our results suggest that it will be primarily among hosts in the same gall generation and probably on the same oak taxon . Further studies across guilds of natural enemies that exploit potentially coevolving hosts are needed to assess the generality of the patterns of community structure found here . Influential generalist natural enemies attacking spatiotemporally linked metacommunities of hosts may be a feature of many natural communities , as oak gall communities show many similarities with those centred on other concealed insect herbivores ( such as other gall inducers , leaf miners , or stem borers ) [15] , [16] , [22] , [45]–[47] , [65] , [68] , including many pests and potential targets for biological control . Our work underlines the need to incorporate host phylogeny into analyses of community structure , and doing so may help to predict both the natural enemies awaiting invading hosts , and the nontarget hosts of possible biocontrol agents [65] .
Full names of all host gallwasp species are listed in Table S1 and gall traits are defined in Table 1 . Gall morphology traits were recorded for mature galls and are listed in Table S2 , and shown in Figure S2 . Gall season refers to the date of the first recorded onset of development of a gall type ( see Table 1 ) . Gall persistence was measured in weeks from the onset of development until the gall inducer emerged , or the gall fell from the tree , or the end of parasitoid attack in a given year—assumed here to be the end of October on the basis of parasitoid emergence dates from our rearings . Host abundance per gall was estimated using the mean number of parasitoids emerging from a single gall of each phenotype . Both sample size and the mean number of parasitoids per gall were ln ( value +1 ) transformed prior to analysis . Galls were collected between 2000 and 2003 at five field sites in Hungary ( Figure S1 ) and reared individually in outside insectaries . At each sample site , galls were collected from >100 individual trees comprising all oak species present , separated by an average of 20–50 m , over an area of approximately 0 . 25 km2 ( 500 m×500 m ) . Each site was searched systematically and thoroughly at fortnightly intervals between April and October , and where natural host distributions allowed , each gall type was harvested as far as possible across the full site area . Galls were harvested haphazardly with respect to height and aspect , to a maximum of 8 m above ground level with a long handled pruner . Galls were always harvested in their first year of development , typically across a number of dates but also before emergence of gall inhabitants and after adequate gall growth to allow inhabitants to develop to adulthood . The emerging wasps were identified to species level and all host and parasitoid species are listed in Tables S1 and S3 . The target sample size per gall type per site was 150 , based on our previous work on cynipid communities [24] , [28] , [29] . Because of unavoidable variation in what was actually reared ( Table S4 ) , sample size was fitted as a covariate in all analyses . Pairwise similarities between parasitoid communities for use in both MCA and Analyses Of SIMilarity ( ANOSIM; [71] ) were calculated as Bray-Curtis similarities in PRIMER 5 ( Primer-E Ltd ) from standardised untransformed parasitoid abundances . We did this for individual sites and for the pooled sites dataset ( i ) for pooled gallwasp generations to allow us to test differences between sexual and asexual generation communities ( ANOSIM ) , and ( ii ) for sexual and asexual generations separately to allow analyses of patterns within each generation ( MCA ) . The ANOSIM analyses were carried out as a one-way design using generation as explanatory factor . The number of possible permutations was capped at 999 . To allow analysis of community composition using phylogenetically independent contrasts , the Bray-Curtis matrix was decomposed into three mutually independent variables using MDS [72] . Three MDS dimensions were used for each dataset , which reduced STRESS [72] ( a measure of goodness of fit ) to below an acceptable threshold [72] of 0 . 15 in every case . Each MDS dimension was tested as a separate response variable . The parasitoid community response variables and other descriptors ( MDS axes , species richness , sampling effort ) are listed in Table S4 . The oaks attacked by gallwasp hosts were identified to oak section , either section Cerris ( Q . cerris ) or section Q . sensu stricto ( Q . petraea , Q . pubescens , Q . robur ) . We did not attempt to separate species within the section Quercus because extensive hybridisation makes definitive allocation of individuals to species using either morphological or molecular markers impossible [73]–[78] . In this we match observed patterns in oak gallwasp host specificity [25] , [39] , [41] and follow previous analyses of insect biodiversity on Western Palaearctic oaks [79] , [80] . The gallwasp phylogeny was estimated from partial sequences ( 433 base pairs , accession numbers in Table S1 ) of the mitochondrial cytochrome b locus . Generation of the sequence data and selection of appropriate models of sequence evolution for this locus are discussed in detail elsewhere [39] , [41] . Our phylogenetic hypothesis ( working phylogeny sensu Grafen [42] ) was generated using a Bayesian approach in MrBayes 3 . 0 [81] using the general time-reversible ( GTR ) model of sequence evolution . Trees were sampled over 106 generations with an empirically determined burn-in period of 105 generations before tree sampling began . Convergence of parameter estimates over each run was confirmed using Tracer [82] . As in previous phylogenetic analyses of Western Palaearctic oak gallwasps [23] , [25] , [41] , we used the rose gallwasp Diplolepis rosae as the outgroup . The topology of the phylogeny used here matches very closely the results of more extensive published analyses that use a combination of mitochondrial and nuclear markers [25] , [41] . GenBank ( http://www . ncbi . nlm . nih . gov/Genbank ) accession numbers for all sequences used in our analysis are listed in Table S1 . We used PRA [42] for multiple regression of phylogenetically independent contrasts in GLIM 4 . 0 . This approach uses a user-defined working phylogeny to structure a generalised linear modelling analysis in which significance of each explanatory variable was tested in turn while controlling for all others . The approach assumes a normal distribution during model fitting , and where necessary , variables were transformed to meet this assumption ( see “Host Species and Their Gall Traits” above ) . To minimise the impact of phylogenetic uncertainty on the regression procedure [42] , we took the conservative approach of collapsing nodes with a posterior probability of <70% into polytomies . Grafen's default “figure 2 method” [42] was used to determine the initial distribution of branch lengths ( node height = ( i − 1 ) / ( n − 1 ) , where n = number of species and i = number of species below that node in the phylogeny ) . Minimal adequate models ( MAM ) were determined by stepwise removal of all nonsignificant ( p>0 . 05 ) variables . Where the multilevel factors “plant organ galled” and “gall toughness” were retained in models , the categories were split into a series of binary variables . These were then tested in all possible combinations , controlling for other significant variables , to reveal significant categories . In analyses for separate generations and sites , significance levels were adjusted for multiple tests using the Bonferroni correction ( corrected threshold p = 1 − ( 1 − alpha ) 1/k where k is the number of tests and alpha is the desired threshold value of 5% ) . The numbers of species and independent contrasts in each analysis are given in Table 4 . In these analyses , host relatedness was incorporated as a covariate and estimated as ( 1 – the GTR model proportional sequence divergence between host species pairs ) for the cytochrome b data , calculated using PAUP* [83] . Pairwise divergences between species for this gene closely parallel those in a nuclear gene ( long wavelength opsin; [41] ) , suggesting that this is an appropriate measure of phylogenetic relatedness . MCA was carried out with simple or partial Mantel permutation tests in FSTAT [84] , using 2 , 000 permutations and following Manly [85] . As in the PRA , nonsignificant variables were removed from each full model to leave the MAM . Analyses were carried out for pooled and single sites , for separate generations . Similarities in gall traits were calculated using the Manhattan method for continuous variables and the Jaccard index for binary variables [86] . We used a SIMPER analysis in PRIMER5 to reveal which species of parasitoid accounted for the majority of pairwise Bray-Curtis similarity in associated parasitoid communities of host gall types . Most of the variation could be attributed to five species: A . gallarum ( Eulophidae ) , Cecidostiba fungosa ( Pteromalidae ) , Eurytoma brunniventris ( Eurytomidae ) , M . dorsalis ( Torymidae ) , and S . biguttata ( Eurytomidae ) . All five species are extreme generalists and were recorded from more than 20 host gall types in this study ( Table S3 ) . To visualise variation in the impact of these species across galls with different traits , we ( i ) calculated the dominance of each species in each host gall type as the proportion of individuals of that species of the total of emerging parasitoids , and then ( ii ) averaged the dominance values for each parasitoid species across host gall types sharing each selected trait of interest .
|
Herbivorous insects , such as the wasps that induce trees to make galls , and the parasitoids that attack ( and ultimately kill ) the wasps comprise about a third of all animal species , but it remains unclear what determines the structure of these complex coevolving communities . Here , we analyzed 48 parasitoid communities attacking different cynipid wasps that live and feed on oak trees . These communities are diverse and “closed , ” with each centered upon the characteristic gall induced by a given cynipid wasp species . The often spectacular and complex galls are extended phenotypes of gallwasp genes and have been suggested to evolve as gallwasp defenses against their parasitoid enemies—“the Enemy Hypothesis . ” Our analysis showed that similar parasitoid communities occurred on galls with similar structural traits ( e . g . , toughness , hairiness , stickiness ) , supporting the Enemy Hypothesis . We also found similar communities on galls that co-occur frequently in time and space; in particular , those occurring on the same oak species and same plant organ ( e . g . , leaf , bud , seed ) . Our results suggest that cynipid wasps might escape particular parasitoids via evolutionary shifts in the structure or location of their galls . However , escape may often be transient due to recruitment of new enemies already attacking other host galls with similar trait combinations .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
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"ecology/evolutionary",
"ecology",
"ecology/behavioral",
"ecology",
"evolutionary",
"biology/evolutionary",
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"ecology/community",
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2009
|
Host Niches and Defensive Extended Phenotypes Structure Parasitoid Wasp Communities
|
Enteric fever , a systemic infection caused by the bacteria Salmonella Typhi and Salmonella Paratyphi A , is endemic in Kathmandu , Nepal . Previous work identified proximity to poor quality water sources as a community-level risk for infection . Here , we sought to examine individual-level risk factors related to hygiene and sanitation to improve our understanding of the epidemiology of enteric fever in this setting . A matched case-control analysis was performed through enrollment of 103 blood culture positive enteric fever patients and 294 afebrile community-based age and gender-matched controls . A detailed questionnaire was administered to both cases and controls and the association between enteric fever infection and potential exposures were examined through conditional logistic regression . Several behavioral practices were identified as protective against infection with enteric fever , including water storage and hygienic habits . Additionally , we found that exposures related to poor water and socioeconomic status are more influential in the risk of infection with S . Typhi , whereas food consumption habits and migration play more of a role in risk of S . Paratyphi A infection . Our work suggests that S . Typhi and S . Paratyphi A follow different routes of infection in this highly endemic setting and that sustained exposure to both serovars probably leads to the development of passive immunity . In the absence of a polyvalent vaccine against S . Typhi and S . Paratyphi A , we advocate better systems for water treatment and storage , improvements in the quality of street food , and vaccination with currently available S . Typhi vaccines .
The human systemic disease enteric fever is most commonly caused by the Salmonella enterica serovars Typhi ( S . Typhi ) and Paratyphi A ( S . Paratyphi A ) [1] , [2] . The disease is found in areas with poor sanitation and hygiene [3] , and has an estimated global burden of 27 million new cases and 200 , 000 deaths annually [4] . The causative bacteria are transmitted fecal-orally . After ingestion , a 7 to 14 day symptomatic period ensues whereupon bacteremia presents as a persistent non-focal fever with malaise . The bacteria can induce a protracted illness that lasts several weeks , and while rarely fatal , the disease can result in life threatening complications including hypotensive shock and intestinal perforation [2] , [5] . Enteric fever is endemic in Nepal and S . Typhi and S . Paratyphi A are the most commonly isolated organisms from the blood of febrile patients in our Kathmandu-based healthcare setting [6] , [7] . A retrospective analysis highlighted a substantial burden of enteric fever within the local population , particularly in school-age children and males aged 15 to 25 years [8] . Furthermore , we have shown that indirect transmission through contaminated drinking water may play a more important role in maintaining the endemicity of infection in Kathmandu than close contact with symptomatic or asymptomatic individuals [9] , [10] . However , there are several gaps in our knowledge of the transmission of enteric fever and specific behavioral risk factors for infection in Kathmandu have not been identified . Several case-control studies have investigated risks for enteric fever; the majority implicate water and food as important transmission routes [11]–[21] . Additional risk factors include previous contact with an enteric fever case [12] , [19] , [22] , recent antimicrobial treatment [15] , local flooding [19] , poor hygiene [13] , [14] and poor socioeconomic status [13] , [19] . The identification of tractable risk factors and probable routes of transmission for the agents of enteric fever are necessary for the development of targeted interventions to reduce disease burden . In this study , a case-control investigation and age-stratified serology were performed to identify risk factors for , and measure expose to , enteric fever in Kathmandu .
This study was approved by the institutional ethical review boards of Patan Hospital and The Nepal Health Research Council . All enrollees were required to provide written informed consent for the collection and storage of all samples and subsequent data analysis . In the case of those under 18 years of age , a parent or guardian was asked to provide written informed consent . Patan Hospital is a 318-bed government hospital providing emergency and elective outpatient and inpatient services located in Lalitpur Sub-metropolitan City ( LSMC ) within the Kathmandu Valley ( Figure 1 ) . Enteric fever is common at the outpatient clinic at Patan Hospital , which has approximately 200 , 000 outpatient visits annually . The population of LSMC is generally poor , with most living in overcrowded conditions and obtaining their water from stone spouts or sunken wells ( Figure 1 ) . All febrile patients attending the outpatient or emergency department between April and October 2011 with a non-focal fever lasting 3 or more days , aged between 2–65 years and providing informed consent were eligible for this study . All individuals received a blood culture and only those with a blood culture positive for S . Typhi or S . Paratyphi A were enrolled . Community-based controls were matched for age , sex and residential ward and enrolled at a ratio of 3∶1 . Controls were identified in households neighboring cases . If the case lived in a stand-alone house , the household to the right of the “case-household” was approached by a community medical assistant ( CMA ) within 2 weeks of the case enrollment . If this control refused , the household to the left was approached , followed by the house parallel across the street . If the case lived in multi-story building , the household above the “case-household” was approached . If this control refused , a household a storey either below or two stories below was approached . Controls were required to be within 5 years of the age of the case and must not have had fever , gastrointestinal disturbances or history of enteric fever in the month before administration of the questionnaire . If an approached control failed to meet the enrollment criteria or refused participation , the house to the left or the storey below the “case-household” was approached for enrollment . The trained CMAs administered a 129-question questionnaire to each enrolled individual . Anti-coagulated blood samples were collected from all febrile patients upon arrival in the outpatient department . For those over the age of 12 years , 10 ml of blood sample was collected; 5 ml was collected from those aged 12 years or less . The blood samples were inoculated into tryptone soya broth and sodium polyethanol sulphonate up to 50 ml . The inoculated media was incubated at 37°C and examined daily for bacterial growth over seven days . On observation of turbidity , the media was sub-cultured onto MacConkey agar . Any bacterial growth presumptive of S . Typhi or S . Paratyphi was identified using serotype specific antisera ( Murex Biotech , Dartford , UK ) . ELISAs to measure IgG against the Vi and O:2 antigens ( NVGH , Siena , Italy ) [23] , [24] were performed on 795 plasma samples that were age-stratified and randomly selected from a serum bank comprised from the blood of patients attending Emergency Department of the Patan Hospital for reasons other than typhoid treatment or those relating to a febrile illness between January 2009–December 2011 . Ages of enrollees ranged from 0–65 and were resident in the same demographic area as the case/control enrollees . All plasma samples were subjected to ELISA detecting IgG antibodies to both Vi and O:2 . Briefly , plasma was diluted into 1∶200 and aliquoted into ELISA plates ( Nunc , Sigma-Aldrich Co , UK ) independently coated with Vi and O:2 antigens . After washing , bound IgG was detected using an alkaline phosphatase–conjugate antiserum ( Sigma-Aldrich Co , UK ) . Antibody levels were quantified using standard curves . The standard curve for Vi-antibody was created using a prepared anti-human Vi-antibody standard . The standard curve for O:2-antibody was created using a pool of plasma from S . Paratyphi A confirmed patients who had high levels of antibody to O:2 , which had been screened prior to the serum bank . The cutoff value of the ELISAs was defined as the optical density of blank control wells plus two standard deviations . Data were imported into STATA v9 . 2 ( College Station , TX , USA ) . The association between the outcome of enteric fever ( defined as infection with either S . Typhi or S . Paratyphi A ) and each exposure of interest was examined using a matched univariate analysis through conditional logistic regression . A conceptual model was generated to develop a biologically plausible set of covariates a-priori that were thought likely to influence the outcome , including those related to poor socioeconomic status and poor water quality . Variables were included in a matched multivariate analysis through a-priori selection or were associated ( p<0 . 25 ) with enteric fever in the univariate analysis [25] . Model fit was assessed through log likelihood and relative AIC value . Co-linearity among variables was assessed but no strong associations were found between the variables included in the final model . The presence of effect modification was evaluated between each of the salient exposures and confounders of interest through the χ2 test for homogeneity . Several sets of interactions were found to be significant . Each interaction term was included in the final model of interest and model fit was assessed . However , due to small numbers of patients in many of the strata , inclusions of these interactions led to an unstable estimate and were thus discarded . One interaction , however , household size affecting the risk of water storage on outcome of enteric fever , improved model fit and led to stable estimates so was included . The categorical variable household size was generated through assessing whether the house was the same size or smaller than the median number of people ( 12 ) in this dataset or larger than the median . All P values are two-sided .
During the period of investigation 103 febrile patients with culture confirmed enteric fever were enrolled , 48% ( 49/103 ) were positive for S . Typhi and 52% ( 54/103 ) were positive for S . Paratyphi A . Baseline characteristics of the enteric fever cases are described in Table 1 . Briefly , those with culture confirmed enteric fever were more often male ( 64%; 66/103 ) and had a median age of 18 years ( interquartile range ( IQR ) 10–23 years ) . Males and females with S . Paratyphi A did not differ significantly in age ( median: 18 years , IQR: 5–55 and 20 years , IQR: 6–28 , respectively ) , but female S . Typhi cases were significantly older ( median: 21 . 5 years , IQR: 8–50 ) than male S . Typhi cases ( median: 16 years , IQR: 7–32 ) ( P = 0 . 03 , Mann-Whitney U test ) . A total of 294 controls were enrolled , leading to a final case-control ratio of 1∶2 . 85 . The clinical presentations of S . Typhi and S . Paratyphi A were largely indistinguishable , with most cases exhibiting a progressive fever ( 77%; 79/103 ) , nausea ( 50%; 51/103 ) , and a limited number having an abdominal rash ( 2%; 2/103 ) or constipation ( 6%; 6/103 ) . Patients with S . Typhi were more likely to present with abdominal pain ( 61%; 30/49 ) than those with S . Paratyphi A ( 33%; 18/54 ) ( P = 0 . 005 , χ2 test ) and to have diarrhea ( 25%; 12/49 and 9%; 5/54 , respectively ( P = 0 . 038 , χ2 test ) ) . To identify important associations between various exposures and the outcome of enteric fever due to either S . Typhi or S . Paratyphi A , we performed a series of univariate analyses and , to control for confounding , built a multivariate model . As shown in Table 2 , a variety of exposures were found to be protective against enteric fever in a univariate analysis . These protective variables included , an awareness of enteric fever , reporting a previous enteric fever episode , recent contact with an enteric fever case , using a metal cover on a household water storage container , and the consumption of pani puri . The counterintuitive protective effects of recent contact with an enteric fever case and consumption of pani puri were considered to be a result of study design . As cases and controls were geographically matched , it is likely that controls were aware of local cases . Additionally , as pani puri has the potential to be fecally contaminated [26] , the protective association is likely to be explained by uncollected information . These exposures were not included in the final model . Enteric fever risks from the univariate analysis included a household monthly income of <$125 , the duration of residence in Kathmandu , the use of stone spout water , storing water in the household , the use of a household latrine , and the recent consumption of street food . From the multivariate model , an awareness of enteric fever ( AOR: 0 . 25 , 95%CI: 0 . 1–0 . 5 , p<0 . 001 ) and the use of a metal cover on household water storage ( AOR: 0 . 28 , 95%CI: 0 . 1–0 . 7 , p = 0 . 006 ) remained significantly protective against infection . The use of a household latrine , compared to a community latrine ( AOR: 4 . 10 , 95%CI: 1 . 4–12 . 4 , p = 0 . 013 ) , as well as recent street food consumption ( AOR: 2 . 85 , 95%CI: 1 . 4–6 . 0 , p = 0 . 006 ) remained as strong risk factors for infection . Notably , the risk of storing water was found to vary by household size . Those living in households with more than 12 people were at significant risk of infection if they stored water ( AOR: 9 . 35 , 95%CI: 1 . 7–51 . 0 , p = 0 . 010 ) , while those who lived in smaller houses were unaffected by water storage habits . From the series of univariate analyses performed on S . Paratyphi A cases and matched controls only , many of the identified protective and risk factors , as shown in Table 3 , were similar to the overall enteric fever analysis shown in Table 2 . However , the final multivariate model demonstrated that the use of a metal cover for water storage ( AOR: 0 . 19 , 95%CI: 0 . 1–0 . 7 , p = 0 . 014 ) remained strongly protective against infection with S . Paratyphi A , whereas the consumption of street food within the two weeks preceding illness remained a significant risk factor ( AOR: 2 . 95; 95%CI: 1 . 1–7 . 8 , p = 0 . 028 ) . Furthermore , residing in Kathmandu for less than two years ( AOR: 2 . 29 , 95%CI: 0 . 9–5 . 7 , p = 0 . 077 ) remained a weakly significant risk factor for S . Paratyphi A infection only . Many of the important protective and risk factors for S . Typhi infection only were again similar to the overall enteric fever analysis , however some S . Typhi-specific exposures emerged . Firstly , the use of stone spout water and household water storage were strong risk factors for S . Typhi infection in the univariate analysis , although the use of stone spout water was only mildly significant in the multivariate model ( AOR: 4 . 17 , 95%CI: 0 . 9–20 . 5 , p = 0 . 078 ) . Water storage was a risk but was not significantly associated with S . Typhi infection in the multivariate model ( AOR: 2 . 56 , 95%CI: 0 . 8–8 . 6 , p = 0 . 129 ) . Living in a house with more than 12 people was also a risk that did not remain strongly significant in the multivariate model ( AOR: 2 . 11 , 95%CI: 0 . 8–5 . 5 , p = 0 . 127 ) , although reporting a monthly income of less than $125 was strongly and independently associated with S . Typhi infection ( AOR: 5 . 21 , 95%CI: 1 . 5–18 . 4 , p = 0 . 010 ) . Awareness of enteric fever was strongly protective , specifically against infection with S . Typhi ( AOR: 0 . 28 , 95%CI: 0 . 1–0 . 8 , p = 0 . 014 ) . To assess exposure to S . Typhi and S . Paratyphi A in the local population , we measured IgG against Vi antigen ( S . Typhi ) and O:2 antigen ( S . Paratyphi A ) in 795 age-stratified ( 0–65 years ) serum samples derived from the same population as our case/control enrollees ( Figure 2 ) . The resulting data demonstrated a consistently high level of IgG against Vi and O:2 in all age groups . IgG against S . Typhi was highest at birth and then declined with a secondary peak at the age of 17–18 years . Antibody against S . Paratyphi A was lowest at birth and peaked at the age of 11–12 years and subsequently declined; yet persisted into old age . There was a weak correlation between levels of IgG to Vi and to O:2 ( Spearman's ρ: 0 . 30 , P<0 . 001 ) , this was most apparent within the group aged 11–20 years ( Spearman's ρ: 0 . 50 , P<0 . 001 ) ( Figure 2 ) .
The aim of this study was to elucidate risk factors for , and protective behavior against , S . Typhi and S . Paratyphi A infection in Kathmandu . These analyses were performed in order to more clearly define the epidemiology of enteric fever for developing appropriately targeted control measures . We identified several risk factors and protective variables that could be targets for future intervention studies; in addition , our data show some differences in epidemiology between the two enteric fever serovars and highlight continued exposure and enteric fever risk as a function of daily life in this location . Compared to S . Paratyphi A , enteric fever due to S . Typhi is historically thought to be more common , have a more severe clinical course , and result in more frequent and severe sequelae [27] . However , recent studies have suggested that infections caused by S . Paratyphi A are now more prevalent in areas endemic for enteric fever , and that infections caused by S . Typhi and S . Paratyphi A are clinically indistinguishable [8] , [19] , [28]–[33] . Our data in the present study support this trend . Whether this increase in S . Paratyphi A is a consequence of the decline of enteric fever due to S . Typhi or due to an absolute increase in the incidence of S . Paratyphi A is still not clear [19] , [34] , [35] . The relative increase in S . Paratyphi A has important implications for public health efforts to control the burden of disease . Particularly , the oral Ty21a and the parenteral Vi typhoid vaccines offer limited or no protection , respectively , against S . Paratyphi A [29] , [36] . From our serological data , we suggest that vaccination with the currently available vaccines in many age groups may not dramatically impact the rates of disease in this population due to sustained exposure and high levels of pre-existing antibody . The dawn of Vi and O:2 conjugate vaccines may substantially reduce the burden of disease , but selecting the pivotal target population is paramount for the success of these vaccines . When examining all enteric fever cases and matched controls , several factors that were protective against infection emerged . Notable protective variable included , an awareness of enteric fever , and the use of a metal cover on household water storage containers . The strong protective effect of a metal covering for water storage ( as opposed to plastic ) is likely explained by uncollected information associated with water treatment practices . We understand that households that filter their water prior to drinking are likely to have more permanent water storage containers with a metal covering; therefore it is likely that use of a ceramic filter explains the protective association of a metal covering on water storage units . We identified the use of a household latrine , apposed to a community latrine , as a significant risk for enteric fever in this analysis . Proximity of a household latrine to the kitchen or sleeping area of a family may explain this risk factor . In times of water shortage people are not be as likely to flush after each use , thereby exposing residents to contamination . The community latrines , however , are located at considerable distances from the living area of families in this setting and thus present a lesser risk of household fecal contamination . Further investigation into household toilet usage and behavior is warranted . Additionally , storage of water in houses with a large number of residents was found to be a risk for infection . Contamination of water stored in a household is common in regions where municipal water supply is unsafe [37] . Large numbers of people using one supply of stored water increases the opportunity for contamination due to increased contact with hands and utensils . It has been suggested that S . Paratyphi A and S . Typhi may follow different transmission routes and the former requires a higher infectious dose for clinical disease [19] . Our exposure analysis supports this notion and suggests that while the two serovars share some risk factors and protective effects , the two organisms may have some independent epidemiology . In comparing various exposures between those with S . Typhi and S . Paratyphi A , S . Typhi cases were more likely to be associated with a poor-quality water source than the S . Paratyphi A cases . Water contamination with S . Typhi has been demonstrated previously in Nepal [16] , [21] . The traditional stone spouts found throughout Kathmandu are supplied by a system of waterways with high risk of sewage contamination due to poor maintenance . High concentrations of S . Typhi and S . Paratyphi A DNA , in addition to fecal coliforms , have been reported from water samples from these stone spouts [10] . The fact that a majority of water-related and neighborhood condition exposures did not remain significantly associated with S . Typhi infection in the analysis is likely a result of our study design as both cases and controls within a particular neighborhood were likely to report using the same water sources . Finally , an additional risk factor specific for S . Typhi included a household income <$125/month; poor socioeconomic status is a well-known risk factor for infection with S . Typhi [13] , [19] . For S . Paratyphi A induced enteric fever , two factors were found to be associated with risk of infection: residing in Kathmandu for less than years and eating street food in the two weeks preceding illness . Migration to Kathmandu from surrounding rural areas in search of economic prosperity is common and thus recent arrival into the city may correlate with immunologic naiveté . As S . Paratyphi A is an emerging pathogen in this setting [28] , individuals from rural areas may not be exposed to the bacteria until arriving in Kathmandu . As S . Typhi has traditionally been the dominant serovar in this region , it is possible that transmission occurs throughout urban and rural Nepal [38] . Additionally , eating street food has been implicated in a previous study from Indonesia as a risk factor for infection with S . Paratyphi A as S . Paratyphi A reaches the required threshold to cause disease within certain food products [19] . As there is no hygiene legislation for street vendors in Kathmandu , such conditions and lengthy incubation periods are feasible . There are some limitations with this study . Firstly , matching controls for ward of residence influenced the analysis , as the ward of residence is likely to be a correlate of various exposures . This altered the interpretation of the results , as many of the case/control pairs were concordant with regard to exposures , and did not contribute to the overall estimate of effect . Additionally , as shown by the serological data , identifying an appropriate group of controls in an endemic area poses a significant challenge . Given these challenges , we were still able to identify risks for enteric fever within this population that may allow for targeted interventions for the reduction of transmission . Such information is valuable as informed control and prevention strategies could provide a palatable and feasible method of reducing overall disease burden in this area of high endemicity . Historical surveillance data suggest that enteric fever rates decrease in parallel with the introduction of treatment of water supplies , and the exclusion of human feces from food production [39] , [40] . Improvements in the infrastructure of the municipal water delivery system , in addition to the provision of a combined vaccine against both S . Typhi and S . Paratyphi A , would be optimum for eliminating enteric fever in Kathmandu . In the short to mid-term absence of these interventions , we advocate safer household water supplies through the use of small water filtration and storage systems [41] . Additionally , our data suggest that improvements in the quality of street food , as well as promotion of enteric fever and toilet hygiene awareness through campaigns educating the population on risks , symptoms and preventive measures would have the largest impact on the burden of enteric fever in Kathamandu .
|
Enteric fever , caused by ingestion of bacteria Salmonella Typhi or Salmonella Paratyphi A , is common in regions with poor water quality and sanitation . We sought to identify individual-level risks for infection in Kathmandu , Nepal , a region endemic for enteric fever . In this study , we enrolled patients presenting to hospital who were blood-culture positive for enteric fever and a series of community controls matched for age , gender and residential ward . Our findings suggest that while some risks for infection with S . Typhi and S . Paratyphi A overlap , these organisms also have distinctive routes of infection in this setting; poor water and socioeconomic status seemed more influential in infection with S . Typhi , whereas food consumption habits and migratory status were shown to play a larger role in infection with S . Paratyphi A . Additionally , serological evaluation of IgG levels against the Vi ( Salmonella Typhi ) and the O:2 ( Salmonella Paratyphi A ) antigens demonstrated high titers against both antigens throughout life , suggesting frequent and constant exposure to these organisms in Kathmandu . As major improvements in sanitation infrastructure are unlikely in this setting , we recommend water treatment and storage-based prevention strategies , as well as street food quality regulation , and the promotion of vaccination with existing typhoid vaccines .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"bacterial",
"diseases",
"infectious",
"diseases",
"infectious",
"disease",
"epidemiology",
"epidemiology",
"infectious",
"disease",
"control",
"gastrointestinal",
"infections",
"salmonella"
] |
2013
|
Differential Epidemiology of Salmonella Typhi and Paratyphi A in Kathmandu, Nepal: A Matched Case Control Investigation in a Highly Endemic Enteric Fever Setting
|
The epithelial-mesenchymal transition ( EMT ) is an embryonic transdifferentiation process consisting of conversion of polarized epithelial cells to motile mesenchymal ones . EMT–inducing transcription factors are aberrantly expressed in multiple tumor types and are known to favor the metastatic dissemination process . Supporting oncogenic activity within primary lesions , the TWIST and ZEB proteins can prevent cells from undergoing oncogene-induced senescence and apoptosis by abolishing both p53- and RB-dependent pathways . Here we show that they also downregulate PP2A phosphatase activity and efficiently cooperate with an oncogenic version of H-RAS in malignant transformation of human mammary epithelial cells . Thus , by down-regulating crucial tumor suppressor functions , EMT inducers make cells particularly prone to malignant conversion . Importantly , by analyzing transformed cells generated in vitro and by characterizing novel transgenic mouse models , we further demonstrate that cooperation between an EMT inducer and an active form of RAS is sufficient to trigger transformation of mammary epithelial cells into malignant cells exhibiting all the characteristic features of claudin-low tumors , including low expression of tight and adherens junction genes , EMT traits , and stem cell–like characteristics . Claudin-low tumors are believed to be the most primitive breast malignancies , having arisen through transformation of an early epithelial precursor with inherent stemness properties and metaplastic features . Challenging this prevailing view , we propose that these aggressive tumors arise from cells committed to luminal differentiation , through a process driven by EMT inducers and combining malignant transformation and transdifferentiation .
While the disruption of embryonic processes has been acknowledged as a cause of the outgrowth of paediatric neoplasms , more recent observations suggest that the aberrant reactivation of developmental regulatory programs might also contribute to progression in the advanced stages of cancers in adults [1] . At the crux of this concept is the subversion of the epithelial-mesenchymal transition ( EMT ) during tumor progression . This developmental program converts epithelial cells into mesenchymal ones through profound disruption of cell-cell junctions , loss of apical-basolateral polarity and extensive reorganization of the actin cytoskeleton [2] . During embryogenesis , EMT plays critical roles in the formation of the body plan and in the differentiation of most of the tissues and organs derived from the mesoderm and the endoderm [3] . This process is tightly regulated through a delicate interplay between environmental signals from WNT , TGFβ , FGF family members , and a complex network of signaling pathways that converge on the activation of transcription factors that induce EMT through repression of CDH1 ( encoding for the E-cadherin ) and activation of mesenchymal genes . EMT-inducing transcription factors include several zinc finger proteins ( e . g . , SNAIL1 , SNAIL2 ) , basic helix-loop-helix transcription factors ( e . g . , TWIST1 , TWIST2 and E2A ) and zinc-finger and homeodomain proteins ( ZEB1 , ZEB2/SIP1 ) [4] , [5] . Importantly , while EMT inducers are maintained in a silent state in adult differentiated epithelial cells , their reactivation is commonly observed in a variety of human cancers with a frequent correlation with poor clinical outcome [6] . In the course of tumor progression , the gain of cell motility and the secretion of matrix metalloproteases associated with EMT promote cancer cell migration across the basal membrane and invasion of the surrounding microenvironment , favoring metastatic dissemination . Furthermore , EMT may also facilitate second site colonization by endowing cells with stem-like features including self-renewing properties [7]–[9] . While the involvement of EMT inducers in the invasion-metastasis cascade of epithelial tumors is well delineated , their contribution to tumorigenesis remains unclear . Supporting an oncogenic activity within primary lesions , we recently demonstrated that the TWIST proteins were able to prevent cells from undergoing oncogene-induced senescence and apoptosis by abrogating both p53- and RB-dependent pathways [10] , [11] . As a consequence , TWIST1 and TWIST2 can cooperate with an activated version of RAS to transform mouse embryonic fibroblasts [11] . Furthermore , the ZEB transcription factors were recently shown to overcome EGFR-induced senescence in oesophageal epithelial cells , suggesting that several EMT-inducers might share the property of inhibiting oncogene-induced failsafe programs [12] . On the basis of these findings , we sought to formally assess the oncogenic activity of these EMT-promoting factors in the model of breast tumorigenesis by generating Twist1 transgenic mouse models and by performing cooperation assays in human mammary epithelial cells ( HMECs ) . The focus of this study was underpinned by the common reactivation of ZEB1 , ZEB2 and TWIST1 in aggressive and undifferentiated human breast cancers , especially in the newly identified claudin-low intrinsic subtype [13] . Here we demonstrate that commitment to an EMT program favors breast tumor initiation by inhibiting crucial tumor suppressor functions , including PP2A ( protein phosphatase 2A ) activity , and thus minimizes the number of events required for neoplastic transformation . Importantly , upon aberrant activation of an EMT inducer , a single mitogenic activation is sufficient to transform mammary epithelial cells into malignant cells exhibiting all the characteristic features of claudin-low tumors . These findings extend our understanding of the role of EMT-inducing transcription factors during tumor development and highlight the claudin-low tumor subtype of breast cancers as the first example of human adult malignancies driven by aberrant reactivation of an embryonic transdifferentiation program .
To gain insight into the role of EMT commitment in tumor initiation and primary tumor growth , we used a Twist1 transgenic mouse model exhibiting a lox-STOP-lox ( LSL ) version of the active version of the murine TWIST1 ( TWIST1-E12 tethered dimer ) under a ubiquitous promoter [14] . These mice were crossed with a line expressing a lox-STOP-lox regulated knock-in of the activated K-Ras oncogene ( LSL-K-rasG12D ) [15] , [16] for in vivo oncogenic cooperation experiments . Transgene expression was first induced using a Mouse Mammary Tumor Virus promoter driven Cre recombinase ( MMTV-Cre ) and thereby restricted to secretory tissues , in particular the mammary gland and skin epithelia , as well as to the hematopoietic system [17] , [18] . Neither wild-type nor MMTV-Cre;Twist1 mice exhibited tumor formation by one year of age ( n = 47 ) . Expression of knock-in K-rasG12D was associated with low-grade splenic lymphomas as well as anal and oral papillomas ( Figure 1 ) . Importantly , papillomas never progressed to a malignant stage but could grow to the point to physical obstruction leading to cachexia and requiring euthanasia ( n = 85 , median survival 85 days ) . In contrast , MMTV-Cre;K-rasG12D;Twist1 mice invariably developed aggressive multifocal squamous cell carcinomas ( SCC ) at very young ages necessitating euthanasia at the significantly earlier median age of 35 days ( n = 12 , p<0 . 0001 , Figure 1 ) . These observations demonstrated for the first time the oncogenic properties of TWIST1 in vivo and underscored the cooperative effect between K-RAS and TWIST1 in promoting malignant conversion . Due to the speed with which SCC developed in the MMTV-Cre;K-rasG12D;Twist1 animals , the role of TWIST1 in promoting mammary tumor formation could not be assessed . Consequently , transgene expression was next restricted to differentiated mammary epithelial cells by using mice expressing the Cre recombinase under the control of the Whey Acidic Protein promoter ( WAP-Cre ) [17] . WAP is a milk protein expressed late in the differentiation pathway of mammary epithelial cells [19] . The 2 . 6-kb fragment of the mouse WAP gene promoter used in the present study is active in the mammary alveolar epithelium during the second half of pregnancy upon the initiation of differentiation [17] , [20] . In virgin animals , the promoter is only transiently activated in a few mammary alveolar and ductal cells , during estrus ( average age at first estrus = 35 days ) , allowing mosaic transgene activation so as to better mimic the emergence of spontaneous oncogenic activations [19] . Wild-type , WAP-Cre;K-rasG12D , and WAP-Cre;Twist1 transgenic females exhibited normal mammary gland development ( Figure 2B ) and remained healthy for at least 240 days ( n = 19 ) . However , all virgin WAP-Cre;K-rasG12D;Twist1 females developed multifocal breast carcinomas by 140 days of age , approximately 105 days after first transgene expression ( p<0 . 001 , median survival = 125 days , n = 9 ) ( Figure 2A and 2D ) . These tumors exhibited metaplastic features with a mixed morphologic aspect that included epithelial-type and spindle shaped cells ( Figure 2D ) . In support of the relevance of EMT in vivo , the presence of the tagged-TWIST1 transgene in both the epithelial and fusiform cancer cell contingents demonstrated that mesenchymal cells arose through transdifferentiation of their epithelial counterparts ( Figure 2D ) . Molecular characterization of human and murine breast tumors led to identifying five intrinsic subtypes ( luminal A , luminal B , HER2-enriched , basal-like and claudin-low ) [13] , [21] , [22] . Global gene expression profile analysis ( Accession number GSE32905 ) classified tumors developed by WAP-Cre;K-rasG12D;Twist1 transgenic mice as claudin-low ( Figure S1 ) . Immunostaining of epithelial and mesenchymal markers ( Figure 2D ) and quantitative RT-PCR analysis ( lack of E-cadherin and claudin expression , high expression of vimentin; Figure S2 ) were highly consistent with this classification . Of note , endogenous expression of the Zeb1 , Zeb2 and Twist2 EMT inducers was also induced ( Figure S2 ) , further supporting the association of the EMT interactome with the claudin-low breast cancer subtype [23] . Both basal-like and claudin-low subgroups are aggressive , chemoresistant , triple-negative carcinomas ( estrogen-receptor- , progesterone-receptor- , and HER2-negative ) . Yet claudin-low tumors exhibit several characteristic features , including low expression of adherens and tight junction proteins , a low level of luminal/epithelial differentiation , stem cell-like features , and a high frequency of metaplastic differentiation [24] . These tumors are believed to originate from an early epithelial precursor with inherent stemness properties and metaplastic features [24]–[26] . Nevertheless , the observation of claudin-low tumors in WAP-Cre;K-RasG12D;Twist1 transgenic mice suggested that the development of these neoplasms could rely upon an EMT-driven process affecting epithelial cells formerly engaged in differentiation . We sought to test this hypothesis by mimicking in non-stem cells events occurring commonly in this breast cancer subtype . Claudin-low tumors and cell lines frequently exhibit increased levels of the EMT-inducing transcription factors TWIST1 , ZEB1 , and ZEB2 [13] and show activation of RAS/MAPK pathway components [24] , [27] . To functionally reproduce these two common features of claudin-low tumors , we performed oncogenic cooperation assays by transducing genes encoding a single EMT inducer ( TWIST1 , ZEB1 or ZEB2 ) and/or an active form of H-RAS ( H-RASG12V ) into immortalized human mammary epithelial cells ( hTERT-HMECs , thereafter named HME cells ) . As in all experiments the infection efficiency exceeded 80% , the hypothesis of selection of a rare subpopulation of parental cells can be ruled out . Forced expression of an EMT inducer triggered acquisition of EMT features , including significant upregulation of mesenchymal markers ( i . e . , vimentin , fibronectin ) and decreased expression of genes involved in epithelial cell-cell adhesion ( i . e . , E-cadherin , occludin ) ( Figure 3 ) . However , the degree of EMT commitment observed after infection was highly dependent on the EMT-inducing transcription factor , both in the absence or in the presence of the active form of RAS . In the absence of the mitogenic oncoprotein , ZEB1 expression was sufficient to promote a complete transdifferentiation process , giving rise to typical spindle-like cell morphology , and a total loss of E-cadherin expression ( Figure 3; HME-ZEB1 cells ) . In contrast , ZEB2-expressing cells ( HME-ZEB2 cells ) and TWIST1-expressing cells ( HME-TWIST1 cells ) exhibited intermediate phenotypes , maintaining significant levels of E-cadherin expression and retaining a cobblestone morphology despite increased levels of mesenchymal markers such as vimentin and fibronectin ( Figure 3 ) . Transduction of an active form of RAS further promoted EMT induction , as assessed by cell morphology ( Figure 3A ) and protein expression analysis ( Figure 3F and 3G ) . Nevertheless , HME-ZEB2-RAS and HME-TWIST1-RAS cells still exhibited an intermediate phenotype . Importantly , combining the mitogenic oncoprotein with either TWIST1 , ZEB1 or ZEB2 was sufficient to provide cells with transformation potential , as assessed by their ability to form colonies in an assay on semi-solid medium and by acquisition of a characteristic stellate phenotype in 3D cell culture ( Figure 3C and 3D ) . This observation suggested that EMT inducers could exert a potent oncogenic activity in the absence of a complete EMT . Global gene expression array analysis was next performed on freshly established cell lines ( Accession number GSE32905 ) . Strikingly , HME-ZEB1 cells and HME-ZEB1-RAS cells were defined as basal-B ( P = 2 . 7×10−20 and 2 . 4×10−22 respectively ) , reminiscent of claudin-low tumors [13] , [21] , HME-TWIST1 , HME-TWIST1-RAS and HME-ZEB2 as basal-A ( P = 3 . 6×10−8 , P = 1 . 1×10−2 , P = 3 . 8×10−9 respectively ) , reminiscent of basal-like tumors [13] , [21] , while HME-ZEB2-RAS positively correlated with basal-A/basal-like ( P = 5 . 0×10−2 ) and basal-B/claudin-low ( P = 2 . 3×10−2 ) ( Figure 4 and Figure S3 ) , suggesting a direct link between the extent of EMT and the intrinsic subtype . Confirming this hypothesis , exposure of HME-TWIST1-RAS and HME-ZEB2-RAS to the EMT-promoting cytokine TGFβ triggered a complete EMT with a shift to basal-B/claudin-low ( P = 2 . 3×10−18 for TGFβ-treated HME-TWIST1-RAS cells; P = 6 . 8×10−22 for TGFβ-treated HME-ZEB2-RAS cells ) ( Figure 4 and Figure S3 ) . The extent of the EMT and the basal-B/claudin-low profiling were strongly associated with acquisition of stem cell-like features , as judged by the ability to form mammospheres under non-adherent culture conditions ( Figure 3E ) and by the fraction of cells exhibiting the CD44+/CD24−/low stem-like antigenic phenotype ( respectively 84 . 5% , 83 . 2% , 20 . 6% and 15 . 3% of HME-ZEB1; HME-ZEB1-RAS , HME-ZEB2-RAS and HME-TWIST1-RAS; Figure 5 ) . The gain of a mammary stem cell signature was also revealed by the use of the recently described Genomic Differentiation Predictor [13] , following global gene expression array analysis on freshly established cell lines ( Figure 6 ) . Cells exhibiting the more pronounced mesenchymal phenotype ( HME-ZEB1 , HME-ZEB1-RAS; HME-ZEB2-RAS treated with TGFβ and HME-TWIST1-RAS treated with TGFβ ) exhibited a mammary stem like signature , whereas cells with an epithelial or an intermediate phenotype ( HME , HME-TWIST1 , HME-TWIST1-RAS; HME-ZEB2; HME-ZEB2-RAS ) showed a luminal progenitor signature . As expected from earlier studies [28]–[30] , immortalized HMEC cells into which only H-RASG12V had been transduced ( HME-RAS ) exhibited a low transformation potential ( Figure 3D ) . Characterization of the few colonies growing on soft agar revealed a constant endogenous activation of EMT inducers , including TWIST1 , ZEB1 and ZEB2 ( Figure S4 ) , and a mesenchymal phenotype , further highlighting the deleterious interplay between the mitogenic oncoprotein and EMT-promoting factors . To confirm the association between the RAS-induced transformation and the endogenous expression of EMT inducers , immortalized HMECs were transduced with an H-RASG12V and sorted by flow cytometry using the EpCAM epithelial antigen . EpCAM-positive epithelial cells were next cultured in the presence of TGFβ . As shown in Figure S5 , TGFβ exposure triggered morphological and phenotypical features of EMT , associated with increased expression of TWIST1 , TWIST2 , ZEB1 and ZEB2 EMT inducers . Reactivation of these transcription factors was associated with the acquisition of a transformed phenotype . Taken together , these observations showed that activation of EMT inducers , through either forced expression or endogenous induction , fosters malignant transformation of mammary epithelial cells and confers to them basal-like or claudin-low signatures , according to the extent of transdifferentiation . Our data demonstrated that EMT inducers can promote transformation of mammary epithelial cells . We and others have previously shown that the TWIST and ZEB proteins can functionally inhibit p53- and RB-dependent pathways , preventing cells from undergoing oncogene-induced senescence and apoptosis [10]–[12] . To test whether the oncogenic properties of EMT-inducing transcription factors act only to lift these two oncosuppressive barriers or whether they might be involved in additional processes , we have generated human mammary epithelial cells deficient in both pathways . The INK4A tumor suppressor , a crucial regulator of the RB-dependent pathway , is known to be silenced by progressive promoter methylation in HMECs escaping from stasis [31] . We depleted these cells of p53 by means of RNA interference ( using a shRNA TP53 thereafter named shp53 or , as a control , a scrambled shRNA ) . Knockdown of p53 was checked by western blotting and by demonstrating that , in response to DNA damage , p53 induction and the resulting G1 growth arrest were abolished ( Figure S6 ) . Cells were next infected with H-RASG12V and immortalized by transfection with hTERT to generate shp53/H-RASG12V/hTERT HMECs ( hereafter named HME-shp53-RAS cells ) . Characterization of the colonies generated after growth of these cells in soft agar demonstrated that a vast majority of them expressed mesenchymal markers ( Figure S7E ) . This observation led us to hypothesize that a subset of HME-shp53-RAS cells committed spontaneously to an EMT program and that initiation of the transdifferentiation process promoted cell transformation . In support of the first hypothesis , cells exhibiting a cobblestone phenotype and expressing epithelial markers ( E-cadherin+ and EpCAM+ ) and cells displaying a fibroblastic morphology and exhibiting mesenchymal markers ( vimentin+ ) were found to coexist in HME-shp53-RAS cells ( Figure S7C ) . Epithelial and mesenchymal cell subpopulations were next sorted on the basis of their differential antigenic phenotypes ( EpCAM+ and EpCAM− respectively ) ( Figure S8 ) . The phenotypes of the epithelial and mesenchymal cell populations were confirmed by assessing the expression of additional epithelial markers ( β-catenin , E-cadherin , ZO-1 , and occludin ) and mesenchymal markers ( fibronectin and vimentin ) by immunofluorescence staining and western blotting ( Figure S9 ) . The sorted mesenchymal-cell subpopulations specifically displayed EMT-associated features such as motility , invasiveness , and a stellate phenotype when cultured in 3D ( Figure 7 ) . Gene profile analysis classified these mesenchymal cells as claudin-low/basal B , while epithelial HME-shp53-RAS cells were classified as basal-like/basal A ( Figure 4 and Figure S3 ) . Although epithelial and mesenchymal HMEC derivatives exhibited similar H-RASG12V expression levels ( Figure S10 ) , only mesenchymal cells grew in soft agar and gave rise to tumor formation , within three months , when homotopically xenografted in nude mice ( 6 of 7 mice , Figure 7 ) . These observations demonstrated that EMT commitment fosters malignant transformation of human mammary epithelial cells deprived of functional p53- and RB-dependent pathways . To further confirm this hypothesis sorted epithelial HME-shp53-RAS cells were treated with TGFβ . Exposure to this EMT-inducing cytokine triggered a pronounced shift from epithelial to mesenchymal markers , associated with induction of ZEB1 ( 200-fold ) and ZEB2 ( 10-fold ) ( data not shown ) and with a dramatic gain in anchorage-independent growth properties ( Figure S11 ) . Importantly , forced expression of ZEB1 in sorted epithelial HME-shp53-RAS cells was sufficient to mimic TGFβ exposure , promoting both EMT and cell transformation ( Figure S12 ) . Our observations strongly suggested that , beyond the inhibition of the p53- and RB-dependent pathways , EMT inducers display additional , as yet unidentified oncogenic activities . It has been previously shown that , in vitro , transformation of normal human epithelial cells , including mammary epithelial cells , requires disruption of the telomere maintenance system and dysregulation of at least four key signaling pathways: activation of the RAS-dependent pathway and inhibition of the p53- , RB- , and protein phosphatase 2A-dependent pathways [28]–[30] . We thus endeavored to analyze the effects of EMT commitment on protein phosphatase 2A ( PP2A ) activity . PP2A is a ubiquitously expressed serine/threonine phosphatase accounting , with protein phosphatase 1 ( PP1 ) , for 90% of all the serine/threonine phosphatase activity in the cell [32] . By using a peptide substrate ( synthetic phosphothreonine peptide RRA ( pT ) VA ) compatible with the phosphatase activity of PP2A but not with that of PP1 and by employing experimental conditions ensuring the specificity of PP2A activity ( see Materials and Methods ) , we found sorted mesenchymal HME-shp53-RAS cells to exhibit lower phosphatase activity than sorted epithelial HME-shp53-RAS cells ( Figure 8 ) . More importantly , expression of either TWIST1 , ZEB1 , or ZEB2 in HME cells was sufficient to trigger significant ( 2-fold ) downregulation of serine/threonine phosphatase activity , revealing a novel oncogenic feature of these proteins . This downregulation was similar to that observed in immortalized HMECs transformed with H-RASV12 and the SV40 large T and small t antigens ( HMLER cells; Figure 8 ) , the small t antigen being known to inhibit PP2A activity [33] . Notably , claudin-low HME-ZEB1-RAS cells exhibited 4-fold lower phosphatase activity than immortalized HMECs ( Figure 8 ) .
Whereas TWIST1 has been convincingly implicated in the metastatic dissemination of breast cancer cells , these data underscore the importance of EMT-inducing transcription factors in driving mammary carcinogenesis , with a dual role in cell transformation and dedifferentiation . Tumor development has been portrayed as a multistep processes with a progressive acquisition of genetic and epigenetic abnormalities providing cells with biological capabilities such as sustained proliferation , replicative immortality , survival advantages , angiogenesis and , in some cases , invasive growth and metastasis [34] . According to the Darwinian model of cancer development , each of these acquired traits confers a distinct selective advantage , originating successive waves of clonal expansion that drive tumor progression . It is well known that this complex and time consuming process requires abrogation of several oncosuppressive barriers . In epithelial cells , including mammary epithelial cells , these barriers comprise the p53- , RB- and PP2A-dependent pathways [28]–[30] . We and others have previously demonstrated that TWIST and ZEB transcriptions factors were capable to inhibit p53- and RB- dependent pathways [10]–[12] . Remarkably , our observations reveal that activation of these factors in HMECs also affects PP2A phosphatase activity . Considerable evidence highlights the tumor-suppressor functions of this serine/threonine phosphatase . For example , it has been shown in vitro that the transforming ability of the SV40 small t antigen requires interactions with PP2A and downregulation of its activity [34]–[36] . In vivo , mutations affecting different components of the PP2A holoenzyme complex have been identified in a variety of human malignancies and , in mouse models , mutation of PP2A favors tumorigenesis [37] . Loss of PP2A during cell transformation triggers multiple events , such as upregulation of kinases involved in mitogenic and survival signaling ( e . g . AKT and MAPK ) , stabilization of protooncogenes ( e . g . MYC ) , destabilization of tumor suppressors ( e . g . p53 and RB ) , and loss of proapoptotic signaling pathways ( e . g . BAD ) [32] . Modulation of downstream components of the RAS signaling pathway by PP2A might be of particular significance in our model , as the ability of PP2A to antagonize the oncogenic properties of RAS by dephosphorylating crucial downstream effectors such as c-MYC and AKT makes its downregulation a prerequisite to RAS-induced malignant transformation . The modulation of PP2A activity by EMT inducers might thus be an important mechanism underlying the deleterious cooperation of these factors with oncogenic RAS in cell transformation . The inhibition of PP2A activity by EMT inducers might also be relevant during embryogenesis , as PP2A appears as a negative regulator of the WNT signaling cascade [38] which is required for several crucial steps in early development . Further studies are needed , however , to better characterize the mechanisms involved in this regulatory process , as PP2A represents a complex family of holoenzyme complexes known to display different activities and to exhibit diverse substrate specificities [39] . Given the importance of p53- , RB- and PP2A-dependent protective barriers against tumorigenesis and their role in regulating cell differentiation and self-renewal [40] , aberrant reactivation of EMT inducers might profoundly affect the multistep nature of tumorigenesis by increasing cell plasticity and leapfrogging the mutation bottleneck toward tumor progression . This view is supported by our in vitro transformation assays demonstrating that , upon a single mitogenic activation , forced expression of either TWIST1 or ZEB1/2 is sufficient to trigger malignant conversion of immortalized human mammary epithelial cells ( hTERT-HMECs ) . It is also consistent with the observed rapid and repeated appearance of multifocal breast carcinomas in WAP-Cre;K-rasG12D;Twist1 mice . It is further supported by the work by Phuoc T . Tran and colleagues , who demonstrated in an elegant inducible transgenic mouse model that TWIST1 overexpression accelerates K-RAS-induced lung tumorigenesis [41] . Several phenomena associated with tumor initiation , such as inflammation [42] , physical constrains ( including hydrostatic pressure , shear stress and tension forces ) [43] , abnormal activation of signaling pathways such as those controlled by WNT , NOTCH , or TGFβ [4] , [44] , and hyperactivation or RAS-ERK1/2 signaling [45] are known to trigger expression of EMT-promoting factors and could thus induce reactivation of these embryonic transcription factors in early stages of tumor development , as previously observed in animal models [46] . Moreover , beyond the deleterious consequences of aberrant reactivation of EMT inducers in differentiated or committed epithelial cells , the ability of EMT inducers to inhibit key oncosuppressive pathways also implies that embryonic or adult stem cells that normally express these factors are particularly vulnerable to cell transformation . Cooperation assays demonstrate that activation of EMT-inducing transcription factors such as TWIST1 or ZEB2 is sufficient to make cells highly prone to transformation , even in the absence of a complete mesenchymal morphological shift . In line with this view , immunohistochemical analysis of TWIST1 in human non-invasive breast cancers ( ductal carcinomas in situ , DCIS ) has revealed frequent overexpression of this EMT inducer within the bulk of the primary lesion , while the cancer cells maintain an epithelial phenotype ( Figure S13 ) . EMT is known to be a highly dynamic process giving rise to a series of important changes in cell phenotype , including loss of cell polarity , loss of cell-cell adhesion structures , remodeling of the cytoskeleton , and promotion of cell motility . As recently highlighted by Klymkowsky and Savagner , although the term EMT is generally applied as if it were a single conserved process , EMT-related processes can in fact vary in degree from a transient loss of cell polarity to total reprogramming of the cell [47] . The existence of malignant cells with combined epithelial and mesenchymal characteristics has previously been demonstrated in vivo , in both mouse models of EMT and human tumors [48] , [49] . Especially , epithelial cells coexpressing cytokeratins 5/19 and vimentin have been identified by dual immunofluorescence labeling in claudin-low and basal-like breast cancers , two breast cancer subtypes frequently exhibiting overexpression of EMT-inducing transcription factors [13] . Overall , these observations strongly suggest that EMT-promoting factors can exert oncogenic functions in cells retaining an epithelial phenotype , in the total absence of morphological features of EMT , and probably long before initiation of the invasion-metastasis cascade . Previous in vitro studies using human mammary epithelial cells have revealed a link between EMT , malignant transformation , and acquisition of stem cell properties . For example , the transformation of HMECs by means of a combination of hTERT , SV40 large T and small t antigens , and H-RASG12V ( HMLER cells ) is associated with both mesenchymal and stem-like features [7] , [8] , [50] . In the absence of oncogenic RAS , introduction of SV40 T and small t antigens and hTERT into mammosphere-derived HMECs also generates malignant cells exhibiting EMT and stem-like properties [51] . Recent reports further demonstrate in human cancer cell lines that spontaneous EMT or TGFβ/TNFα-mediated EMT generates cells with a claudin-low phenotype [52] , [53] . Yet the intrinsic role of EMT inducers was not addressed in these studies . We highlight herein a dual role of these factors in cell transformation and dedifferentiation . Remarkably , in the context of a very few genetic events , the aberrant activation of an EMT inducer can initiate mammary epithelial cell transformation in vitro and in vivo and can drive the growth of undifferentiated tumors exhibiting all the characteristic features of claudin-low tumors , including a malignant phenotype , low expression of tight and adherens junction genes , EMT traits , and stem-cell-like characteristics . The origin of the different intrinsic subtypes of human breast cancer is a topic of contentious debate and remains ill defined . Recent in vitro observations support the view that both luminal and basal-like breast cancers derive from a common luminal progenitor cell , whereas claudin-low tumors , viewed as the most primitive malignancies , originate from a stem/progenitor cell with inherent stemness properties and metaplastic features [24]–[26] . Others suggest that basal-like and claudin-low tumors arise from transformation of a similar stem cell , but that the claudin-low tumors stay arrested in an undifferentiated state , while basal-like cancer cells divide asymmetrically and give off differentiated progeny arresting at the luminal progenitor state [54] . Our observations pave the way for an alternative model highlighting a dynamic process orchestrated by the activity of EMT-inducing transcription factors . According to this model , aberrant activation of EMT inducers in committed cells ( e . g . luminal progenitors ) might foster initiation of triple-negative breast tumors and confer basal-like or claudin-low signatures , according to the extent of transdifferentiation . Our model also implies that basal-like tumors might progressively evolve towards a claudin-low phenotype through completion of the EMT process . This view is supported by the histopathology of human metaplastic breast tumors: phenotypically , the acquisition of mesenchymal features can occur at various stages of the disease [55] , highlighting the dynamic role of transdifferentiation during tumor development and pointing to the interaction between cancer cells and the microenvironment as a key determinant of tumor phenotype and behavior . It is noteworthy that a model of murine claudin-low tumors has recently been described , involving transplantation of p53-null mammary tissues into the cleared fat pads of wild-type recipients [56] . This observation is consistent with the role of p53 loss in EMT induction [57]–[59] and with the spontaneous generation of mesenchymal cells exhibiting a claudin-low phenotype in HME-shp53-RAS cells . Yet in this model described by Herschkowitz and colleagues , p53-null mouse mammary tumors fell into a variety of molecular groups , also including luminal and basal-like subtypes [56] . WAP-Cre;K-rasG12D;Twist1 mice thus appear as the first mouse model consistently generating claudin-low tumors . These transgenic mice might thus serve as valuable preclinical models for testing both potential therapeutic agents targeting these aggressive neoplasms and potential preventive agents .
The TWIST1-E12 tethered heterodimer was generated by PCR by fusing the human TWIST1 and E12 proteins using a G3-S2-G2-S-G3-S-G3-S2-G2-S-G3-S-G polylinker as described in [60] . The full-length murine HA-tagged ZEB1 and ZEB2 cDNAs were cloned into the pBabe retroviral construct . The TP53 shRNA ( shp53 ) pRETRO SUPER expression construct has been described in [61] . Animal maintenance and experiments were performed in a specific pathogen free animal facility “AniCan” at the CRCL , Lyon , France in accordance with the animal care guidelines of the European Union and French laws and were validated by the local Animal Ethic Evaluation Committee . The heterozygous knock-in LSL-K-rasG12D mouse strain [16] was crossed with CAG-LSL- ( Myc ) -Twist1 mice ( FVB background ) [14] . Both TWIST1 monomer and T1-E12 dimers were used producing similar results [60] . K-rasG12D;Twist1 offspring were subsequently crossed with mice carrying the Cre recombinase under the control of the Mouse Mammary Tumor Virus or Whey Acidic Protein promoters ( MMTV-Cre ( B6129F1 background ) or WAP-Cre ( c57BL/6 background ) [17]; purchased from the NCI-MMHCC . Cre ( wild type ) , Cre;K-rasG12D , Cre;Twist1 , and Cre;K-rasG12D;Twist1 virgin animals were maintained and monitored at least weekly for tumor incidence . End points were determined based on tumor diameter ( >17 mm ) or the sick appearance of an animal . Tissues were harvested and either snap frozen in N2 ( l ) or immersed in formalin until pathological analysis . Genotyping of genomic DNA from tails purified using the NucleoSpin Tissue kit ( Macherey-Nagel ) was performed with primers described in references [14] , [16] , [17] using REDTaq 2× ReadyMix ( Sigma ) . Primary human mammary epithelial cells ( HMECs ) were provided by Lonza . HMEC-derivatives were cultured in 1∶1 Dulbecco's Modified Eagle's Medium ( DMEM ) /HAMF12 medium ( Invitrogen ) complemented with 10% FBS ( Cambrex ) , 100 U/ml penicillin-streptomycin ( Invitrogen ) , 2 mM L glutamine ( Invitrogen ) , 10 ng/ml human epidermal growth factor ( EGF ) ( PromoCell ) , 0 . 5 µg/ml hydrocortisone ( Sigma ) and 10 µg/ml insulin ( Actrapid ) . Three-dimensional cultures consisted in culturing 5×103 cells/well in 2% growth factor reduced Matrigel ( BD Biosciences ) on top of a 100% matrigel layer . 20 days after seeding , cells were fixed in 3% paraformaldehyde ( Sigma ) , permeabilized in 0 . 5% Triton 100X ( Sigma ) in PBS buffer for 10 min . After several washes in PBS , cells were labeled with 1 µg/ml of TRITC-conjugated Phalloïdin P1951 ( Sigma ) for 45 min . Following washes in PBS , nuclei were stained with Hoechst 5 µg/ml for 10 min and mounted with Fluoromount-G ( SouthernBiotech ) . For mammosphere formation , after filtration through a 30 µm pore filter , single-cells were plated at a density of 105 cells/ml in Corning 3261 ultra-low attachment culture dishes . Primary cell spheres were enzymatically dissociated with 0 . 05% trypsin for 15 min at 37°C to obtain single-cell suspension . The ability to generate mammospheres was defined after three consecutive passages . Treatment with TGFβ was performed with 2 . 5 ng/ml of the recombinant cytokine ( Peprotech ) for a three week period . Cell distribution was performed using the FITC-EpCAM VU-1D9 ( Stem Cell ) , the FITC-CD44 G44-26 ( BD Pharmingen ) and the PE-CD24 ML5 ( BD Pharmingen ) monoclonal antibodies , the FACScan Calibur ( Becton Dickinson ) and analyzed using the FlowJo software . Matrigel ( BD Biosciences ) was added to the wells of an eight-well Labtek chamber in a volume of 300 µl/well . A Matrigel plug of about 1 mm diameter was removed . The hole was successively filled with 105 cells and 100 µl of Matrigel . Appropriate growth medium was added on top . Cultures were analyzed after 24 h ( Figure 7 ) or 72 h ( Figure S11 ) . Areas of migration were visualized using an Olympus IX50 ( NA 0 . 075 ) . Samples were performed in duplicate . 5×104 cells were placed in the upper chamber of an 8 µM Transwells ( BD Biosciences ) . 24 h later , chambers were washed twice with PBS . The filter side of the upper chamber was cleaned with a cotton swab . The membrane was next cut out of the insert . Cells were fixed in methanol and stained with 5% Giemsa 30 min at room temperature . 2×106 Phoenix cells were transfected by calcium-phosphate precipitation with 10 µg of retroviral expression vectors . 48 h post-transfection , the supernatant was collected , filtered , supplemented with 5 µg/ml of polybrene ( Sigma ) and combined with 106 targeted cells for 6 h . Cells were infected twice and selected 48 h post-second infection with puromycin ( 0 . 5 µg/ml ) , neomycin ( 100 µg/ml ) or hygromycin ( 25 µg/ml ) . To measure anchorage-independent growth , cells were detached with trypsin and resuspended in growth medium . Plates were prepared with a coating of 0 . 75% low-melting agarose ( Lonza ) in growth medium and then overlaid with a suspension of cells in 0 . 45% low-melting agarose ( 5×104 cells/well ) . Plates were incubated for 3 weeks at 37°C and colonies were counted under microscope . Experiments were performed in triplicate . Eight-week old female athymic Swiss nude mice ( C . River laboratories ) were X-irradiated ( 4 Gy ) prior to injection . Single cell suspensions , ( 5×106 HMEC derivatives resuspended in a PBS-Matrigel ( 1/1 ) mixture ) were injected into the fat pad of a mammary gland . Tumor incidence was monitored up to 90 days post-injection . Animals were allowed to form tumors up to 1 . 5 cm in diameter , at which point animals were euthanized . Each tumor was dissected , fixed in paraformaldehyde and processed for histopathology examination . Cells were washed twice with phosphate buffered saline ( PBS ) containing CaCl2 and then lysed in a 100 mM NaCl , 1% NP40 , 0 . 1% SDS , 50 mM Tris pH 8 RIPA buffer supplemented with a complete protease inhibitor cocktail ( Roche ) . Protein expression was examined by western blot using anti-E-cadherin clone 36 ( Becton Dickinson ) , anti-β-catenin clone 14 ( Becton Dickinson ) , anti-fibronectin clone 10 ( Becton Dickinson ) , anti-vimentin clone V9 ( Dako ) , anti-N-cadherin clone 32 ( Becton Dickinson ) , anti-occludin clone OC-3F10 ( Zymed Laboratories ) , anti-β-actin clone AC-15 ( Sigma ) , anti-HA clone 11 ( BabCO ) , anti-TWIST Twist2C1a ( Abcam ) monoclonal antibodies and a rabbit polyclonal anti-H-RAS clone C20 ( Santa Cruz ) for primary detection . Horseradish peroxidase-conjugated rabbit anti-mouse and goat anti-rabbit polyclonal antibodies ( Dako ) were used as secondary antibodies . Western blots were revealed using an ECL detection kit ( Amersham ) or a western-blotting Luminol reagent ( Santa Cruz ) . 104 cells were seeded on 8-well Lab-TekII chamber slide , fixed in 3% paraformaldehyde ( Sigma ) and permeabilized in 0 . 1% Triton 100X ( Sigma ) in PBS buffer at room temperature for 10 min . The cells were then washed 3 times with PBS and incubated with a blocking solution ( 10% horse serum in PBS ) . The cells were then incubated with the anti-E-cadherin clone 36 ( Becton Dickinson ) or the anti-vimentin clone V9 ( Dako ) primary antibodies overnight at 4°C . Phalloidin labeling was performed by incubating cells with 1 µg/ml of TRITC-conjugated Phalloïdin P1951 ( Sigma ) for 30 min . Following extensive washes in PBS , nuclei were stained with Hoechst 5 µg/ml for 10 min and mounted with Fluoromount-G ( SouthernBiotech ) . All matched samples were photographed ( control and test ) using an immunofluorescence microscope ( Leica ) and identical exposure times . The immunohistochemical study was performed on three microns deparaffinized sections , using the avidin-biotin-peroxidase complex technique ( LSAB universal , Dako ) , after 15 min heat-induced antigen retrieval in 10 mM citrate buffer , pH 6 . The primary anti-E-cadherin clone 36 diluted at 1/500 ( Becton Dickinson ) , anti-vimentin clone V9 diluted at 1/200 ( Dako ) and anti-c-MYC A14 at 1/100 ( Santa-Cruz Biotechnology ) antibodies were applied 60 min at room temperature . Paraffin embedded tumors were serially sectioned at a thickness of 4 µm . After deparaffinisation and rehydration , endogenous peroxidases were blocked by incubating the slides in 5% hydrogen peroxide in sterile water . For heat induced antigen retrieval , tissue sections were boiled at 97°C for 40 min either in a 10 mM citrate buffer pH 6 ( when anti-cytokeratin and anti-vimentin antibodies were used ) or in buffer pH 7 ( Dako ) ( for the anti-E-cadherin antibody ) clone 36 ( BD Biosciences ) . Slides were then incubated with the monoclonal pancytokeratin clone AE1/AE3 , ( Dako ) , the polyclonal anti-vimentin SC7557 ( Santa Cruz ) or the monoclonal anti-E-cadherin clone 36 ( BD Biosciences ) primary antibodies or a non-immune serum used as a negative control , for 1 h at room temperature . Slides were rinsed in phosphate buffered saline , and then incubated with a biotinylated secondary antibody bound to a streptavidin peroxidase conjugate ( LSAB+ kit , Dako ) . Microarray processing and data analysis as well as procedures to classify human cell lines and mammary tumors are described in detail in Text S1 . Cells were lysed in a 50 mM Tris-HCl ( pH 7 . 5 ) , 150 mM NaCl , 2 mM EDTA , 1 mM EGTA , 0 . 3% CHAPS lysis buffer supplemented with a protease inhibitor cocktail ( Roche ) and cleared by centrifugation . The PP2A activity was assessed using the “Serine/Threonine Phosphatase Assay System” ( Promega ) according to the manufacture instruction . Briefly , the cleared cell lysate was filtered through a Sephadex G25 column to remove free phosphate . Protein concentration was determined using the Bradford method . 5 µg of cell protein was incubated in presence of the RRA ( pT ) VA substrate in a 250 mM imidazole pH 7 . 2 , 1 mM EGTA , 2 mM EDTA , 0 . 1% β-mercaptoethanol , 0 . 5 mg/ml BSA PP2A-specific reaction buffer at 25°C for 30 min . After incubation with 50 µl of molybdate dye/additive at 25°C for 30 min , optical density was measured at 620 nm . All determinations were performed in triplicate and the absorbance of the reactions was corrected by determining the absorbance of control reactions without phosphoprotein substrate . The PP2A activity was performed in presence of absence of 5 nM of okadoic acid to confirm the specificity of these reaction conditions . The amount of phosphate released ( pmol ) was calculated from a standard curve ( 0–2000 pmol ) and was normalized with respect to HMEC-hTERT cells .
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The epithelial-mesenchymal transition ( EMT ) is essential to germ layer formation and cell migration in the early vertebrate embryo . EMT is aberrantly reactivated under pathological conditions , including fibrotic disease and cancer progression . In the latter process , EMT is known to promote invasion and metastatic dissemination of tumor cells . EMT is orchestrated by a variety of embryonic transcription factors called EMT inducers . Among these , the TWIST and ZEB proteins are known to be frequently reactivated during tumor development . We here report in vitro and in vivo observations demonstrating that activation of these factors fosters cell transformation and primary tumor growth by alleviating key oncosuppressive mechanisms , thereby minimizing the number of events required for acquisition of malignant properties . In a model of breast cancer , cooperation between a single EMT inducer and a single mitogenic oncoprotein is sufficient to transform mammary epithelial cells into malignant cells and to drive the development of aggressive and undifferentiated tumors . Overall , these data underscore the oncogenic role of embryonic transcription factors in initiating the development of poor-prognosis neoplasms by promoting both cell transformation and dedifferentiation .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"medicine",
"biology"
] |
2012
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EMT Inducers Catalyze Malignant Transformation of Mammary Epithelial Cells and Drive Tumorigenesis towards Claudin-Low Tumors in Transgenic Mice
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Immunization programs have often been impeded by vaccine scares , as evidenced by the measles-mumps-rubella ( MMR ) autism vaccine scare in Britain . A “free rider” effect may be partly responsible: vaccine-generated herd immunity can reduce disease incidence to such low levels that real or imagined vaccine risks appear large in comparison , causing individuals to cease vaccinating . This implies a feedback loop between disease prevalence and strategic individual vaccinating behavior . Here , we analyze a model based on evolutionary game theory that captures this feedback in the context of vaccine scares , and that also includes social learning . Vaccine risk perception evolves over time according to an exogenously imposed curve . We test the model against vaccine coverage data and disease incidence data from two vaccine scares in England & Wales: the whole cell pertussis vaccine scare and the MMR vaccine scare . The model fits vaccine coverage data from both vaccine scares relatively well . Moreover , the model can explain the vaccine coverage data more parsimoniously than most competing models without social learning and/or feedback ( hence , adding social learning and feedback to a vaccine scare model improves model fit with little or no parsimony penalty ) . Under some circumstances , the model can predict future vaccine coverage and disease incidence—up to 10 years in advance in the case of pertussis—including specific qualitative features of the dynamics , such as future incidence peaks and undulations in vaccine coverage due to the population's response to changing disease incidence . Vaccine scares could become more common as eradication goals are approached for more vaccine-preventable diseases . Such models could help us predict how vaccine scares might unfold and assist mitigation efforts .
Vaccine coverage in England & Wales during the whole cell pertussis vaccine scare in the 1970s and the measles-mumps-rubella ( MMR ) vaccine scare in the 1990s share a common pattern of decline and recovery over many years ( Figure 1 ) . For pertussis , the decline resulted in large-scale outbreaks . MMR coverage declined much less and the resulting outbreaks were smaller , although measles was declared endemic again by 2008 [1] . Theory suggests that vaccine scares exemplify a “free-rider problem”: vaccine-generated herd immunity can reduce disease incidence to such low levels that vaccine risks appear large in comparison , causing some individuals to cease vaccinating . Hence , these non-vaccinators effectively “free ride” on the herd immunity generated by vaccinators . Game theory analyzes situations where the outcome of an individual's choice depends on the choices made by other individuals . Thus , game theory can be used to analyze free-rider problems such as vaccine scares . A growing literature combines mathematical models of disease transmission with game theory or other behavioral models to explore the feedback loop that connects disease incidence and vaccinating behavior among individuals: disease incidence influences vaccinating behavior through individuals wanting to avoid health risks , and vaccinating behavior in turn influences disease incidence through herd immunity generated by vaccination [2]–[14] . A crucial assumption of these “behavior-incidence” models is that disease incidence feeds back on vaccinating behavior: a surge in disease incidence can convince individuals to start being vaccinated again . However , it is not immediately clear whether feedback is necessary to explain the time series of vaccine coverage in Figure 1: it may just reflect the gradual evolution of individuals' risk perception , irrespective of the influence of disease incidence . In both vaccine scares , the publication of alleged vaccine risks was followed by a media firestorm in national newspapers , television , and radio [15] , [16] . In light of this , the fact that it took 4–5 years for vaccine coverage to bottom out is puzzling . Peer opinion partly determines vaccine uptake [17] , and social learning might explain the delay: to some extent , non-vaccinating behavior would have to spread from parent to parent . For significant parts of many historical vaccine coverage time series , vaccine coverage is roughly constant if a vaccine scare is not occurring . It is relatively easy to make behavior-incidence models reproduce constant vaccine coverage because there are sufficient degrees of freedom in parameter space [7] . In contrast , vaccine scares constitute a more decisive test of these models , because a large part of the space of possible model dynamics is visited over the course of the vaccine scare , due to relatively rapid changes in vaccine coverage over time . Hence , we focus our analysis on the time periods during ongoing vaccine scares . Two vaccines scares in England & Wales offer ideal natural experiments for testing these models: the whole cell pertussis vaccine scare from the 1970s and the MMR vaccine scare from the 1990s . Our first objective was to determine whether a behavior-incidence model that includes social learning and disease incidence feedback can explain the vaccine coverage data from these two vaccine scares better than competing explanations that ignore social learning and/or feedback mechanisms . Our second objective was to determine whether this model could predict in advance the time evolution of vaccine coverage and disease incidence as observed in these two vaccine scares .
In stage one , we formulated a social learning process based on the imitation dynamic of evolutionary game theory [18] . An individual samples others in the population at a constant rate . If the sampled person is playing a different strategy and is receiving a higher payoff , the individual switches to that strategy with a probability proportional to the expected gain in payoff . The payoff gain depends on the difference between the penalty for being vaccinated and the penalty for risking infection . In our model , individuals can choose to vaccinate , or not to vaccinate ( “vaccinator” versus “non-vaccinator” strategies ) . The infection penalty is the perceived probability of being infected—which we assumed is simply proportional to the current disease incidence—times the perceived cost of being infected . In stage one , we simply took disease incidence directly from the data in Figure 1 instead of an incidence model , resulting in a “behavioral model” rather than a full behavior-incidence model . The resulting equations for the behavioral model are ( 1 ) where x is the proportion of vaccinators in the population at time t , s is the sampling rate , θ is the proportionality constant influencing the probability of switching strategies according to the expected gain in payoff , cv is the penalty to vaccinate , ci is the penalty for becoming infected , L is the number of case notifications at time t ( taken from the data in Figure 1 ) , and m is a proportionality constant governing the perceived probability of being infected ( we note that for m and θ sufficiently small the relevant probabilities are always less than 1 ) . The expression ( −cv+cimL ) is the payoff gain for switching strategies and its sign determines whether vaccinator or nonvaccinator is the favored switch . Equation ( 1 ) is derived in the Supporting Information ( Text S1 ) . Equation ( 1 ) can be further distilled to ( 2 ) where κ = sθcim and ω = cv/mci . This is the form of the model we use in the analysis . The parameter ω has absorbed cv which , unlike other parameters , evolves over time as the perceived vaccination penalty changes during the vaccine scare . We wanted to determine whether adding social learning and feedback in this way to some underlying description of how the perceived vaccination penalty evolves over time can better explain Figure 1 . Hence , we formulated five risk evolution curves that govern how the perceived vaccination penalty could rise and fall during the scare . The function ω = ω ( t ) denotes the risk evolution curve describing time evolution of the vaccine penalty . ω ( t ) is constant at ωpre until the vaccine scare , then climbs linearly for Dincrease years to reach a maximum of σωpre ( where σ>1 ) and remains there for Dmax years before declining linearly back to ωpre over a period of Ddecrease years . We explored five possible shapes for ω ( t ) : A diagram of ω ( t ) appears in Supporting Information ( Figure S1 ) . These curves were not motivated by a specific mechanistic model of risk perception , but rather were intended to describe a wide range of possible functional forms requiring differing numbers of parameters , thus enabling the explanatory power of the behavioral model to be tested against a broad range of potential competing candidates , as opposed to a single candidate . Public health efforts to restore faith in a safe and efficacious vaccine are represented as the eventual decline in perceived vaccine risk in these risk evolution curves . For each curve , we compared the parsimony ( explanatory power ) of the behavioral model with both social learning and feedback—Equation ( 2 ) —to three reduced behavioral models with: ( a ) social learning but no feedback: ( 3 ) ( b ) feedback but no social learning: ( 4 ) where ρ is a proportionality constant , and ( c ) neither social learning nor feedback: ( 5 ) These equations are derived in Supporting Information ( Text S1 ) . We used the AICc—a modified Akaike Information Criterion [19]–[22]—to evaluate the parsimony of the four models under all five risk evolution curves for each model , yielding 20 candidates altogether . Information criteria have a strong rooting in information theory , and favor models that explain the data as well as possible with as few parameters as possible . The model with the most negative AICc score is the one with the greatest parsimony , suggesting that it is likely capturing crucial determinants of the observed dynamics . We obtained confidence intervals using a non-parametric bootstrapping method . Additional details on model fitting and bootstrapping appear in the Supporting Information ( Text S1 ) . In stage 2 , we evaluated the parsimony of the full behavior-incidence model . We augmented our behavioral model with a Susceptible-Infectious-Recovered ( SIR ) compartmental model that captures disease transmission processes . Despite their simplicity , similar models have been shown to capture pertussis and measles dynamics relatively well [23]–[25] . In the SIR model , individuals are either Susceptible , Infectious , or Recovered ( immune ) . Susceptible individuals are infected at some rate and thereby moved to the Infectious compartment . From the Infectious compartment they recover at some rate and enter the Recovered compartment . Susceptible individuals who are efficaciously vaccinated are also moved to the Recovered compartment . Individuals are born into the Susceptible compartment at some rate , and leave the population due to death at some rate . For measles , the transmission rate was also made to vary seasonally [23]–[25] . For the behavioral component of the model , instead of making the perceived probability of being infected depend on the disease incidence data ( L ) , it now depends on the disease prevalence generated by the SIR model ( I ) . In turn , a proportion of infants are vaccinated according to the abundance of vaccinator strategists in the population at a given time ( x ) , completing the feedback loop . The equations for the resulting behavior-incidence model are: ( 6 ) where μ is the birth/death rate per capita , ε is the vaccine efficacy , β is the transmission rate , τ is the case importation rate , and γ is the recovery rate . For measles , a delay was also introduced between changes in incidence and changes in vaccine coverage , to capture phenomenologically the fact many parents have opted to delay immunization rather than avoid it altogether . As a result , for measles the x equation becomes ( 7 ) where δ is the delay , in years . We opted to introduce a fixed delay in the equations rather than explicitly incorporate delayer strategies in order to keep the number of parameters relatively low . The design of the parsimony analysis for the behavior-incidence model was similar to that of the behavioral model ( see Supporting Information , Text S1 ) . We note that the goodness of fit of the behavior-incidence model to disease incidence data does not , and cannot , contribute to the AICc score because in this respect there is no way to make a fair comparison between the behavior-incidence model ( which is capable of predicting incidence ) and the reduced models ( two of which are not capable of predicting incidence , by definition ) . In stage 2 we also tested the predictive power of the behavior-incidence model , under risk evolution curve #1 . The slope of curve #1 is fixed at the start of the scare and does not change thereafter . This allowed us to fit the behavior-prevalence model under curve #1 to the early data points on both vaccine coverage and disease incidence in Figure 1 ( t≤tfit ) , to see whether it can predict later data on vaccine coverage and disease incidence ( tfit>t ) . We fitted disease incidence and vaccine coverage simultaneously , by minimizing a weighted sum of the residual sum of squares ( RSS ) for vaccine coverage and the RSS for disease incidence . We also conducted a probabilistic sensitivity analysis ( PSA ) to assess the sensitivity of these predictions to parameter uncertainty . PSA defines plausible intervals for crucial model parameters and initial conditions . For each model realization , samples are drawn from statistical distributions based on those intervals and the model is fitted using those parameter values . Over many such realizations it is possible to see how sensitive the model predictions are to variations in the input parameters . A bootstrapping analysis was also performed to further test model sensitivity to input parameter uncertainties and to acquire confidence intervals . Details of fitting , PSA and bootstrapping appear in the Supporting Information ( Text S1 ) .
We analyzed both the whole cell pertussis vaccine scare and the MMR vaccine scare . For pertussis , the behavioral model with social learning and feedback fit the vaccine coverage data quite well under all risk evolution curves ( Figure 2 ) . In comparison , the two reduced models with feedback but no social learning , and social learning but no feedback , produced poor fits and were much less parsimonious ( Supporting Information Figure S2 ) . The third reduced model with neither social learning nor feedback also did worse in terms of fit and parsimony , except under risk evolution curve #5 ( Figure 2 ) . Thus , on the whole , adding social learning and feedback significantly improved model parsimony and fit . Results were very similar for MMR , with the behavioral model doing better in all cases except for the reduced model with neither social learning nor feedback under curve #5 ( Supporting Information Figure S3 ) . Confidence intervals and best-fitting parameter values appear in Supporting Information Tables S1 , S2 , S3 , S4 , S5 . We discuss the significance of the reduced model with neither social learning nor feedback under curve #5 in the Discussion section . We repeated the parsimony analysis using the full behavior-incidence model , finding some further improvement in fit and parsimony relative to the three reduced behavioral models . For pertussis , the behavior-incidence model again achieves a better AICc score in all cases except for the model with neither social learning nor feedback under curve #5 ( Supporting Information Figure S4 ) . In contrast , for the case of MMR , the behavior-incidence model under curve #1 becomes the most parsimonious of all 20 candidates . Interestingly , its best-fitting solution for vaccine coverage is almost indistinguishable from the data for most of the vaccine scare ( Figure 3; see Supporting Information Figures S5 for full results ) . By comparing the fit of the behavior-incidence model to the fit of the reduced model with neither social learning nor feedback , under curve #1 for MMR ( Figure 3 ) , we can see the effects of adding social learning and feedback to an underlying model of risk perception evolution: social learning delays the trough in vaccine coverage ( an effect that is also observed in the data , where vaccine coverage bottoms out many years after the hypothesized link between MMR vaccine and autism was published ) , and feedback allows the model to capture the undulations in vaccine coverage observed in the data , which appear to be caused by entrainment of vaccine coverage dynamics with disease dynamics . The results for the reduced model with feedback but no social learning are also telling ( Supporting Information Figure S4 ) : although the overall trend in vaccine coverage is tracked approximately , the predicted vaccine coverage is too irregular because without the inertial effects of an imitation dynamic , vaccine coverage responds too rapidly to slight changes in infection prevalence and the coupled behavior-incidence dynamics become unstable . In principle , a good fit could occur because the model is underdetermined: there are too many parameters for the amount of available data and thus the model is able to fit any arbitrary pattern by adjusting the parameter values appropriately . To rule out this possibility , we also fitted the model to randomly generated time series ( correlated white noise ) for the case of MMR . If the model were underdetermined , then the model should also be able to fit these arbitrary time series . In Supporting Information Figures S6 , S7 , S8 , S9 , S10 , S11 , S12 , S13 , S14 , S15 , S16 , S17 , we show that the model fits to these arbitrary time series are worse than its fit to the empirical vaccine coverage data in Figure 3 , and thus the model may not be underdetermined . In stage 2 we also evaluated the predictive power of the behavior-incidence model by fitting the model to the first part of vaccine coverage and disease incidence time series ( t≤tfit ) to see how well it predicts the second part ( t>tfit ) . For pertussis , the model has little predictive power in the first few years of the scare: the best-fitting solution fails to capture the long-term dynamics of either vaccine coverage or disease dynamics , and the sampled realizations of the PSA are likewise inaccurate and widely scattered ( tfit = 1973; Figure 4a , b ) . This situation remains unchanged through 1977 ( tfit = 1977 , Figure 4c , d ) . However , in 1978 , the first large incidence peak occurs , resulting in an abrupt increase in predictive power: now , the best-fitting solution predicts both future vaccine coverage and disease dynamics fairly well up until 1988 , and the sampled realizations of the PSA converge around future data points ( tfit = 1978; Figure 4e , f ) . Hence , the 1978 incidence peak acts to provide information that collapses model uncertainty , enabling reasonably accurate long-term predictions . This occurs despite the fact that—based on information available in 1978—it would not have been clear whether vaccine coverage had actually bottomed out or how quickly vaccine coverage would rebound . The model also qualitatively captures the subtle undulations in vaccine coverage between 1982 and 1987 that are superimposed on the longer-term trend ( Figure 4e ) : in both model and data , two incidence peaks occur during this time period , each followed shortly thereafter by a surge in vaccine coverage . However , the amplitude of the surges is larger in the model than in the data , and the first surge is predicted to occur a year before it actually happened . From the incidence plot ( Figure 4f ) we see that the model predicts the first incidence peak a year too soon , which is what causes the model to predict the first vaccine coverage a year too soon as well . This suggests that using a slightly more sophisticated transmission model might result in better alignment of predicted and observed vaccine coverage surges . These simulations highlight the fact that vaccine coverage in the data surges at exactly the time it should , if vaccine coverage were partly driven by disease dynamics . We also note that the ability of the model to track subtle undulations is responsible for much of the model's AICc score , especially in the case of MMR ( Figure 3 ) . From 1978 onward , model predictions are gradually refined and the vaccine coverage undulations become better aligned ( Figure 4g , h; see Supporting Information Figure S18 for all tfit values ) . However , even when tfit = 1988 and the whole time series is used to fit the model , it continues to over-predict the magnitude of the first incidence peak; this may be partly explained by under-reporting of pertussis incidence in the early years of the vaccine scare when misdiagnosis would have been more likely . The model also places the first incidence peak in 1975 , instead of 1974 when it actually occurred . In the years preceding the time window shown in Figure 4 , the modeled vaccine coverage is close to a steady state . The modeled vaccine coverage returns to this steady state after the scare is finished . This pattern is also observed in the vaccine coverage data . However , given that the whole cell pertussis vaccine was replaced with an acellular vaccine in the early 1990s , vaccine coverage data from this time period cannot be used to validate the model . The results are qualitatively similar under the bootstrapping analysis: the bootstrapped predictions change abruptly in 1978 , generating coherent and accurate predictions through 1988 ( Supporting Information Figure S19 ) . Using the whole time series to fit the model ( tfit = 1988 ) , from the bootstrapping analysis we estimate that σ = 27 ( 95% CI: 19 , 35 ) , corresponding to a 27-fold increase in the perceived vaccine risk at the start of the vaccine scare . Other confidence intervals and best-fitting parameter values appear in Supporting Information Table S6 . Predicting behavior-incidence dynamics during the MMR vaccine scare is more challenging . Vaccine coverage declined less . Measles did not become endemic until 2008 [1] , so there is a lower volume of lab-confirmed cases with which to parameterize the model , and no large epidemic outbreaks until later . As a result , measles dynamics are highly stochastic until 2008 , meaning that deterministic models such as the SIR model are less suited to describing this phase of the vaccine scare . Perhaps as a result of this , the model does not develop good predictive power until 2005 ( Figure 5; see Supporting Information Figure S20 for all tfit values ) . This appears to be stimulated by an unmistakable rebound of vaccine coverage , rather than by incidence peaks . Despite this limitation , by 2005 , the model predicts vaccine coverage in 2009 relatively well . It also captures qualitatively the subtle undulations caused by feedback—the sudden deceleration of coverage in 2006–2007 and the subsequent acceleration in 2008–2009 . Bootstrapping again yields similar results to PSA ( Supporting Information Figure S21 ) . Using the whole time series to fit the model ( tfit = 2009 ) , from the bootstrapping analysis we estimate that σ = 3 . 9 ( 95% CI: 3 . 1 , 4 . 6 ) , corresponding to a 4-fold increase in the perceived vaccine risk at the start of the vaccine scare . This value is much less than the 27-fold increase estimated for pertussis . For the delay δ , we estimate a biologically plausible value of 1 . 2 years ( 95% CI: 0 . 6 , 1 . 8 ) . The main effect of δ is to improve model fit by allowing peaks in the incidence data to stimulate correctly timed surges in the vaccine coverage data . If the delay is fixed at δ = 0 , the alignment becomes worse . Other confidence intervals and best-fitting parameter values appear in Supporting Information Table S7 . In both vaccine scares , the fit to vaccine coverage is better than the fit to disease incidence data . This occurs because individuals weigh both infection risks and vaccine risks in their vaccinating decisions ( Equation ( 6 ) ) , therefore vaccine coverage is determined both by disease incidence feedback and by the risk evolution curve . As a result , if the transmission model over-predicts incidence in some part of the time series , vaccine coverage can still be made to fit well by increasing the perception of vaccine risk during the same time period , such that an increase in the prevalence of infection is balanced by an increase in the perception of vaccine risk . For instance , in the first six years of the pertussis scare ( where the model over-predicts the size of the incidence peak relative to subsequent incidence peaks ) , this can be accomplished by increasing the value of σ such that perceived risk jumps more significantly at the start of the vaccine scare . For risk evolution curve #1 , this also elevates perceived vaccine risk later on in the time series , but not as much since vaccine risk tends to return to baseline over time and therefore the resulting incremental change in vaccine risk is smaller during the later years of the vaccine scare . Something similar can be said of MMR , which is why the timing of the incidence peaks appears to be more important for model fit than the relative size of incidence peaks .
Here we analyzed a relatively simple mathematical model of behavior-incidence dynamics . The model was based on evolutionary game theory , included both social learning and feedback of disease incidence on vaccinating behavior , and also included an exogenous description of how perceived vaccine risk evolves during a vaccine scare . We showed that the behavior-incidence model explains vaccine coverage data more parsimoniously than most reduced models with the same risk evolution curve but without social learning and/or feedback . More interestingly , in some circumstances , the behavior-incidence model can predict future vaccination coverage and disease incidence in a population where a vaccine scare has taken hold . These results suggest that strategic ( game theoretical ) interactions between individuals and social learning may be crucial governing mechanisms of the population response to a vaccine scare , in addition to changes in subjective vaccine risk perception . The models with both social learning and feedback ( both the behavioral model and the behavior-incidence model ) were significantly more parsimonious than most other candidates . The exception was the reduced model with neither social learning nor feedback under curve #5 , which did better in 3 of the 4 comparisons . In some sense , our experimental design “stacks the cards” against the behavior-incidence model: by adding a sufficient number of free parameters to the risk evolution curve it will always be possible to achieve an arbitrarily good AICc score without adding social learning or feedback ( see Supporting Information , Text S1 ) . At some point , enough parameters are added to allow a “naked” risk evolution curve to outperform the corresponding behavior-incidence model; in the current analysis that point was reached with curve #5 with its five free parameters . However , our risk evolution curves were intended to represent phenomenologically a broad range of potential competing models , and in practice it may not even be possible to construct a mechanistic risk evolution model that can track the data as closely as curve #5 does . For example , we note that a simple SIR-type rumor propagation model could not replicate the approximately linear decline and recovery in vaccine coverage seen in the case of pertussis . Considering these issues , it may not be appropriate to interpret our results in terms of a classical model selection exercise ( where the model with the best AICc score is adopted ) . Additionally , we have little idea of how perceived vaccine risk actually evolved during these vaccine scares and hence it is difficult to construct a mechanistic risk evolution model in the first place , which makes a true model comparison elusive . Because of the apparent difficulties in teasing out the effects of the inherent dynamics of a vaccine scare from those of social learning and feedback , we refrain from interpreting our results as a classical model selection exercise . Rather , we choose to emphasize that a theoretically motivated approach consistent with human behavior improves model fit with little or no parsimony penalty , even when the underlying risk evolution curve is very crude ( such as curves #1–#4 ) . Adding layers of sophistication to the model by including serious outcomes , combination versus single vaccines , age structure , spatial structure , or stochasticity may further improve the model's predictive power . These aspects represent opportunities for future work . Likewise , introducing a mechanistic model of how risk perception evolves instead of imposing risk evolution curves is worth pursuing , particularly in light of the interpretation caveats described in the previous paragraph . For example , this could take the form of a more mechanistic description of the impact of public health efforts such as information campaigns . However , the parsimony and predictive power of the model even without these extensions is considerable , and may be attributable to tight coupling between vaccinating behavior and disease incidence . This research illustrates the importance of choosing the right transmission model when constructing a behavior-incidence model . Whooping cough incidence during the whole cell pertussis vaccine scare entered the regime of deterministic dynamics ( widespread and unbroken chains of transmission ) , meaning that a simple , deterministic SIR model could capture the incidence peaks relatively well . However , measles incidence during the MMR scare was in a highly stochastic regime for most of the vaccine scare , which may explain the worse fit of the deterministic SIR model in that case . A significant model limitation is the necessity to choose a weight governing how much the overall goodness of fit is determined by model fit to vaccine coverage versus the model fit to disease incidence . In the case of MMR , the fit to disease incidence was not weighted very strongly , on account of the poor ability of the deterministic model to fit stochastic disease dynamics . When model fit to both incidence and vaccine coverage is good , then the choice of w should not matter . Otherwise , knowing which value of w to choose requires experimentation with the data and therefore forces use of large values of tfit , which means the predictive capacity of the model is less . Another model limitation is that , in the predictive analysis , the behavioral model is ‘trained’ on modeled incidence for t<tfit , rather than on actual incidence . This amounts to assuming that individuals were making vaccinating decisions based on modeled incidence , rather than on the incidence dynamics that the population actually experienced . One way to avoid this would be to fit the behavioral model to historical incidence data ( t≤tfit ) and then rely on modeled incidence data for projections into the future ( t>tfit ) . However , there are technical difficulties arising from the switch at t = tfit that would make this approach problematic . In particular , because of under-reporting in the empirical data , it would be easy to ‘confuse’ the behavioral model by switching its dependence from empirical incidence data to modeled incidence data at t = tfit . Moreover this model limitation is not a problem if agreement between modeled and empirical incidence is sufficiently close . Hence , ideally , it is better to train the behavioral model on modeled incidence for t<tfit . In any case , the issue of how to design good tests of the predictive ability of behavior-incidence models requires more thought . The model cannot predict when a vaccine scare will occur since this presumably depends on singular historical events , such as publication of a study linking a vaccine to health risks . The model also requires data from the first years of a vaccine scare to predict subsequent years . In our analysis , we fitted the parameter σ that determines how much the vaccine penalty jumps when the scare starts . The predictive power of the model could increase if σ were known from the start . This is possible in principle , since it could be estimated from population surveys after a vaccine scare begins . This also represents opportunity for future work . In 2003 , polio was on the verge of global eradication when a vaccine scare in northern Nigeria caused an international resurgence of the disease [26] . Our results suggest that vaccine scares or other forms of “free riding” could become more common as eradication goals are approached for more vaccine-preventable diseases . Behavior-incidence models may help mitigate the impact of vaccine scares , and assist in planning the global eradication endgame against some infectious diseases .
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“Herd immunity” is a phenomenon whereby an entire population—including unvaccinated individuals—can be protected from infection by vaccinating only a certain percentage of the population . This suggests that immunization programs can be victims of their own success: past vaccinations can drive disease incidence to such low levels that as-yet unvaccinated individuals feel no incentive to get vaccinated , which creates conditions for future outbreaks . “Behavior-incidence” models capture this interplay between disease dynamics and vaccinating behavior . However , the predictive and explanatory value of these models is rarely tested against empirical data , and it is not clear whether the implied strategic interaction between individuals drives vaccinating behavior in real populations . Here we develop a behavior-incidence model based on evolutionary game theory and social learning . We show it often explains vaccine coverage data during a vaccine scare better than most competing models without strategic interactions and/or social learning . It can also predict future vaccine coverage and disease incidence peaks to a significant extent . Thus , strategic interactions between individuals via herd immunity appear to be a significant driver of behavior during a vaccine scare . It may be possible to harness behavior-incidence models to predict how future vaccine scares might unfold and possibly also to mitigate them .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"infectious",
"diseases",
"mathematics",
"theoretical",
"biology",
"applied",
"mathematics",
"biology",
"population",
"biology"
] |
2012
|
Evolutionary Game Theory and Social Learning Can Determine How Vaccine Scares Unfold
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Rheumatic heart disease ( RHD ) is considered a major public health problem in developing countries , although scarce data are available to substantiate this . Here we quantify mortality from RHD in Fiji during 2008–2012 in people aged 5–69 years . Using 1 , 773 , 999 records derived from multiple sources of routine clinical and administrative data , we used probabilistic record-linkage to define a cohort of 2 , 619 persons diagnosed with RHD , observed for all-cause mortality over 11 , 538 person-years . Using relative survival methods , we estimated there were 378 RHD-attributable deaths , almost half of which occurred before age 40 years . Using census data as the denominator , we calculated there were 9 . 9 deaths ( 95% CI 9 . 8–10 . 0 ) and 331 years of life-lost ( YLL , 95% CI 330 . 4–331 . 5 ) due to RHD per 100 , 000 person-years , standardised to the portion of the WHO World Standard Population aged 0–69 years . Valuing life using Fiji’s per-capita gross domestic product , we estimated these deaths cost United States Dollar $6 , 077 , 431 annually . Compared to vital registration data for 2011–2012 , we calculated there were 1 . 6-times more RHD-attributable deaths than the number reported , and found our estimate of RHD mortality exceeded all but the five leading reported causes of premature death , based on collapsed underlying cause-of-death diagnoses . Rheumatic heart disease is a leading cause of premature death as well as an important economic burden in this setting . Age-standardised death rates are more than twice those reported in current global estimates . Linkage of routine data provides an efficient tool to better define the epidemiology of neglected diseases .
Rheumatic heart disease ( RHD ) is the chronic consequence of an aberrant immune response to infection by the bacterial pathogen Streptococcus pyogenes that results in permanent scarring of the heart valves . [1] This process , which may manifest clinically as heart failure , stroke and early death , [2] remains a major public health problem in developing countries . [3–5] Despite this , efforts to measure the disease burden and institute control strategies are impeded by the lack of up-to-date epidemiologic data from endemic areas . [4 , 6 , 7] Although current global estimates assert there are approximately 275 , 000 deaths due to RHD each year , [8] deriving such figures has been problematic . [4 , 7] In 2005 , a WHO report found mortality estimates based on either vital registration data or verbal autopsy techniques to be unreliable , largely because of the difficulty distinguishing RHD-attributable death from other causes of cardiac death . [9] To remedy this , the authors extrapolated from estimates of prevalence and studies of natural history . However , with few current data [10] , the only available data were those from urban populations living in the UK , USA and Japan in the early to mid-twentieth century and socially disadvantaged indigenous populations living in Australia and New Zealand today . [9] We therefore sought to measure RHD-attributable mortality in Fiji , a developing nation in the Western Pacific , where a high prevalence of RHD has consistently been reported . [11 , 12]
We established a new national and historical cohort of RHD patients in Fiji by probabilistic record-linkage , using diagnostic information and outcome events ascertained from routine clinical and administrative records for the period 2008–2012 ( Fig 1 ) . We used relative survival methods to estimate and examine RHD-attributable deaths in persons aged 5–69 years . [13] We then used census data to calculate crude , age-specific and age-standardised RHD-attributable death rates for the wider population , as well as years of life lost ( YLL ) from which we estimate the cost to the economy . Finally , we compared our RHD mortality estimate with vital registration data for 2011–2012 . Fiji is an independent nation located in the Western Pacific with an estimated population of 837 , 271 at the most recent census in 2007 . [14] The population consists of two major ethnic groups , Indigenous iTaukei Fijians ( 57% ) and Fijians of Indian Descent ( 38% ) . Fiji is ranked 96th of 186 nations for the composite human development index . [15] The routine data used in this study was obtained from four sources: an electronic patient information system , a database of death certificates , a disease control register , and echocardiography clinic registers . Information that referred to the same person was amalgamated from the four datasets using identifier fields . [19] We designed and calibrated a probabilistic record-linkage procedure using 1 , 406 known duplications in the patient information system from which we calculated the sensitivity and specificity ( S1 Box ) . In its final configuration , our procedure identified the known duplications with sensitivity of 91 . 4% and specificity of 99 . 9% with record pairs considered a match if they achieved a posterior probability of over 50% . Stata® code for the procedure is available for download at: http://users . ox . ac . uk/~clme1250/data_linkage/linkage . html . Our first step was to find at least one match in the patient information system for each record available from the death certificate database , the control programme register and the echocardiography clinic registers . In the absence of a universal identifier , we used a variety of identifier fields in the records including names , dates and demographics to detect pairs of records that referred to the same person ( S2 Table ) . After cleaning and standardisation of names , dates and other identifier fields , we performed an initial shortlisting step ( termed ‘blocking’ ) by finding groups of individuals of similar age with similar names . Next we compared identifiers within each pair or shortlisted records and classified them as being in agreement , partial agreement , disagreement or missing . [20] These classifications allowed for out-of-order names and dates as well as abbreviations and spelling discrepancies of names . [21] We then undertook a further blocking step using combinations of the identifier classifications to define smaller blocks that were expected to contain 5–75% true matches based on the number of pairs per search record . Next , for each block , we estimated the likelihood that each combination or pattern of classifications indicated a true match under the Fellegi-Sunder model of record-linkage; [22] match and nonmatch probabilities were estimated using an expectation maximisation algorithm as previously described . [23] Finally , we obtained a posterior probability of a match by multiplying the raw likelihood by an estimate of prior probability obtained from the product of: 1 ) The probability that a random pair represented a match , which equated to the reciprocal of the size of the final block in which that pair was found [20]; 2 ) An estimate of the probability that a given individual had actually been registered in the patient information system [20] , based on their locality of residence , age , gender and ethnicity; and 3 ) For pairings with death certificates only , the probability an individual had died that year based on their age , gender and ethnicity . [24] Record pairs achieving a posterior probability of 50% or more in at least one block were consider a match . Once links had been identified , we repeated the procedure to confirm or refute the merger of two or more records in the patient information system . Because it was impractical to search for duplicates across the entire patient information system , we limited this search for duplicates to a shortlist of records that were potentially relevant because of a possible match or because they contained useful clinical information such as a relevant admission diagnosis . Finally we pulled clinical information from each of the sources into a single linked record and deleted identifiers . If there was a discrepancy between the records , the patient information system was assumed to be correct unless that field was missing . Where there were discrepancies amongst two or more patient information system records these fields were set to missing . We focused on 2008–2012 because the most complete data were available for this period . The study was restricted to people aged 5–69 years because RHD is expected to cause very few deaths before five years of age and cause-of-death information can be unreliable in old age . Based on diagnostic information in control programme records , echocardiographic data , hospital discharges and death certificates , any individual with follow-up at ages 5–69 years who had a least one record of a diagnosis of either RHD or its precursor acute rheumatic fever ( ARF ) was eligible for inclusion ( S2 Box ) . We assumed the onset of disease was in childhood irrespective of the date the individual became known to clinical services , circumventing potential bias due to late presentation . The primary outcome was the time to all-cause death defined by a date of death in either the patient information system or the death certificate database . The secondary outcomes were cause-specific death defined: 1 ) Narrowly , where the underlying cause-of-death was ascribed to an International Statistical Classification of Diseases and Related Health Problems 10th Revision ( ICD10 ) code pertaining to RHD or ARF; and 2 ) Broadly , where the death certificate listed an ICD10 code pertaining to either RHD , an alternative description of valvular heart disease , or a cardiac complication thereof ( i . e . heart failure , stroke , infective endocarditis , arrhythmia ) as an immediate or underlying cause-of-death , without listing ischaemic heart disease . We surmised there was no loss to follow-up because the linked patient information system and the death certificate database would include most deaths in the country during 2008–2012 . Deaths in the cohort that went undetected due to , for example , emigration , would have led us to under-estimate RHD-attributable mortality . Data were inspected for missing and outlying points , categorical variables were tabulated and continuous variables were summarised in histograms . The cohort data were expanded into thirteen five-year age categories and standardised mortality ratios ( SMR ) and relative survival rates were calculated by applying the background age category , gender and ethnicity specific death rates to the cohort . [13] Background death rates were calculated using the count of deaths in the patient information system and death certificates after duplicates had been removed divided by estimates of population using data from the Fiji Bureau of Statistics . [14] Before application these rates were checked for adherence with expectations by performing of standard quality control checks such as log rate versus age category . We assumed time-to-death from RHD was independent of time-to-death from other causes , consistent with the previous application of relative survival to RHD mortality , [25] but as an alternative performed competing risk analyses based on broad and narrow definitions for cause-specific death . Using Poisson regression , we modelled the relative risk conferred by decade of age , gender and ethnicity , adjusting for calendar year of exit to account for artifactual differences in the background death rates during the five year study . We used excess mortality in the cohort to investigate RHD-attributable mortality before age 70 years in the wider population under the assumptions that all RHD patients in the country had been detected and no RHD deaths occurred before age 5 years . We calculated crude , age-specific and age-standardised RHD-attributable death rates for the general population , deriving 95% confidence intervals ( CI ) using Poisson , and using estimates of population from the Fiji Bureau of Statistics as the denominator . We tested the robustness of these results by changing the stringency of the record-linkage procedure through adjusting the posterior probability at which record pairs were considered to match . We also calculated YLL in each of the thirteen age categories using WHO Life Tables [26] and calculated rates for the wider population . We used the WHO World Standard population for direct standardisation . [27] Finally , the availability of ICD10 coded underlying cause-of-death classifications during 2011–2012 permitted comparison of our estimates with the number of reported deaths due to RHD itself and other conditions . To make the comparison , we collapsed ICD10 codes into the diagnostic categories used in the Global Burden of Disease ( GBD ) project , [28] grouping ill-defined codes separately . [29] To maximise parity we recalculated the number of RHD-attributable deaths based only on deaths associated with a death certificate . We used a human capital approach [30] to define the cost of premature mortality to Fiji . We estimate wider cost of illness due to RHD/ARF in Fiji in a separate paper , providing further details and justification for the methods . [31] We estimated the cost of a death in each of the five-year age categories by multiplying life-expectancy in years using Fiji’s per-capita gross domestic product for the year in which the death occurred as estimated by the World Bank [32] discounting 3% each year . For each category and year of the study , we then multiplied this estimate by the excess deaths and summed the results to obtain the total cost of RHD-attributable deaths over the five-year period . We searched for population-based studies of RHD mortality published in a thirty year period , 1985–2014 , in Ovid Medline , Embase and Global Health ( S4 Fig ) . We used the search terms “rheumatic heart disease” and “mortality” with a previously described filter to detect studies from developing countries . [33] We excluded case reports , case series , studies focused on valve surgery and studies not specific to RHD . Permission for the study was granted by the Fiji National Research Ethics Review Committee ( 2013–89 ) in addition to the Oxford Tropical Research Ethics Review Committee ( 1055–13 ) . Once the record-linkage procedures were complete , all data analysed were anonymised .
In total , 1 , 773 , 999 records were available , including 34 , 773 records that pertained to a death . Links were identified in the patient information system for 87 . 1% of control programme records , 85 . 3% of echocardiography clinic records and 66 . 0% of death certificates ( S3 Table ) . After selecting eligible individuals with an RHD or ARF diagnosis , a cohort of 2 , 619 individuals remained for analysis ( Fig 2 ) . Of these , 1 , 038 ( 39 . 6% ) were present in more than one database ( S1 Fig ) . Characteristics are summarised in Table 1; the person-time observed totalled 11 , 537 . 5 person-years . During follow-up , 430 of the 2 , 619 ( 16 . 4% ) RHD/ARF patients were linked to a death in the patient information system , the death certificate database or both . This equated to 2 . 1% of the 20 , 796 deaths in the general population in the same age bracket during this time . The all-cause unadjusted death rate amongst RHD/ARF patients was 3 . 7% per year ( 95% CI 3 . 4–4 . 1% ) . Death rates based on cause-of-death information are summarised in S6 Table . From late childhood onwards , the death rate observed in the cohort exceeded that in the wider population ( SMR 8 . 3 , 95% CI 7 . 5–9 . 0 , S2 Fig ) . The relative survival was 96 . 9% ( 95% CI 96 . 1–97 . 5% ) at one year and 81 . 2% ( 95% CI 79 . 2–83 . 0% ) at five years ( S3 Fig ) . The risk of death among RHD/ARF patients increased with age over and above background rates; there was also increased risk for both male and iTaukei patients ( S4 Table ) . Based on the 378 excess deaths , of which 177 ( 46 . 8% ) occurred before age 40 years , we estimated there were 9 . 1 RHD-attributable deaths ( 95% CI 8 . 2–10 . 1 ) per 100 , 000 person-years in those aged 0–69 years . This estimate remained stable to adjustments in the record-linkage threshold and between the 2008–2010 and 2011–2012 components of the dataset ( S7 Table ) . Age-specific rates of RHD-attributable death increased throughout life ( Fig 3A ) . Standardised to the portion of the WHO World Standard Population aged 0–69 years , our primary estimate equates to 9 . 9 deaths ( 95% CI 9 . 8–10 . 0 ) per 100 , 000 person-years ( S5 Table ) . Additionally , we estimated 323 . 3 YLL ( 95% CI 317 . 9–328 . 6 ) per 100 , 000 person-years , equating to a WHO standardised rate of 331 . 0 YLL ( 95% CI 330 . 4–331 . 5 ) per 100 , 000 person-years ( S5 Table ) . Age-specific RHD-attributable YLL rates were elevated from late childhood onwards ( Fig 3B ) . The cost of these deaths for the five-year period was current Fiji Dollar $58 , 810 , 903 , which at mid-market rates equates to United States Dollar $30 , 387 , 153 . Finally , we estimated there were 132 RHD-attributable deaths during 2011–2012 based only on deaths associated with a death certificate , compared to 81 RHD deaths reported in vital registration data ( Fig 4 ) . Only five other conditions caused more than 132 deaths in the wider population while ten caused more than 81 deaths ( S8 Table ) . Moreover , only drowning caused more than the 40 deaths attributable to RHD at ages 5–29 years , which was greater than the number attributed to suicide and road injury , both well-recognised causes of death in young people .
These are the first national population-based age-standardised estimates of mortality due to RHD in a developing country ( S4 Fig ) , and confirm that RHD is an important cause of premature death in Fiji leading to a substantial loss of life and economic productivity . The study was made possible by applying record-linkage techniques to the routine clinical and administrative data that are increasingly available in an electronic format in many developing countries . The results are robust to changes in record-linkage thresholds and remain broadly similar throughout the five years the study covers , despite changes in local death reporting practices during this time . Using the background death rates in the general population , we were able to estimate RHD-attributable mortality by measuring excess mortality . These methods , which are widely used for population-based cancer survival analyses [13] , are highly applicable to RHD , a disease for which cause-of-death information is often absent or unreliable . [6 , 25] For example , if our results are compared to underlying cause-of-death classifications in vital registration data for 2011–2012 , we find 1 . 6-fold more RHD-attributable deaths at ages 5–69 years , a discrepancy that peaked at 3 . 8-fold in the 30–49 years age group ( Fig 4 ) . This finding is consistent with a recent study of mortality amongst RHD patients in Western Australia which , by reviewing death certificates and other clinical data , concluded a third of RHD-attributable deaths were ascribed to other underlying cause-of-death diagnoses . [34] Few existing data are available for comparison . One recently published study presents RHD-attributable death rates based on vital statistics for South Africa for the period 1997–2012 during which time the crude all-age death rate declined from 1 . 2 to 0 . 7 per 100 , 000 person-years . [35] As the authors acknowledge , however , the reliability of estimates based on death certification is questionable [35] , particularly given that in 1999 both the UK and Japan reported RHD death rates over two-fold higher than this . [9] Interestingly , our age-standardised and age-specific rates are more comparable to those made for the Coloured population in South Africa for 1978–1982 , the authors of that report estimating an age-standardised rate of 3 . 5 and 4 . 2 per 100 , 000 for men and women respectively . [36] Moreover , our age-standardised estimate is similar to that reported for Indigenous populations in New Zealand [37] and Alaska [38] during the 1970–1980s although it exceeds death rates reported from New Zealand [39] and Australia [25] more recently . Alternatively , we can compare our results to the estimates made by the GBD project ( Fig 3 ) . [28] Directly standardised to the population aged 0–69 years , we report higher death and YLL rates than GBD ( S6 Table ) , the latter amounting to a 2 . 6-fold difference in the death rate and 2 . 4 in the YLL rate compared to the GBD’s developing countries estimate . Thus our data not only have important implications for the Pacific region but also , if generalisable to other developing countries , for global summary estimates . Although the results appear reasonable , they have some limitations . First , there are potential shortcomings to using background death rates to estimate exposure-attributable mortality . If we were wrong to assume time-to-death from RHD was independent of time-to-death from other causes , we may have over-estimated RHD mortality; however , the impact would be small and alternatives such as cause-specific survival remain unsatisfactory . [13] Second , the cohort was heterogeneous with respect to the chronicity and severity of the illness , and there was no means to distinguish new onset from relapses of chronic disease . This led us to make the conservative assumption that participants were at risk from childhood onwards , which would lead to an under-estimate of mortality if the true onset was later . Third , the study was retrospective and relied on routine clinical and administrative data , which are likely to have contained errors . In particular , while death certificate submission in Fiji is relatively complete [17 , 18] , data pertaining to underlying cause-of-death should be interpreted with some caution; [18] our comparison with other causes of death may slightly exaggerate the disease’s importance . Fourth , these data provide neither sufficient detail nor follow-up to answer important outstanding questions about why such a burden of disease exists in this setting . For example , the discrepancy between the two largest ethnic groups remains unexplained , although a number of cultural , socioeconomic , geographic and potentially biological factors may contribute . Finally , we are unable to report on RHD-attributable deaths beyond age 69 years although we note GBD estimated a fifth of global RHD deaths occurred in this age group . By illustrating the high burden of premature death due to RHD in Fiji , these data help substantiate the assertion that RHD remains , on a global scale , [6] an important cause of mortality . By using record-linkage techniques , we have demonstrated that routine clinical and administrative data can be used to quantify the impact of RHD in developing countries , a finding which has important implications for both research and disease control .
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Rheumatic heart disease is the result of an abnormal immune response to the bacteria Streptococcus pyogenes . The disease causes permanent scarring of the heart values , which results in heart failure , stroke and early death . It primarily affects the world’s poorest and most disadvantaged populations and despite the availability of cheap and effective prevention strategies receives little attention from policy-makers and funders . One of the major difficulties has been measuring how many people die prematurely from this disease . Simply counting up deaths is highly inaccurate and so an alternate strategy was needed . Focusing on Fiji in the Western Pacific , we pulled together information from several different health databases using a process called record-linkage . We then worked out how much more frequently rheumatic heart disease patients die than you might expect when comparing them to persons of similar age , gender and ethnicity in the general population . From these data we estimate about twice as many patients were dying from the disease than had been previously suggested . Most of these deaths occurred earlier than was thought with substantial knock-on effects for the economy . On balance we think this strategy for measuring mortality is useful and robust , and it will be increasingly possible to employ it elsewhere .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
|
Rheumatic Heart Disease-Attributable Mortality at Ages 5–69 Years in Fiji: A Five-Year, National, Population-Based Record-Linkage Cohort Study
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Identifying the ancestry of chromosomal segments of distinct ancestry has a wide range of applications from disease mapping to learning about history . Most methods require the use of unlinked markers; but , using all markers from genome-wide scanning arrays , it should in principle be possible to infer the ancestry of even very small segments with exquisite accuracy . We describe a method , HAPMIX , which employs an explicit population genetic model to perform such local ancestry inference based on fine-scale variation data . We show that HAPMIX outperforms other methods , and we explore its utility for inferring ancestry , learning about ancestral populations , and inferring dates of admixture . We validate the method empirically by applying it to populations that have experienced recent and ancient admixture: 935 African Americans from the United States and 29 Mozabites from North Africa . HAPMIX will be of particular utility for mapping disease genes in recently admixed populations , as its accurate estimates of local ancestry permit admixture and case-control association signals to be combined , enabling more powerful tests of association than with either signal alone .
The identification of chromosomal segments of distinct continental ancestry in admixed populations is an important problem , with a wide range of applications from disease mapping to understanding human history . Early efforts to solve this problem used coarse sets of unlinked markers [1]–[3] and mostly focused on populations such as African Americans [4] , [5] and Latinos [6]–[8] that admixed within the past approximately 10 generations . Applying this approach to more anciently admixed populations has led to ancestry predictions that are ambiguous at many loci [9] . However , methods based on coarse sets of markers do not take advantage of the much richer haplotype information available in genome-wide data . More recent methods have been designed to use data from genome-wide scanning arrays [10]–[12] , but these methods do not fully model linkage disequilibrium ( LD ) in the ancestral populations . Thus , they do not capture all of the available information about ancestry , and can be far from optimal . Furthermore , unless a trimming step is applied to remove linked markers [11] , unmodeled LD may cause systematic biases in estimated ancestry , leading to false-positive inferences of a deviation in ancestry at certain loci [13] . Here , we describe a haplotype-based method , HAPMIX , which applies an extension of the population genetic model of Li and Stephens [14] to the problem of local ancestry inference in populations formed by two way admixture . We apply the method to simulated mixtures of African and European chromosomes to show that the resulting local ancestry inference is exceedingly accurate in comparison to other methods , even in the case of ancient admixture in which the shorter ancestry segments are more difficult to infer . As expected from its use of an explicit population genetic model , HAPMIX makes more complete use of dense genome-wide data , producing more accurate results . We examine the sensitivity of local ancestry inference to a wide array of factors . We also explore the utility of HAPMIX for drawing inferences about both the ancestral populations and the date of admixture . We apply HAPMIX to 935 African American individuals genotyped at ∼650 , 000 markers . By studying a large set of individuals from an admixed population of high relevance to disease mapping , we validate the effectiveness of this method in a practical setting and specifically show that the ancestry estimates are not systematically biased within the limits of our resolution . To illustrate how the method can provide insights into the history of an anciently admixed population , we also apply HAPMIX to a data set of 29 individuals from the Mozabite population of northern Africa that were genotyped at ∼650 , 000 markers as part of the Human Genome Diversity Panel ( HGDP ) [15] . We show that the Mozabite have inherited roughly 78% ancestry from a European-related population and 22% ancestry from a population related to sub-Saharan Africans . Our analysis also shows that the Mozabite admixture has occurred over a period that began at least 100 generations ago ( ∼2 , 800 years ago ) , and that has continued into the present day . We are able to infer small , ancient , ancestry segments in the Mozabite , and we demonstrate that the segments show considerable drift relative to all the other HGDP populations , consistent with the historical isolation of the Mozabite population .
For the African American data , informed consent was obtained from each study participant , and the study protocol was approved by the institutional review board at either the Johns Hopkins University or Howard University . HAPMIX assumes that the admixed population being analyzed has arisen from the admixture of two ancestral populations , and that phased data are available from unadmixed reference populations that are closely related to the true ancestral populations ( e . g . phased data from HapMap [16] ) . In theory , discrepancies between the reference populations and the true ancestral populations may lead to inaccuracies , but in practice HAPMIX is robust to this concern under a variety of realistic scenarios ( see below ) . The central idea of the method is to view haplotypes of each admixed individual as being sampled from the reference populations: for example , haplotypes of an African American individual could be sampled from phased African and European chromosomes from HapMap . At each position in the genome , HAPMIX estimates the likelihood that a haplotype from an admixed individual is a better statistical match to one reference population or the other . A Hidden Markov Model ( HMM ) is used to combine these likelihoods with information from neighboring loci , to provide a probabilistic estimate of ancestry at each locus . The method allows transition at two scales . The small-scale transitions are between haplotypes from within a reference population , typically at a scale of every few tens of thousands of bases [14] . The large-scale transitions are between the reference populations , at a scale of up to tens of millions of bases for a recently admixed population such as African Americans . Figure 1 illustrates the method schematically . An important strength of HAPMIX is the way it analyzes diploid data from admixed individuals . A naïve way to use population genetic methods to infer ancestry would be to pre-process such a data set using phasing software , and then to assume that this guess about the underlying phased haplotype is correct . However , phase switch errors that arise from this procedure ( which are common even with the best phasing algorithms [17] , [18] ) would inappropriately force the method to infer ancestry transitions . HAPMIX circumvents this problem by not assuming that any one haplotype phase solution is correct . Instead , it uses a built-in phasing algorithm , similar to that of [17] , which allows it to average inferences about ancestry over all possible phase solutions within each admixed individual . We treat the reference populations as fully phased , partly because in some cases , e . g . African and European chromosomes from HapMap , this phasing uses unambiguous trio information and is therefore highly accurate . More importantly , we expect our approach to be robust to errors in phasing in the reference populations , because these are unlikely to force inappropriate ancestry switches , in contrast to phasing errors in the admixed data itself . HAPMIX is also notable in inferring probabilities for whether an individual has 0 , 1 , or 2 alleles of a particular ancestry at each locus . As our simulations show , these estimates are well-calibrated . Thus , when the method generates a probability p for an individual being heterozygous for ancestry at a locus , they are in fact heterozygous approximately this proportion of the time . A well-calibrated probability of ancestry at each locus is important for a variety of applications , and also allows us to evaluate the robustness of the results . HAPMIX is fundamentally different from existing methods such as ANCESTRYMAP and LAMP [1] , [11] . ANCESTRYMAP applies a Hidden Markov Model to unlinked SNPs to model ancestry transitions , while LAMP computes a majority vote of ancestry information using windows of unlinked SNPs , but neither of those methods makes use of haplotype information . Another method for investigating admixture segments , HAPAA , has recently been published [19] . In common with HAPMIX , the HAPAA software uses a Hidden Markov Model to model linkage disequilibrium within populations , and infers ancestry segments . However , there are also a number of important differences between our model and that used by HAPAA . First , unlike HAPAA , we allow for some rate of miscopying of ancestry segments from the “wrong” population , which we have found greatly improves our ancestry estimation ( instead of this , the HAPAA software uses a post-hoc “filtering” of inferred segments , which removes all segments of size below a certain minimum threshold ) . Second , we fully allow for unphased data in our model , while the HAPAA approach requires a prior phasing of the data , and then attempts to account for the effect of phase-flip errors on ancestry inference via a heuristic procedure . We believe that these features of HAPMIX are likely to be critical in unraveling older admixture events , where ancestry segments are much shorter . A final advantage of HAPMIX over HAPAA is that it is designed to produce accurate estimates of uncertainty in inferred segments , even for old admixture events . We used HAPMIX to analyze 935 African American samples collected from volunteers living in the Baltimore–Washington , D . C . metropolitan region and genotyped on the Illumina 650Y chip as part of an asthma study . All subjects gave verbal and written consent . The Johns Hopkins and Howard University Institutional Review Boards ( IRBs ) determined that the samples were consented for genetic research , but not for public release of genotype data . Roughly half of these samples were asthma cases and half were non-asthmatic controls , but all phenotypic information was ignored in the current study ( disease mapping analyses of these data will be described elsewhere; K . Barnes et al . , unpublished data ) . We note that irrespective of whether asthmatic cases considered separately exhibit an admixture association signal , one would not expect to observe such a signal in a combined analysis of all 935 samples ignoring phenotypic information , due to dilution of the signal . The analyses were restricted to 510 , 324 autosomal markers which passed quality controls in the 935 African Americans and were polymorphic in phased YRI and phased CEU data from HapMap . We ran HAPMIX using YRI and CEU as input reference populations , setting μ1 = 20% and running at various values of T to infer the date of admixture ( see above ) . For comparison purposes , we also ran the ANCESTRYMAP and LAMP-ANC programs on this data , in each case restricting all input data to a subset of markers that were unlinked in the reference populations , as described above . To draw inferences about the ancestral populations of African Americans , we ran HAPMIX in the mode that samples random paths to reconstruct chromosomal segments from the ancestral populations ( see above ) , and used the resulting allele counts to compute FST values between the inferred ancestral segments and the reference populations ( YRI and CEU ) , as well as additional populations genotyped as part of the HGDP . To estimate the number of ancestry segment changes in each of the 935 African American individuals , we inferred ancestry using the most likely state at each site , and identified ancestry transitions from these ancestry states , assuming zero changes between pairs of SNPs with identical ancestry states . To produce an estimator of the number of generations since admixture for each individual with >20 ancestry segments , we note that the genetic map used as input to the software has total length 35 . 5 Morgans . For an individual with admixture proportion α , we expect to observe a fraction 2α ( 1-α ) of all recombination events occurring since admixture ( i . e . those that result in a change in ancestry ) . Given λ generations since admixture , we therefore expect to see a total of 142 λ α ( 1-α ) events in a diploid individual . Estimating α using the observed genome-wide ancestry proportion μ for that individual , if N ancestry transitions are observed , then a natural moment estimator of the number of generations since admixture is We excluded 3 clear outlier individuals who had more than 20 inferred generations of admixture , because we believe this is likely to indicate partial ancestry from a third source population in these individuals . We analyzed 29 Mozabite samples from the HGDP data set . A total of 30 Mozabite individuals were originally genotyped as part of the HGDP , but one individual ( HGDP01281 ) was excluded due to cryptic relatedness . We ran HAPMIX on the 29 Mozabite individuals using YRI and CEU as the input reference populations . We inferred the number of generations since admixture that provided the best fit to the data , and computed FST values between the inferred ancestral segments and the reference populations ( YRI and CEU ) , as described above for the African American data set . We ran HAPMIX on a total of 13 populations from the HGDP data that were of African , European , or Middle Eastern ancestry . For each population , we used YRI and CEU as the input reference populations , and estimated the European-related mixture proportion . For populations with European-related ancestry that was estimated to be more than 0% and less than 100% , we also estimated the number of generations since mixture . The HAPMIX software is available for downloading at the following URL: http://www . stats . ox . ac . uk/~myers/software . html .
We ran HAPMIX on 935 African American samples to obtain local ancestry estimates at each location in the genome ( see Materials and Methods ) . Although the true number of European copies at each locus is unknown , the probabilities produced by HAPMIX provide an estimate of the squared correlation between predicted and true number of European copies ( see Materials and Methods ) . Our estimate was r2 = 0 . 98 , which implies that HAPMIX can provide close to full power for admixture mapping of disease genes in African Americans . We also ran the ANCESTRYMAP and LAMP-ANC programs on these data [1] , [11] ( see Materials and Methods ) . Discernment of ancestry transitions was much sharper for HAPMIX compared to the other methods , as seen in a plot of number of European copies predicted by each method for an African American sample on chromosome 1 ( Figure 2B ) . This is expected from our results on simulated data ( Figure 2A ) . In addition to verifying that predictions are accurate on average , it is also important to check that there are no regions of the genome showing systematically inaccurate ancestry predictions . Such regions could produce spurious signals of selection after admixture in scans of control individuals , or spurious admixture association signals in scans of disease cases [13] . Because such scans examine the tail of the observed distribution , even a single region where results are biased could be a serious confounder . With this in mind , we computed the average ancestry across all samples for each locus in the genome , as predicted by either HAPMIX or ANCESTRYMAP , and then searched for unusual deviations . HAPMIX estimates ranged between 16% and 22% European ancestry , and ANCESTRYMAP estimates ranged between 16% and 21% , with a mean of 19% for both methods . These small deviations from the mean are not statistically significant ( nominal P-value = 0 . 001 for the most extreme value over hundreds of independent loci ) and can be attributed to sampling variation in the individuals analyzed . We used HAPMIX to estimate the value of λ ( the number of generations since admixture ) that provided the best fit to the African American data set by computing likelihoods at different values of T ( see Materials and Methods ) . We obtained an estimate of λ = 7 , which matches the value of λ = 7 . 0 inferred by ANCESTRYMAP on the same data , and is similar to the value of λ = 6 . 3 previously inferred by ANCESTRYMAP on other African American data sets [4] . We also used inferred segments of African or European ancestry to estimate FST values between the true ancestral populations of African Americans and the two reference populations used here ( YRI and CEU , as well as African and European populations from the HGDP ) ( see Materials and Methods ) . We obtained estimates of 0 . 001 for the FST between the true African ancestral population and YRI , and 0 . 001 for the FST between the true European ancestral population and CEU . This is consistent with estimates of FST = 0 . 001 derived from the τ parameter inferred by ANCESTRYMAP on the same data ( FST = 0 . 5/τ ) , and consistent with our previous findings that YRI and CEU provide accurate reference populations for admixture analysis of African Americans [4] , [25] . Correspondingly , among the HGDP populations the lowest FST to the true African ancestral population was obtained for the Yoruba population ( FST = 0 . 0008 ) . The Bantu South African , Mandenka and Bantu Kenya groups had the next lowest values ( FST<0 . 007 ) , and all other African populations showed FST>0 . 035 . This supports a West African origin for the African ancestry segments in African Americans , in agreement with historical records . For the European ancestral population , the lowest FST was with French ( FST = 0 . 0013 ) with Italian , Orcadian , Tuscan , Russian , Basque and Adygei then showing increasing values , but FST<0 . 01 in all cases . This is supportive of a North-West European origin for the majority of the European segments , again agreeing with historical records . We sought to investigate whether our precise ancestry inference revealed a correlation between time since admixture and ancestry proportion across individuals . For each individual separately , we estimated a time since admixture ( Materials and Methods ) . The mean estimated time across individuals was 6 . 62 generations , in close agreement with our λ = 7 estimate . However , different individuals showed admixture time estimates ranging from 1 . 25 generations to 13 generations . Plotting ancestry proportions against these time estimates revealed a striking trend ( Figure 5 ) , whereby those individuals carrying higher levels of European ancestry clearly show more recent estimated admixture times . Because individuals with the lowest proportion of European admixture have an estimate admixture time of ∼10 generations , these results demonstrate continuing admixture between Europeans and African Americans over at least 10 generations . Our estimation of admixture time involves rescaling the raw count of ancestry switches , according to the fraction of recombination events since admixture expected to lead to ancestry switches , in a manner dependent on the overall ancestry proportion in the genome ( Materials and Methods ) . We note that individuals with 30%–50% African ancestry show unscaled ancestry switch counts much smaller than for those individuals with 50%–70% African ancestry ( p = 0 . 0007 by Wilcoxon rank sum test ) , despite the fact that in both groups we expect to observe the same proportion , 42%–50% , of all recombination events , ruling out the idea that the observed trend is simply a consequence of the rescaling . We analyzed 29 HGDP samples from the Mozabite population of North Africa , which has previously been reported to inherit a mixture of both European-related ancestry and ancestry related to sub-Saharan Africans [15] , [26] ( see Materials and Methods ) . We therefore continued to use YRI and CEU as input reference populations , to identify segments of sub-Saharan African-related ancestry , and European-related segments . Our analysis aimed to shed light on the origins of the admixing populations , as well as the period in which historical admixture occurred . Runs at a wide range of input μ1 values all indicated approximately 80% European-related ancestry on average , and thus we fixed the input μ1 parameter at 80% and ran HAPMIX using a range of input T values . The highest likelihood was obtained at T = 100 generations . In this run , the average % European-related ancestry of all samples was equal to 78% and the estimated r2 between predicted and true number of European copies was 0 . 79 , which is identical to the value we observed in our λ = 100 simulations using inaccurate reference populations . We further investigated whether local ancestry inference in Mozabite samples matches our expectations from simulated data by simulating an anciently admixed sample with admixture parameters chosen to be similar to Mozabite . Specifically , we assumed 80% European and 20% African ancestry ( French and Yoruba from HGDP ) and 100 generations since admixture . HAPMIX results on chromosome 1 , along with true ancestry , are displayed in Figure 6 . We see that HAPMIX is fairly accurate , but not perfectly accurate , in inferring segments of African ancestry . For comparison , HAPMIX results on chromosome 1 for three different Mozabite individuals are displayed in Figure 7 . Results are discussed below , but look generally similar , apart from showing some much larger ancestral segments , to Figure 6 . Different Mozabite individuals within our sample had different estimates of sub-Saharan African ancestry proportions , with a majority at close to 20% , but several individuals having a somewhat higher fraction . Exploration of the causes of this variation ( Figure 7 ) revealed a systematic tendency for those individuals with higher proportions of sub-Saharan African ancestry to have large ( tens of megabases ) segments in their genome with an African origin . Such large segments are only consistent with admixture within the last 20–30 generations , showing the admixture process has continued into more recent times . In fact , the individual with the highest estimated proportion ( 75% ) of sub-Saharan African ancestry had at least one inferred non-European chromosome throughout virtually their entire genome ( Figure 7 ) , consistent with admixture in the last generation , and demonstrating that the admixture process continues today in the Mozabite population . When we restricted our HAPMIX-based dating inference to those two individuals with the highest estimated sub-Saharan African ancestries , we found that the highest likelihood was obtained at 10 generations , much lower than the 100 generations estimated for the combined dataset . In conclusion , the data are most consistent with a model in which individuals from sub-Saharan Africa have been genetically interacting with the Mozabite population as an ongoing process for at least the last 100 generations ( ∼2800 years ) and probably considerably longer , given the underestimation properties of our dating method in simulations , and the likely contribution of recent admixture in producing this estimate . Overall , we were encouraged by the ability of HAPMIX to infer both long and short blocks of distinct continental ancestry in this anciently admixed population . Which modern-day populations are most closely related to the founder populations for the Mozabite ? Following the promising results of our simulation study , we used inferred segments of African-related or European-related ancestry to estimate FST values between the true ancestral populations of the Mozabite and the two reference populations ( YRI and CEU ) . We obtained estimates of 0 . 034 for the FST between the true African ancestral population and YRI , and 0 . 026 for the FST between the true European ancestral population and CEU . Substituting various HGDP Bantu-African and European/West Asian populations for YRI and CEU in the FST computations yielded similar results , with FST values ranging between 0 . 02 and 0 . 04 . For the African founder population , the West African Mandenka and Yoruba populations , and another HGDP Bantu population , “BantuKenya” , had the smallest FST values ( 0 . 034–0 . 035 ) . For the European-related founder population , the Italians and Tuscans , closely followed by the Palestinians , had the smallest FST values ( 0 . 021–0 . 022 ) , suggesting an origin in South-East Europe or the Middle East . Although care should be taken in interpreting these values , they indicate that the ancestral segments of Mozabite are significantly diverged from extant Bantu-African and European-related populations . To verify this , we ran principal components analysis on the Mozabite samples together with French and Yoruba samples from HGDP , using the EIGENSOFT software [27] . Results are displayed in Figure 8 . The first eigenvector indicates , as expected , that the Mozabite samples are intermediate between Europeans and sub-Saharan Africans , consistent with the admixture detected by HAPMIX , and identifying the same two outlier samples with much higher African ancestry . In support of our FST analysis on the ancestry segments , the second eigenvector appears mainly to separate the Mozabite from the other populations , indicating that they are not perfectly modeled as a linear combination of European and African ancestry . Apart from the 2 individuals with much higher African ancestry , the EIGENSOFT plot identifies a further set of 8 Mozabite individuals showing reduced genetic drift ( i . e . second eigenvector coefficients ) , and much more variable ancestry estimates relative to the full set ( Figure 8 ) . For these 8 samples , HAPMIX gave a maximum likelihood estimate of 75 generations for the admixture event , again noticeably lower than 100 generations for the full dataset and demonstrating more recent admixture in these individuals . Therefore , we observe a correlation between time since admixture across different individuals , and level of genetic drift relative to modern-day European and African populations . A hypothesis consistent with this finding is that genetic drift has occurred in the Mozabite population itself , during or after admixture , in way that has affected both African and European ancestral segments . Alternatively , the founder populations may have gradually drifted during the thousands of years of admixture that have affected this group . To understand the performance of HAPMIX on real populations with a wider range of histories , we applied the method to 13 different HGDP populations that were of African , Middle Eastern , or European origin . Using YRI and CEU as ancestral populations , HAPMIX inferred that 5 of these populations had greater than 0% and less than 100% European-related ancestry ( Table 4 ) . The estimates of European-related ancestry in these 5 populations range from 2%–97% , and the numbers of generations since mixture range from 60–120 . The three Middle Eastern populations ( Bedouin , Palestinian , and Druze ) all show a substantial African-related mixture ( 3%–9% African-related ancestry ) . The inferred dates of 60–90 generations correspond to about 2 , 000–4 , 000 years ago – contemporaneous with our estimate of the oldest admixture time for the North African Mozabite population – taking into account the fact that HAPMIX systematically underestimate mixture dates by up to 25% for mixtures this old ( see simulations above ) . These results are historically interesting , allowing us to conclude that there is likely to be African ancestry in Middle Eastern populations today that dates to population mixture that occurred in Biblical times . The West African Mandenka population appear to have received ancestry from outside sub-Saharan Africa around the same period or before ( 120 generations ago ) . This mixture may not be unexpected , given the Mandenka's geographical location relatively close to the Sahara , and suggests that gene flow across the Sahara has occurred in both directions . Finally , the Middle Eastern results contrast with results for the HGDP European populations , where we consistently estimate the African mixture proportions at close to 0% .
We have described a method that takes advantage of haplotype information to accurately infer segments of chromosomal ancestry in admixed samples , even in the case of ancient admixture . The method is likely to be useful both for disease mapping in admixed populations and for drawing inferences about human history , as our empirical analyses of samples from African American and HGDP populations have demonstrated . The ability to reconstruct chromosomal segments from ancestral populations that contributed to recent or ancient admixture is a particular advance , as it implies that genetic analyses need not be restricted to extant populations but can also be applied to populations that have only left admixed descendents today [28] . By reconstructing allele frequencies and haplotypes from these populations , extensions of HAPMIX may be able to learn about population relationships as they existed at the time of the Neolithic agricultural migrations or even before . An open question is how far back in time HAPMIX will be able to probe the histories of anciently admixed populations . The simulations of Figure 3 suggest that HAPMIX has power in theory to produce informative estimates of local ancestry even for populations that admixed 400 generations – over 10 , 000 years ago . HAPMIX has particularly important applications for disease gene mapping , especially in African Americans where the ancestry estimates are exceedingly accurate and where we have shown that they are not systematically biased . With the accurate estimates of ancestry that emerge from HAPMIX it should be possible to carry out dense case-control association studies with hundreds of thousands of markers , which simultaneously test for admixture association [1]–[3] and case-control association , providing more power to detect disease associations from the data than that can be obtained from either approach alone . While our analyses show that HAPMIX—because of its explicit use of a population genetic model—has better power to infer locus-specific ancestry than many recent methods , the method also has some limitations in the range of scenarios in which it can be used . For example , it is not currently designed for the analysis of mixtures of more than two ancestral populations , and it requires the use of reference populations . Future directions for extending the HAPMIX method include allowing more than two ancestral populations , using the admixed samples as a pool of reference haplotypes instead of relying on input haplotypes from reference populations , and automating the fitting of model parameters . In addition , although determining the number of generations since admixture with high accuracy is not necessary for effective inference of local ancestry , our results motivate additional work to enable detection of multiple admixture events at different points in time in order to refine the inferences that can be made about human history .
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The genomes of individuals from admixed populations consist of chromosomal segments of distinct ancestry . For example , the genomes of African American individuals contain segments of both African and European ancestry , so that a specific location in the genome may inherit 0 , 1 , or 2 copies of European ancestry . Inferring an individual's local ancestry , their number of copies of each ancestry at each location in the genome , has important applications in disease mapping and in understanding human history . Here we describe HAPMIX , a method that analyzes data from dense genotyping chips to infer local ancestry with very high precision . An important feature of HAPMIX is that it makes use of data from haplotypes ( blocks of nearby markers ) , which are more informative for ancestry than individual markers . Our simulations demonstrate the utility of HAPMIX for local ancestry inference , and empirical applications to African American and Mozabite data sets uncover important aspects of the history of these populations .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"genetics",
"and",
"genomics/genetics",
"of",
"disease",
"mathematics/statistics",
"genetics",
"and",
"genomics/bioinformatics",
"genetics",
"and",
"genomics/population",
"genetics"
] |
2009
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Sensitive Detection of Chromosomal Segments of Distinct Ancestry in Admixed Populations
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We previously showed that fusion between hepatocytes lacking a crucial liver enzyme , fumarylacetoacetate hydrolase ( FAH ) , and wild-type blood cells resulted in hepatocyte reprogramming . FAH expression was restored in hybrid hepatocytes and , upon in vivo expansion , ameliorated the effects of FAH deficiency . Here , we show that fusion-derived polyploid hepatocytes can undergo ploidy reductions to generate daughter cells with one-half chromosomal content . Fusion hybrids are , by definition , at least tetraploid . We demonstrate reduction to diploid chromosome content by multiple methods . First , cytogenetic analysis of fusion-derived hepatocytes reveals a population of diploid cells . Secondly , we demonstrate marker segregation using ß-galactosidase and the Y-chromosome . Approximately 2–5% of fusion-derived FAH-positive nodules were negative for one or more markers , as expected during ploidy reduction . Next , using a reporter system in which ß-galactosidase is expressed exclusively in fusion-derived hepatocytes , we identify a subpopulation of diploid cells expressing ß-galactosidase and FAH . Finally , we track marker segregation specifically in fusion-derived hepatocytes with diploid DNA content . Hemizygous markers were lost by ≥50% of Fah-positive cells . Since fusion-derived hepatocytes are minimally tetraploid , the existence of diploid hepatocytes demonstrates that fusion-derived cells can undergo ploidy reduction . Moreover , the high degree of marker loss in diploid daughter cells suggests that chromosomes/markers are lost in a non-random fashion . Thus , we propose that ploidy reductions lead to the generation of genetically diverse daughter cells with about 50% reduction in nuclear content . The generation of such daughter cells increases liver diversity , which may increase the likelihood of oncogenesis .
Cell divisions in mitosis are thought to always produce daughter cells with the same chromosome content as the parental cell . Our recent studies with fusion-derived polyploid hepatocytes challenge that ideology . We propose that polyploid hepatocytes can undergo ploidy reductions , leading to the generation of genetically distinct daughter cells with reduced DNA content . Our group previously showed that transplantation of wild-type bone marrow into fumarylacetoacetate hydrolase ( Fah ) knockout mice leads to the generation of fusion-derived hepatocytes [1] , [2] . In this murine model for the human disease hereditary tyrosinemia type 1 , hepatocytes expressing FAH have a strong selective growth advantage and can repopulate the diseased host liver [3] , [4] . Fah−/− mice can be bred and kept healthy by administering the drug 2- ( 2-nitro-4-trifluoro-methylbenzol ) -1 , 3-cyclohexanedione ( NTBC ) in their drinking water [5] . This drug blocks tyrosine catabolism upstream of FAH and , therefore , prevents the accumulation of fumarylacetoacetate , the toxic substrate of FAH . NTBC withdrawal induces liver injury and results in death from liver failure 4–8 weeks later . In bone marrow transplanted Fah−/− mice , fusion between the Fah+/+ donor blood cells and Fah−/− host hepatocytes results in polyploid cells that have a selective advantage and can completely repopulate the liver [1] , [2] , [6] . Furthermore , FAH positive hepatocytes can be serially transplanted into secondary and tertiary Fah−/− recipients , thereby expanding the pool of hybrid hepatocytes and making them amenable to extensive genetic and cell biological analysis [3] , [7] . Bone marrow transplantation has been shown to generate fusion-derived hepatocytes by numerous investigators [8]–[12] . However , direct differentiation of hematopoietic precursors into liver epithelial cells cannot be excluded , but it is clear that the majority of fusion-derived hepatocytes arise by fusion of donor blood cells with preexisting hepatocytes [1] , [2] , [6] . In our previous studies , chromosomal analysis of hepatocytes from bone marrow transplanted mice revealed the presence of diploid fusion-derived hepatocytes [1] . This result was surprising since fusion-derived cells should be at least tetraploid . Thus , we hypothesized that fusion-derived hepatocytes could undergo ploidy reductions during regeneration , leading to genetic diversity among daughter cells . The present study rigorously examines whether fusion-derived hepatocytes undergo ploidy reductions . First , cell fusion experiments show conclusively that fusion-derived polyploid hepatocytes generate daughter cells with one-half DNA content . Unexpectedly , a high degree of aneuploidy was seen among fusion-derived cells . Secondly , ploidy reduction events were associated with independent marker segregation . Hepatocytes derived by cell fusion were expected to retain markers from each cell participating in the fusion event . Indeed , analysis of liver sections from repopulated mice showed the majority of nodules harboring both donor and host markers . However , we also detected a low ( but highly reproducible ) percentage of FAH positive nodules lacking additional markers . Third , to exclude the possibility that diploid hepatocytes expressing donor markers arose from transdifferentiation of hematopoietic cells , we employed a Cre-loxP reporter system in which ß-galactosidase ( ß-gal ) was only expressed in hepatocytes generated though cell fusion . As expected , polyploid hepatocytes expressed ß-gal and FAH . Consistent with the cytogenetic results , hepatocytes with diploid DNA content also expressed ß-gal and FAH . Finally , we tested individual cells for donor and host markers . Single cell PCR analysis of diploid daughter cells revealed a heterogeneous population containing a combination of donor and host markers . Taken together , our results demonstrate that fusion-derived polyploid hepatocytes undergo ploidy reduction events , generating heterogeneous populations of lower ploidy daughter cells .
Cell fusion produces hybrids with increased centrosome and chromosome numbers . Numerous studies have suggested that tetraploidy and aberrant centrosome numbers can result in genetic instability and cancer [13] , [14] . To test whether hepatocytes generated by cell fusion in vivo are genetically stable , we karyotyped hepatocytes from serially transplanted mice . Lethally irradiated Fah−/− recipients were transplanted with cKit+ Linneg/lo Sca1+ ( KLS ) bone marrow cells from wild-type or ROSA26 ( lacZTg/0 ) donors in a sex-mismatched fashion . Following NTBC withdrawal and liver repopulation , hepatocytes were serially transplanted into female Fah−/− recipients , allowing fusion-derived hepatocytes to undergo successive rounds of proliferation ( Figure 1A ) . Importantly , only FAH positive cells ( i . e . , fusion products ) but not Fah−/− hepatocytes can repopulate secondary recipient livers [3] . After completed repopulation , unselected hepatocyte metaphases were analyzed by standard G-banding techniques . Fusion between two diploid cells generates a tetraploid cell . Normal hepatocytes polyploidize in an age-dependent manner and can be diploid , tetraploid , octaploid or higher ( reviewed in [13] ) . Therefore , the chromosome content of hybrid cells arising from hepatocyte-blood fusion must be tetraploid or greater . The chromosome content of fusion-derived hepatocytes ( i . e . , positive for Y-chromosome ) varied widely ( Figure 1B ) . Approximately 15% of the metaphases had exactly 80 chromosomes with the percentage increasing to 30% of metaphases harboring nearly 80 chromosomes ( 80±5 ) . Furthermore , 9% of metaphases had exactly 40 chromosomes , and the percentage increased to 14% containing nearly 40 chromosomes ( 40±4 ) . The presence of diploid and nearly diploid fusion-derived hepatocytes suggests that they were produced by ploidy reduction events of tetraploid fusion products . Fusion hepatocytes with intermediate numbers of chromosomes were also found ( >40 and <80 , >80 and <160 ) ( Figure 1B ) . Nearly all of the Y-chromosome containing metaphases had numerical chromosome abnormalities ( Table 1 ) . These numerical aberrations may have resulted from the fusion process or by DNA damage sustained by Fah−/− hepatocytes during NTBC withdrawal [15] . The distribution of chromosomes among fusion-derived hepatocytes clearly supported a pattern of cell fusion , ploidy reduction and polyploidization ( Figure 1C ) . For example , fusion between a female diploid blood cell ( 40 , XX ) and male diploid hepatocyte ( 40 , XY ) results in a tetraploid fusion-derived hepatocyte ( 80 , XXXY ) . A ploidy reduction event generates two types of daughter cells ( 40 , XY and 40 , XX ) that can polyploidize , giving rise to tetraploid cells ( 80 , XXYY and 80 , XXXX , respectively ) . Because we could not distinguish between daughter cells that lost the Y-chromosome and host female hepatocytes , we focused exclusively on Y-chromosome positive metaphases . Fusion-derived tetraploid hepatocytes ( ∼80 , XXXY ) were detected in 21% of the metaphases analyzed . Approximately 14% of Y-chromosome positive metaphases were nearly diploid ( ∼40 , XY ) , corresponding to daughter cells arising through ploidy reduction . Furthermore , 8% of cells were ∼80 , XXYY , which is the expected karyotype for polyploidized diploid cells . In addition to finding fusion-derived hepatocytes arising from diploid-diploid cell fusion , we detected cells generated by fusion between male tetraploid hepatocytes ( 80 , XXYY ) and female diploid blood cells ( 40 , XX ) . Hexaploid cells ( 120 , XXXXYY ) were detected in 17% of the metaphases . Furthermore , triploid cells ( either ∼60 , XYY or XXY ) , which are the predicted daughter cells of hexaploid reduction events , were identified in 17% of metaphases . Together , these data strongly support the emergence of daughter cells containing one-half DNA content from fusion-derived hepatocytes . Similar to normal diploid hepatocytes , diploid daughter cells either remain diploid or polyploidize to generate tetraploid hepatocytes . After demonstrating that fusion-derived hepatocytes could generate daughter cells with one-half chromosome content , we hypothesized that ploidy reduction events should also lead to marker segregation among daughter cells . If ploidy reduction occurs in a tetraploid fusion-derived hepatocyte , there is a 50% chance of losing a heterozygous or hemizygous marker . Liver sections from mice repopulated by fusion-derived hepatocytes in serial transplantation experiments ( Figure 1A ) were stained for FAH , Y-chromosome and ß-gal activity ( Figures 2A–2D ) . Typically , FAH is co-expressed with the Y-chromosome ( Figure 2A ) and ß-gal ( Figure 2D ) . However , while most regenerating nodules expressed all markers of cell fusion , a fraction of FAH positive nodules ( 2–5% ) were Y-chromosome negative ( Figures 2B and 2C ) or ß-gal negative ( Figure 2D ) . Based on the expected loss-of-heterozygosity frequency of one-half , the observed frequency suggests that 4–10% of the FAH positive nodules were initiated by cells that had undergone ploidy reductions . Heterogeneous FAH positive nodules ( lacking one or more donor markers ) were consistently found in repopulated mice ( Figure 2E ) . Similar results were obtained regardless of the transplantation scheme ( F>M>F or M>F>F ) . Approximately 25–50% of primary recipients contained Y-chromosome negative FAH positive nodules , and this number increased to 90–100% of secondary recipients . ß-gal negative FAH positive nodules were seen in all serially transplanted mice analyzed . These data demonstrate that ploidy reduction events leading to the formation of heterogeneous daughter cells occur in a large fraction of livers repopulated by fusion-derived hepatocytes . To facilitate the genetic analysis of fusion-derived hepatocytes , a Cre-loxP system was used to track fusion products . Fah−/− mice were bred with transgenic animals expressing Cre-recombinase via a hepatocyte-specific albumin promoter ( Alb-Cre ) [16] . Lethally-irradiated recipient mice were transplanted with bone-marrow from a ROSA26 reporter ( R26R ) mouse ( Figure 3A ) [17] . These animals harbor a floxed allele of the lacZ gene at the ROSA26 locus . In this transplantation scheme , ß-gal is only expressed when the R26R and Alb-Cre alleles are combined in the same cell . Hepatocytes were isolated from repopulated mice and found to express ß-gal , conclusively showing that these cells were derived by fusion between donor blood cells and host hepatocytes ( Figure 3B ) . Fusion-derived cells also expressed FAH , indicating the successful activation of wild-type Fah supplied by donor hematopoietic cells ( Figure 3C ) . Next , we examined reduction of DNA content and marker segregation in fusion hepatocytes . Single cell hepatocyte suspensions ( containing a mixture of fusion-derived hepatocytes and host hepatocytes ) from repopulated mice were loaded with the DNA dye Hoechst 33342 and analyzed by flow cytometry . In control experiments , hepatocyte ploidy populations were readily identified in non-transplanted mice ( Figure S1A and S1B ) . As expected , livers isolated from aged mice ( Figure S1A ) contained fewer diploid hepatocytes than livers isolated from young mice ( Figure S1B ) . Furthermore , diploid hepatocytes were isolated with high purity . Sorted 2n hepatocytes were >99% pure ( Figure S1C ) , and they contained a single Y-chromosome ( Figure S1D ) . Analysis of mice repopulated to ∼40% by fusion hepatocytes revealed populations of diploid and polyploid hepatocytes ( Figure 3D ) . The diffuse polyploid population is consistent with the chromosome distribution in Figure 1B . To utilize ß-gal as a marker of cell fusion , hepatocytes were loaded with fluorescein di-ß-D-galactopyranoside ( FDG ) , a substrate that becomes fluorescent when cleaved by the enzyme . As expected from the overall degree of FAH repopulation , ß-gal was expressed by a fraction of polyploid hepatocytes ( 27±11% of all cells ) ( Figure 3E ) . Moreover , interrogation of diploid hepatocytes revealed a subpopulation of ß-gal positive cells ( 8±3% of all diploids ) . Hepatocyte populations of different ploidy were also FACS-sorted and subjected to FAH immunocytochemistry ( Figure 3F ) . A portion of unfractionated hepatocytes expressed FAH ( 36±8% ) . Consistent with ß-gal expression , a subpopulation of diploid hepatocytes also expressed FAH ( 10±3% ) . Together , these results show that fusion-derived hepatocytes undergo ploidy reduction events , generating diploid hepatocytes that express ß-gal and FAH . Although the mechanism by which fusion-derived hepatocytes undergo ploidy reductions is unknown , the data clearly suggest a model in which individual chromosomes/markers segregate independently of one another . For example , as described in Figure 3A , fusion between a donor diploid blood cell ( female , R26RTg/0 , Fah+/+ ) and recipient diploid hepatocyte ( male , Alb-Cre , Fah−/− ) generates a tetraploid cell containing a single Y-chromosome and a single copy of R26R . The Alb-Cre genotyping assay fails to distinguish between hemi- and homozygous mice . Thus , tetraploid fusion-derived hepatocytes undergoing ploidy reductions should generate a pair of diploid daughter cells , and each daughter cell should have a 50% chance of inheriting either R26R or the Y-chromosome . We performed single cell genotyping of diploid hepatocytes to determine whether chromosomes/markers were lost in cells that had undergone ploidy reduction events . Hepatocytes from repopulated livers ( Figure 3A ) were FACS-purified on the basis of DNA content and genotyped for donor ( Fah and R26R ) and host ( Cre and Y-chromosome ) markers . One of the major obstacles to single cell genotyping is PCR failure , resulting from DNA degradation or template inaccessibility [18] . In our hands , PCR failure ranged from 0 to 40% , which is consistent with published rates [18] . To minimize the effects of PCR failure , each marker was detected with two independent primer sets , thus reducing the net dropout rate to 0% ( Fah ) , 13 . 2% ( R26R ) , 0 . 5% ( Cre ) and 2 . 5% ( Y-chromosome ) . As a control , single splenocytes from repopulated mice were genotyped and found to contain only host markers ( cells 1 and 2 ) or donor markers ( cells 3–5 ) ( Figure 4A ) . These results are consistent with a high degree of donor engraftment seen in our KLS and bone marrow transplanted mice ( Duncan , Hickey and Grompe , unpublished results ) . Single cell genotyping was performed on 157 diploid hepatocytes derived from two independently transplanted mice . Representative PCR data is shown ( Figure 4B ) . Cell 7 contained only host markers , suggesting that it was host-derived . Cell 1 , which was positive for all makers , was fusion-derived . All of the remaining cells illustrated loss of one ( cells 2 and 3 ) or more markers ( cells 4–6 ) . Overall , Fah was detected in 29% of the cells . Detailed analysis of the diploid Fah positive fusion products revealed that 57% lost the R26R transgene , 33% lost the Cre transgene and 70% lost the Y-chromosome ( Figure 4C ) . Although PCR failure may account for a small percentage of the observed marker loss , the high degree of marker loss represents loss-of-heterozygosity at the indicated locus . Furthermore , loss of one or more markers was identified in subpopulations of diploid Fah positive hepatocytes ( Figure 4D ) . Only 13% of cells , for example , contained all four markers . Loss of a single marker was detected in 13% ( Y-chromosome ) and 17% ( R26R ) of the cells . Loss of two markers ( R26R/Y-chromosome , Cre/Y-chromosome ) or three markers ( R26R/Cre/Y-chromosome ) was found in 24% , 17% and 15% , respectively , of diploid hepatocytes . Together , these data showed that diploid daughter cells were genetically unique , suggesting that autonomous markers segregate independently during ploidy reduction events .
In this study , we demonstrated that fusion-derived hepatocytes could undergo ploidy reductions . Initially , serial transplantation experiments were performed . Cytogenetic analysis showed that 14% of fusion-derived hepatocytes were nearly diploid . Surprisingly , fusion-derived hepatocytes were highly aneuploid . While most regenerating nodules expressed all markers of cell fusion , a fraction of FAH positive nodules ( 2–5% ) were Y-chromosome negative or ß-gal negative . This frequency suggests that 4–10% of the nodules were initiated by cells that had undergone ploidy reduction events . Next , we utilized a ß-gal reporter system to track fusion products in discrete ploidy populations . Polyploid hepatocytes expressed FAH ( indicating that fusion-derived cells were reprogrammed to express donor genes ) and ß-gal ( demonstrating that these cells were derived by cell fusion ) . Significantly , diploid hepatocytes also expressed ß-gal and FAH , establishing that these cells originated from polyploid fusion-derived hepatocytes . Finally , we carefully tracked donor/host markers in hepatocytes that had undergone ploidy reductions by single cell genotyping . The majority of diploid daughter hepatocytes ( 87% ) were negative for one or more markers , giving rise to a heterogeneous population of cells . These results suggest that markers/chromosomes segregate independently during ploidy reduction events . Hepatocyte polyploidization has been documented in many species ( reviewed by Gupta [13] ) , but ploidy reversal has not been rigorously characterized . Our experiments provide proof-of-concept that ploidy reversal does occur in fusion-derived hepatocytes . Several reports also suggest that normal hepatocytes may undergo ploidy reduction . For instance , treatment of rodents with hepatotoxins thioacetamide [19] and carbon tetrachloride [20] led to a dramatic increase in diploid hepatocytes and concomitant decrease in polyploid hepatocytes over 72 hr . Differential proliferation and/or cell death was not seen among diploid or polyploid hepatocytes [20] . Thus , it is possible that normal polyploid hepatocytes undergo ploidy reductions , but this hypothesis remains to be tested . The high degree of aneuploidy displayed by fusion-derived hepatocytes is surprising . It is unclear whether aneuploidy resulted directly from the fusion and/or ploidy reduction events or indirectly as a consequence of the Fah repopulation model [5] . Furthermore , we cannot exclude the possibility of stochastic chromosome loss during mitosis [21] . Thus , aneuploid hepatocytes could arise from the random loss of chromosomes by fusion-derived hepatocytes undergoing extensive proliferation . A number of possibilities could explain how diploid hepatocytes are generated from polyploid fusion-derived hepatocytes . First , it is theoretically possible that binucleated fusion-derived hepatocytes could simply complete cytokinesis ( Figure 5A ) . Normal binucleated polyploid hepatocytes are formed through failed cytokinesis [22] , [23] . For example , a mononucleated diploid hepatocyte undergoes a regular mitosis , but then separation of the two daughter cells fails , generating a binucleated tetraploid cell with two diploid nuclei [22] . Whether binucleated hepatocytes could resume cytokinesis is unclear , but it remains a possibility . In the context of fusion-derived hepatocytes , the completion of cytokinesis would generate two mononucleated diploid daughter cells , each with the same genotype as the original fusion partners . As seen in Figure 4D , subsets of diploid hepatocytes contained a donor marker ( Fah ) and a recipient marker ( Cre and/or Y-chromosome ) , proving that these cells were genetically distinct from the original fusion partners . Therefore , a cytokinesis-type mechanism can be excluded . The second possibility is chromosome loss via multipolar mitosis , which can lead to the random segregation of chromosome content among two or more daughter cells [24] . Fusion-derived hepatocytes have increased numbers of centrosomes , which could result in the formation of multiple spindle poles during prophase . Thus , multipolar mitotic events could enrich for daughter cells with diploid chromosome content ( Figure 5B ) . However , multipolar mitosis cannot adequately explain the clustering of fusion-derived hepatocytes with atypical chromosome counts . For example , triploid hepatocytes with ∼60 chromosomes ( XXY or XYY ) comprised 17% of the metaphases analyzed ( Figure 1C ) . These daughter cells likely originated from hexaploid fusion-derived hepatocytes . It is difficult to imagine how multipolar mitosis would enrich for cells with such abnormal chromosome counts . Furthermore , if ploidy reduction were achieved by multipolar mitosis , then each chromosome should be lost with the same low frequency ( i . e . , 1/19 for autosomes ) . Single cell genotyping of diploid daughter hepatocytes showed loss of R26R ( located on chromosome 6 [25] ) and the Y-chromosome at 50% or greater ( Figure 4C ) . This high degree of marker segregation strongly suggests that chromosome/marker loss occurs in a non-random fashion . Another possibility to explain the emergence of daughter hepatocytes with one-half DNA content is cell division without DNA replication ( Figure 5C ) . This type of ploidy reduction was first described in the mosquito Culex pipiens [26] , [27] but has never been described in mammalian cells . In this model , fusion-derived hepatocytes could proceed through G1 phase of the cell cycle , skip S-phase and progress to G2/mitosis . Pairing between homologous chromosomes would ensure proper chromosome segregation . This type of mechanism accounts for the generation of diploid daughter cells ( Figures 1B , 3E and 3F ) as well as enrichment for atypical triploid daughter cells ( Figure 1B ) . Moreover , the high degree of marker loss seen in diploid daughter cells ( Figure 4C ) is possible through a chromosome pairing interaction . Rigorous testing of all potential mechanisms must be performed to elucidate the cellular processes governing ploidy reductions in fusion-derived hepatocytes . Finally , direct transmission of DNA via horizontal gene transfer ( HGT ) into diploid hepatocytes must be considered . HGT among somatic cells involves phagocytosis of apoptotic cells followed by nuclear uptake/integration of whole chromosomes or chromosome fragments by the engulfing cell [28] , [29] . HGT was hypothesized to induce hepatocyte reprogramming in xenotransplantation experiments [30] . In our studies , diploid host hepatocytes could acquire genes from apoptotic donor blood cells , resulting in hepatocyte reprogramming while maintaining a nearly diploid chromosome count ( Figure 5D ) . However , the presence of multiple donor markers on different chromosomes by HGT is expected to be rare , and we found that nearly half of diploid fusion-derived hepatocytes harbored at least two donor markers ( Figure 4D ) . Therefore , our data strongly argue against an HGT-type of mechanism . Regardless of the mechanism , ploidy reduction events have significant implications . In the context of fusion-derived hepatocytes , ploidy reductions can be a confounding factor when tracing markers during stem cell transplantation . Donor markers can be lost during ploidy reductions , thus leading to an underestimate of engraftment . Similarly , host markers can be lost from hybrids , obscuring the existence of fusion and giving the false impression of transdifferentiation . Because cell fusion between transplanted cell types and target organs has been described in many experimental systems ( reviewed in [31] ) , the possibility of ploidy reductions needs to be considered when interpreting cell transplantation experiments . Furthermore , we propose that ploidy reduction events may contribute to tumorigenesis . The independent segregation of chromosomes from polyploid cells results in genetically heterogeneous diploid daughter hepatocytes . Individual daughter cells could lose tumor suppressors , generating a subset of hepatocytes with oncogenic potential .
The Institutional Animal Care and Use Committee of the Oregon Health and Science University approved all mouse experiments . The following inbred mouse strains were used: wild-type ( C57Bl and 129 ) , transgenic ROSA26 ( C57Bl and 129 ) [32] , Fah−/− ( C57Bl and 129 ) [33] , transgenic R26R-lacZ C57Bl [34] , transgenic Albumin-Cre C57Bl [35] . KLS cells were sorted from mouse bone marrow , as described [36] . Antibodies used for cell sorting are described in Protocol S1 . Primary hepatocytes were isolated by two-step collagenase perfusion [4] and cultured hepatocytes were isolated by trypsinization . For detection of hepatocyte ploidy populations , hepatocytes ( 2×106/ml ) were incubated with 15 µg/ml Hoechst 33342 ( Sigma ) and 5 µM reserpine ( Invitrogen ) for 30 min at 37° . Cells were analyzed and/or sorted with an InFlux flow cytometer ( Cytopeia ) using a 150 µm nozzle . Dead cells were excluded on the basis of 5 µg/ml propidium iodide ( Invitrogen ) incorporation . Cells adhering to each other ( i . e . , doublets ) were eliminated on the basis on pulse width . Ploidy populations were identified by DNA content using an ultraviolet 355 nm laser and 425–40 nm bandpass filter . Sorted hepatocytes were collected in DMEM with 4 . 5 g/l glucose ( HyClone ) containing 50% fetal bovine serum ( FBS ) ( HyClone ) . The purity of sorted populations was determined at the end of each sort , and only highly purified populations ( >99% pure ) were used for subsequent assays . Transplantation of hematopoietic cells ( either bone marrow or KLS cells ) was performed as previously described [1] , [12] . Briefly , hematopoietic cells ( either 4×106 bone marrow cells or 3–4×103 KLS cells ) were injected retro-orbitally into groups of congenic Fah−/− recipient mice . KLS cells were co-transplanted along with 3×105 bone marrow cells derived from an unirradiated recipient mouse . Host mice were lethally irradiated with 12 Gy using a 137Cs irradiator 4–24 hr prior to transplantation , obtained by two doses of 6 Gy each . Mice were maintained on NTBC drinking water ( 8 µg/ml ) . NTBC was withdrawn 3–12 weeks post transplantation , providing a selective environment for the proliferation of FAH positive fusion-derived hepatocytes . For serial transplantation experiments , hepatocytes were isolated from primary transplanted mice and 1–3×105 cells injected intrasplenically into Fah−/− recipient mice . NTBC was stopped immediately , allowing for selection of FAH positive cells [3] . Freshly isolated primary hepatocytes were seeded at 1–2×103 cells/cm2 on Primaria tissue culture plastic ( Beckton Dickinson ) . Cells were incubated in hepatocyte culture medium containing DMEM with 4 . 5 g/l glucose ( HyClone ) , 10% FBS ( HyClone ) , nonessential amino acids ( Cellgro ) and antibiotic-antimycotic ( Cellgro ) . Nonadherent cells were removed after 4 hr . Hepatocytes were then incubated in culture medium supplemented with 100 ng/ml human epidermal growth factor ( Invitrogen ) plus insulin , transferrin , selenium and ethanolamine ( ITS-X , Invitrogen ) . After ∼40 hr , hepatocytes were treated with 150 mg/ml colcemid ( Sigma ) for 2–4 hr and harvested by trypsinization . After extensive washing , slides were incubated for 10 min in 56 mM KCl with 5% FBS and fixed with methanol:acetic acid ( 3∶1 ratio ) . For karyotype analysis , chromosomes were G-banded with a standard trypsin/Wright's stain protocol . Fluorescent in situ hybridization on interphase hepatocytes was performed using a Cy3-labeled whole chromosome paint ( mouse Y-chromosome ) per manufacturer's instructions ( Cambio ) . Histological analyses were performed as described [3] . For FAH immunocytochemistry , hepatocytes ( either unfractionated or FACS-purified ) were allowed to adhere to collagen-coated Lab-Tek II , CC2-treated chamber slides ( Nunc ) in hepatocyte culture medium for 24 hr . Slides were washed extensively , fixed with methanol and dehydrated with acetone . After blocking in 5% normal donkey serum , cells were incubated with a custom rabbit polyclonal FAH antibody diluted 1∶1000 and detected with 10 ng/ml donkey anti-rabbit secondary antibody conjugated to Alexafluor 555 ( Invitrogen ) . Nuclei were visualized with 200 ng/ml Hoechst 33342 . For detection of ß-gal activity , hepatocytes were plated in culture medium on Primaria plastic . After 24 hr , adherent hepatocytes were subjected to X-Gal ( 5-bromo-4-chloro-3-indolyl-ß-D-galactopyranoside , Invitrogen ) staining as described [37] . Alternatively , flow cytometry was used to detect ß-gal activity in Hoechst-stained hepatocytes ( for the detection of ploidy populations ) using FDG reagent ( Invitrogen ) per manufacturer's instructions . Single hepatocytes or splenocytes were FACS-sorted into individual wells of a 96-well PCR plate , lysed and subjected to semi-nested PCR , as described in Protocol S2 . Fisher's exact test ( two-sided ) was used to determine statistical significance . P values less than 0 . 05 were considered statistically significant .
|
The liver comprises many different types of cells , including hepatocytes . Hepatocytes perform numerous physiological functions , such as detoxification , metabolism , and protein synthesis . Hepatocytes have the ability to fuse with blood cells , generating hybrid hepatocytes that contain nuclei from both fusion partners . In cases of genetic liver disease , fusion between diseased hepatocytes and normal blood cells can result in the formation of hybrid hepatocytes that function normally . In this series of experiments , we show that fusion hepatocytes produce daughter cells with one-half the amount of DNA found in the parental fusion hepatocyte . Furthermore , we show that the daughter cells are genetically distinct from each other . The increase in genetic diversity within the liver could give rise to hepatocytes lacking proper growth control , potentially resulting in tumor formation and cancer .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"cell",
"biology/cell",
"growth",
"and",
"division",
"gastroenterology",
"and",
"hepatology/hepatology",
"developmental",
"biology/stem",
"cells"
] |
2009
|
Ploidy Reductions in Murine Fusion-Derived Hepatocytes
|
Trachomatous trichiasis ( TT ) , inturned eyelashes from repeated infection with Chlamydia trachomatis , is the leading infectious cause of blindness in the world . Though surgery will correct entropion caused by trachoma , uptake of TT surgery remains low . In this case-control study , we identify barriers that prevent TT patients from receiving sight-saving surgery . Participants were selected from a database of TT cases who did ( acceptors ) and did not ( non-acceptors ) have surgery as of August 2015 . We developed an in-home interview questionnaire , using open and closed-ended questions on perceived barriers to accessing surgical services . We compared responses between the acceptors and non-acceptors , examining differences in reasons for and against surgery , sources of TT information , and suggestions for improving surgical delivery . 167 participants ( mean age 61 years , 79 . 7% females ) were interviewed . Compared to acceptors , non-acceptors were more likely to report they had no one to accompany them to surgery ( 75 . 3% vs . 42 . 6% , p<0 . 0001 ) , they could manage TT on their own ( 69 . 9% vs . 31 . 5% , p<0 . 0001 ) , and the surgery camp was too far ( 53 . 4% vs . 28 . 7% , p = 0 . 001 ) . Over 90% of both acceptors and non-acceptors agreed on the benefits of having surgery . Fear of surgery was the biggest barrier stated by both groups . Despite this fear , acceptors were more likely than non-acceptors to also report fear of losing further vision without surgery . Barriers included access issues , familial and/or work responsibilities , the perception that self-management was sufficient , and lack of education about surgery . Fear of surgery was the biggest barrier facing both acceptors and non-acceptors . Increasing uptake will require addressing how surgery is presented to community residents , including outlining treatment logistics , surgical outcomes , and stressing the risk of vision loss .
Trachomatous trichiasis ( TT ) , a complication of trachoma , is the leading infectious cause of preventable blindness in the world today [1] . Repeated infection of children with Chlamydia trachomatis can lead to scarring of the conjunctiva , causing entropion in adults as the eyelid turns inward , and trichiasis as the eyelashes deviate inward and touch the globe . Damage to the cornea from trichiasis , as well as abnormal conjunctiva , can result in vision loss . Trachoma remains a problem in the poorest societies of the world . As of 2014 , an estimated 21 million people were afflicted with active trachoma , 7 . 3 million of whom have trichiasis and 2 . 2 million of whom are either completely blind or severely visually impaired . The majority of this blinding trachoma occurs in African countries [2] . The World Health Organization ( WHO ) recommends surgery to correct entropion caused by trachoma , and this has been shown to improve symptoms and even restore vision [3 , 4] . Despite the benefits , uptake of TT surgery remains low in published reports [5–8] . Some work to elucidate barriers has been done [7 , 9–14] but understanding differences between persons in the same communities who did and did not have surgery would clarify barriers and provide data to improve access to surgical programs . Following a study on screening in Kongwa District , Tanzania for TT using Community Drug Distributors , a TT surgical camp was convened to offer free surgery and transport to the camp for all persons identified with TT [15] . Two years after the camp , we determined those who did and did not have surgery with the goal of interviewing them to identify barriers to receiving sight-saving surgery .
This research was approved by the Institutional Review Board of Johns Hopkins School of Medicine , and by the National Institute for Medical Research in Tanzania . The project was conducted according to the tenets of the Declaration of Helsinki . All subjects provided written , informed consent prior to participation . We conducted a case-control study where cases were TT patients who did not have surgery , and controls were TT cases who did have surgery . TT cases were chosen from all 29 communities in Kongwa District , Tanzania who were in an existing database obtained during screening in 2013 [15] . Each case was confirmed by an eye nurse , and again by the first author , Mr . Bickley . There is no Master Grader training for trichiasis because the signs are so rare . For this study , Mr . Bickley’s training was provided using images and field practice sessions in Kongwa , following WHO criteria that trichiasis is at least one lash touching the globe or evidence of epilation . The training was conducted by the second author , Mr . Mkocha , who has 20 plus years’ experience grading trachoma and trichiasis in the field . The list of TT cases included those who had surgery during the camp , and those who did not have surgery . In addition to the surgery camp in 2013 , static surgery services are available through the Kongwa District Hospital . Village health workers in these communities referred TT cases not on the list to participate in our study on the day of the interviews . The interviews were conducted from July 26 to August 19 , 2015 . Based on previous research [7 , 9–14] , we developed a questionnaire on perceived barriers to accessing health care and surgical services . The questionnaire was translated from English to Swahili and then back-translated from Swahili to English for comprehension , then pilot-tested in Kongwa in non-study villages and refined before administration in study villages . The beginning of the questionnaire included the following domains: participant demographics , past use of health services , knowledge of TT , and source ( s ) of that knowledge ( Fig 1: Background questions asked to participants ) . The next set of questions was designed to elicit barriers and enablers to surgery , and was performed in four parts: The first two parts asked for the perceptions of why “persons in the community” did not have surgery . The first set of questions was open-ended and the participant supplied answers , urged to provide up to three responses . The second set of questions required a yes or no response to a list of specific reasons that might prevent persons from having surgery . Third was a set of questions that asked open-ended questions to reasons why persons would have surgery , and this was followed by the fourth part , which was a set of questions eliciting yes or no responses to reasons why persons would have surgery ( Fig 2: Questions about barriers/enablers to surgery asked to participants ) . For example , the question asking about barriers read as “Here are some of the reasons that we have heard are reasons that people with ‘kope’ [the local word for TT] do not go for surgery . Please tell us if you AGREE with these reasons , or if you do not think they are reasons why people with ‘kope’ do not have surgery” . In this way , the respondent does not have to state that this was his/her reason , but just that it was a reason people may not have surgery . We refer to these as “general” responses , rather than “personal” responses . The last part of the interview asked about the respondents’ personal reasons for choosing to have or not have surgery , first using open-ended questions and then asking questions requiring a yes or no response . We refer to these as “personal” responses . The interview concluded with a question on perceptions of what could be done to improve access to and quality of surgical services in the district ( Fig 3: Questions about respondents’ personal reasons regarding surgery ) . In-home interviews were conducted by trained interviewers who spoke both Swahili and the local languages of Kigogo and Kikaguro . The interviewers were not aware of which participants had or had not had surgery until the end of the interview , at which point an envelope was opened and the status revealed . At that point the final piece of the interview was conducted . Follow-up interviews were attempted at least 3 times for those TT cases that were temporarily away . Data was entered in a customized Microsoft Access database and analyzed using SAS ( Raleigh , NC ) . Using Fisher’s exact test , participant responses were compared between acceptors and non-acceptors of TT surgery . Specifically , differences were examined between demographics , use of health services , sources of learning about TT , reasons for and against having TT surgery ( both specific to the participant and “general” to persons in the community ) , and suggested improvements for TT delivery to residents . Answers to free response questions were translated into English and recoded into categories before comparison . A multivariate model with non-acceptance of surgery as an outcome adjusted for use of health services at the local post was constructed .
A total of 231 cases of TT were identified for this study . 179 TT cases were identified through the list created in 2012 [15] for surgery , and 52 were added at the time of interviewing in the village . Of the 179 cases from the 2012 list , 64 ( 28% ) refused , died , or were not locatable . Nineteen of the 52 newly added cases were newcomers to their respective villages since 2012 . The remaining 33 of those participants not on the 2012 list were in the original village censuses , but were not identified as TT cases at the time of the screening . Of 167 interviews , 93 were considered acceptors because they had surgery during the camp and 74 were considered non-acceptors because they did not have surgery ( Fig 4: Final categorization of 231 TT cases identified for the study ) . Most of the participants were female , and with a mean age above 60 years . There were no differences in age , gender distribution , time of residence in village , use of health services at Kongwa District Hospital , ability to identify their respective Community Health Worker , knowing anyone with TT , or knowing the health effects of having TT between acceptors and non-acceptors of surgery ( Table 1 ) . Non-acceptors were more likely to report use of health services at the local health post but less likely to report knowledge of the village surgical camp . Acceptors were more likely to have heard about TT from health personnel who provide trachoma MDA ( Table 1 ) . Table 2 evaluates the proportion , by surgical status , who responded affirmatively to closed-ended questions regarding barriers to having TT surgery as perceived by persons in the community . Significantly more non-acceptors reported that in “general” people state not being able to afford surgery ( 60 . 3% , p<0 . 001 ) , not knowing where to receive surgery ( 39 . 7% , p = 0 . 0001 ) , stating surgical camps were too far away ( 53 . 4% , p = 0 . 001 ) , not having someone to accompany and help them ( 75 . 3% , p<0 . 0001 ) , and claiming they could manage TT on their own with tweezers ( 69 . 9% , p<0 . 0001 ) . Few respondents regardless of group indicated that reasons might include the possibility that surgery would not help , was done poorly , or resulted in poor appearance afterward . Non-acceptors of surgery were asked about perceived barriers that others might report , and then asked directly about their own personal barriers . The answers they provided for reasons others might have given and their own reasons were compared ( Table 3 ) . Non-acceptors were more likely to report that others in “general” were afraid of getting surgery on their eyelids , 79 . 2% , versus reporting they personally had fears about surgery , 41 . 1% ( p<0 . 0001 ) . Similarly , non-acceptors were more likely to say that others did not have anyone to care for their children or complete their work while away at surgery ( 50 . 7% vs . 27 . 4% , p = 0 . 002 ) , could manage TT on their own with tweezers ( 69 . 9% vs . 30 . 1% , p<0 . 0001 ) , and did not have anyone to accompany them ( the patient ) to the surgery site ( 75 . 3% vs . 8 . 2% , p<0 . 0001 ) , all compared to their personal reasons for not receiving surgery ( Table 3 ) . The most common reason that non-acceptors reported that others would give for not having surgery was fear . For themselves , however , the most common reasons reported was not being able to afford surgery , despite the fact that surgery as well as the transport to and from the camp were free . The second most common personal reason provided was not knowing where to get surgery , which may explain cost uncertainty . Women were significantly more likely than men to report a “general” barrier was no one to accompany them to surgery . When asked about personal reasons , women were significantly more likely to report that they could manage TT with tweezers compared to males . Otherwise , the answers were comparable across genders . The open-ended question where participants could provide reasons why persons did not have surgery was not answered by 15% , and the most common answers had fear as a major component for both groups ( Table 4 ) . When participants were asked about the perceived benefits of having surgery for persons in “general” in an open-ended question , responses were similar between acceptors and non-acceptors , except that non-acceptors were less likely to spontaneously report a benefit to seeing better after surgery/going blind without surgery ( Table 5 ) . This finding contrasts to the benefits elicited when asked in a closed-ended fashion , where there were no significant differences between responses of acceptors and non-acceptors ( Table 6 ) . All the reasons , apart from simply being told to have surgery , appeared to be important reasons for surgery as reported by both groups . Both groups of participants were asked for their opinion on what might be done to improve access to surgical services supplied by the district ( Table 7 ) . Each participant could submit up to three responses . For non-acceptors , the most frequent response was that surgical efforts should continue , and that efforts to continue to aid village residents were needed . For acceptors , the most common suggestion was to provide more education and advice about the need for surgery . Of note , acceptors were more likely to suggest that successful patients should be examples for others , and non-acceptors were more likely to propose providing better information on surgical availability . A multivariate model with non-acceptance of surgery as an outcome adjusted for use of health services at the local post found the same predictors as are reported here in the univariate models .
Though the WHO recommends surgery for TT to prevent progression to blindness , uptake of surgery remains a problem . Previous studies in Kongwa among non-acceptors in the mid-1990s identified barriers to uptake as transportation to surgical sites , cost of surgery , arranging for the care of family and/or farm work while the patient is indisposed , and having someone to accompany the patient to the surgery site [9 , 11] , responses that we found continue to be important . At that time , the only surgical services were provided by local non-governmental organizations ( NGOs ) in the local hospital and there were no community-based surgical camps . Despite trying to bring free services closer and providing transport to and from the camps , the perception of access barriers to having surgery remain . Our results are also similar to those found in a 2012 study in Ethiopia , which identified the greatest barriers as financial , both direct and indirect [10] . Though surgery camps in our study region of Kongwa District , Tanzania were provided free of charge , patients still needed to take time off their farm work and childcare during the day of surgery and recovery , leading to an indirect opportunity cost to having TT surgery . Opportunity cost is not an insubstantial component in evaluating efficiency of health care delivery . Although providers see the ten minutes of a procedure as an investment , many patients are faced with a loss of several days that they may not be able to afford , especially if the patient is female with no child care options . We used a variety of approaches to elicit responses on reasons for accepting or not accepting surgery , including asking about reasons people in “general” might have and reasons participants in personally had for choosing or not choosing to have surgery . Despite using local interviewers when administering our questionnaire , it is possible that participants saw us as connected to health providers simply by asking questions about health care delivery . Because health services in our study areas are so limited , participants may have censored their responses so as not to seem critical or ungrateful of the efforts of health providers . We felt that by allowing participants first to answer what people in “general” might say are barriers , we were in essence eliciting responses they were hesitant to attribute to themselves . This was a valuable approach , as exemplified by our finding that fear was a major barrier to receiving surgery for both acceptors and non-acceptors . Among non-acceptors of surgery , fear was more often reported as a “general” reason against surgery , and not a personal reason for that individual . The fact that fear was a recurring reason in free response answers as well as an affirmative response to a forced choice question validates it as an important reason . It is notable that fear was not among the reasons cited for non-acceptance in earlier studies in Kongwa , although one response at that time was “I would not have surgery under any condition , ” of which fear may be a strong component . If both groups stated that fear motivated them at least to some extent on their choice whether or not to have surgery , what were the factors that enabled the acceptors of surgery to overcome their own fears and receive surgery ? When first asked in an open-ended fashion why others in “general” do have surgery , significantly more acceptors spontaneously responded that people feared losing their vision if they did not receive the surgery . When provided this response as a close ended question , 97% in both groups responded that this was a reason for surgery , although the non-acceptors were less likely to identify it on their own . We suggest that the issue of seeing better after/going blind without surgery was more on the forefront of acceptors’ minds , and was greater than the fear of the surgery itself . This difference in perceived benefits could also indicate a motivating factor for acceptors , and might suggest that more efforts need to be placed on education so that those resistant to having surgery fully understand the consequences of their disease . One limitation of our study is that severity of TT was not measured . That data would be useful to understanding the differential response to the fear of losing vision as a consequence of not having surgery . Our finding that non-acceptors were more likely to report they could manage their TT themselves with epilation may also indicated less severe TT in this group compared to the acceptors . We note that 26% of acceptors of surgery suggested that better education and advice about the surgery would help improve services . A 2015 study in Egypt that sought methods of increasing surgical uptake demonstrated that community-based eye education programs greatly increased the uptake of TT surgery among residents [16] . In our study , a high proportion of both acceptors and non-acceptors had knowledge of what eventually happens to eyes with TT , but the fear of losing vision was a stronger motivator among the acceptors . How to translate the knowledge into action appears to be crucial . Certainly better knowledge about what the surgery actually entails would allow patients to not only understand the course of their disease , but have a better understanding of the treatment . Acceptors suggested the use of successful surgery patients as ambassadors in villages in order to explain to patients the process of surgery , including how they ( the ambassadors ) were able to overcome their personal fears of having surgery . Though this finding may be seen as self-serving , successful surgery patients could be strong voices to help convince non-acceptors to receive surgery; hesitant patients may be more willing to trust fellow villagers as opposed to outsiders to their community . Cataract surgery motivators from within the community have been used in India in previous studies [17 , 18] . Another study in India that examined barriers to surgery for TT also recommended the use of successful patients sharing their experiences with other afflicted community members , in addition to making educational materials more available [12] . As non-acceptors report presenting frequently to local health posts , these sites could be suitable as intervention points for public health education regarding the disease of TT and its surgical treatment . Because of a lack of concern among participants for the quality or efficacy of the surgery , efforts to increase uptake must center on what constitutes fear and how it can be overcome , as opposed to convincing residents of surgeons’ quality or proficiencies . Having local villagers who have undergone successful surgery present and share their stories may go a long way in convincing residents who were previously afraid of surgery . Misinformation around the surgery adds to perceived barriers . Non-acceptors stated the cost of surgery and the location of surgery were both barriers , despite the surgery being offered for free and despite transportation being provided to surgery sites . Some of these responses were due to the higher proportion of newcomers to the village among non-acceptors , and so they would naturally be less likely to have been present or had the information when the surgical camps occurred . Nevertheless , a high proportion of those who had surgery said they heard this was a barrier in their communities , indicating a problem of misinformation . There is a new initiative in Kongwa to treat TT surgery with camps , so as more camps occur , this problem should decline with proper advance communication to residents . Clearly , more efforts should be spent on spreading knowledge of upcoming dates of the surgical camps , including publicizing that they are free of charge and that transport will be provided . Not only will residents better understand where the camps are occurring and at no cost to them , but patients with TT will be able to prepare by finding others to help manage childcare and/or farm and work responsibilities along with someone to help accompany them for their surgery . Outcomes studies are in agreement with the perception that patients who underwent TT surgery have their symptoms alleviated and avoid vision loss , and others have shown improvements in quality of life [19–22] . Additionally , women tend to be more affected by TT than men , since women traditionally care for and therefore gain more exposure to infected children [7 , 23] . Overall , 79% of our study population was female , although there was no evidence of gender bias in who accepted or did not accept surgery . A study in Niger examined how TT affected the quality of life of women and found that the associated sharp pain in the eyes , embarrassment and stigma from friends , family , and their communities , as well as an inability to travel , work , and earn an income were all significant negative effects of having the disease [13] . Increasing the uptake of TT surgery will have the added benefit of reducing gender-based health disparities in endemic communities , in addition to reducing the burden of TT . There are other limitations to our study . While most residents that were approached agreed to participate , 12 ( 6 . 7% of total residents encountered ) refused to be interviewed . All who refused had not received TT surgery , introducing potential bias , since their reasons for non-acceptance of surgery may be different than the reasons of those who did agree to be interviewed . While we cannot estimate this , the fact that the refusal rate was only 6 . 7% suggests bias would be minimal . We had specific hypotheses about the barriers in each of the domains , but recognize that some significant associations may be based on chance alone . Our findings are bolstered by the consistency between the open- and close-ended responses . There may be differences by community as well , in strength of education about TT , cultural beliefs about surgery and TT , or other factors . We surveyed too few persons with TT in each village to adjust by village , but we tried to balance this shortage by including in our study acceptors and non-acceptors in each village . There is a limitation in generalizing our findings beyond the experience in Kongwa District , Tanzania . However , some of our findings are very similar to other reports from Ethiopia , Nigeria , The Gambia , India , and other districts in Tanzania [7 , 10 , 12–14] , suggesting the usefulness of applying our findings to other settings . Our study had several advantages over previous studies . First , we used a variety of approaches to elicit responses , which when concordant strengthened the likelihood the responses were valid . Non-acceptors may be reluctant to admit true reasons for not having surgery , and by asking them to report for people in “general” lessoned the discomfort in responding . This indirect method of interviewing allowed for participants to answer as honestly as possible while trying to minimize any hesitancy at being candid . In addition , the fact that we interviewed participants at home and used experienced female interviewers who spoke the local languages allowed an environment in which participants could speak as freely as possible . Second , we elicited responses from both those who did and did not have surgery among those who were eligible for surgery in order to determine the differences that could be drivers of non-acceptance . Most previous studies have confined questions to those who did not accept surgery only or do not differentiate between those who accept and did not accept surgery [5 , 7 , 10–14] . Our comparison could then focus on barriers that are more unique to non-acceptors rather than those that are overall barriers . Our study on barriers to surgery suggests that eliminating blinding trachoma will require addressing how surgery is presented to community residents , including outlining logistics of treatment , surgical outcomes , follow-up plans , as well as alternatives and the natural course of non-treated TT . Fear of surgery is a major barrier facing both TT patients who did and did not have surgery , not just non-acceptors . We noticed a reluctance among participants to admit their true feelings toward surgery , and future studies of barriers to care must be sensitive to this . One idea worth further exploration is having successful surgical patients serve as ambassadors in their communities to explain the benefits and process of surgery to others with TT .
|
The burden of trachomatis trichiasis ( TT ) , a complication of trachoma , is largest in the poorest societies of our world . One part of the World Health Organization ( WHO ) strategy to eliminate blinding trachoma is surgery for patients with TT , and trachoma programs in endemic countries have worked to increase the availability of surgical services . However despite these efforts , uptake of surgery remains low . In this study , we sought to identify the barriers that prevent TT patients from receiving sight-saving surgery . We interviewed residents with TT who did and did not have surgery . We found that fear was the biggest barrier among all participants , not just those who did not have surgery . But despite this fear , those who received surgery reported more often a fear of losing further vision had they not had surgery , highlighting that an understanding of the visual loss with TT is a possible motivating factor . Barriers to surgery included concern for going unaccompanied , and the perception that self-management was sufficient . Eliminating trachoma requires refining how surgery and education about the disease are presented to residents of endemic communities , including outlining treatment logistics , surgical outcomes , and the natural course of untreated TT .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"and",
"health",
"sciences",
"tropical",
"diseases",
"geographical",
"locations",
"social",
"sciences",
"neuroscience",
"surgical",
"and",
"invasive",
"medical",
"procedures",
"bacterial",
"diseases",
"tanzania",
"eye",
"diseases",
"neglected",
"tropical",
"diseases",
"vision",
"eyes",
"africa",
"blindness",
"infectious",
"diseases",
"head",
"pediatric",
"surgery",
"people",
"and",
"places",
"psychology",
"visual",
"impairments",
"anatomy",
"ophthalmology",
"biology",
"and",
"life",
"sciences",
"ocular",
"system",
"sensory",
"perception",
"trachoma",
"cataract",
"surgery",
"ophthalmic",
"procedures"
] |
2017
|
Identifying Patient Perceived Barriers to Trichiasis Surgery in Kongwa District, Tanzania
|
The availability of a diagnostic test to detect subclinical leprosy cases is crucial to interrupt the transmission of M . leprae . In this study we assessed the minimum sensitivity level of such a ( hypothetical ) diagnostic test and the optimal testing strategy in order to effectively reduce the new case detection rate ( NCDR ) of leprosy . We used the individual-based model SIMCOLEP , and based it on previous quantification using COLEP data , a cohort study of leprosy cases in Bangladesh . The baseline consisted of treatment with Multidrug therapy of clinically diagnosed leprosy cases , passive case detection and household contact tracing . We examined the use of a leprosy diagnostic test for subclinical leprosy in four strategies: testing in 1 ) household contacts , 2 ) household contacts with a 3-year follow-up , 3 ) a population survey with coverage 50% , and 4 ) a population survey ( 100% ) . For each strategy , we varied the test sensitivity between 50% and 100% . All analyses were conducted for a high , medium , and low ( i . e . 25 , 5 and 1 per 100 , 000 ) endemic setting over a period of 50 years . In all strategies , the use of a diagnostic test further reduces the NCDR of leprosy compared to the no test strategy . A substantial reduction could already be achieved at a test sensitivity as low as 50% . In a high endemic setting , a NCDR of 10 per 100 , 000 could be reached within 8–10 years in household contact testing , and 2–6 years in a population testing . Testing in a population survey could also yield the highest number of prevented new cases , but requires a large number needed to test and treat . In contrast , household contact testing has a smaller impact on the NCDR but requires a substantially lower number needed to test and treat . A diagnostic test for subclinical leprosy with a sensitivity of at least 50% could substantially reduce M . leprae transmission . To effectively reduce NCDR in the short run , a population survey is preferred over household contact tracing . However , this is only favorable in high endemic settings .
Leprosy is an infectious disease caused by Mycobacterium leprae , affecting the skin , peripheral nerves , the mucosa of the upper respiratory tract and the eyes [1] . The most likely route of transmission of M . leprae is via the aerosolic route [2] . Individuals , who have close and frequent contact to a patient with leprosy , in particular within households , have the highest risk of acquiring the infection and developing leprosy [3 , 4] . Currently , the main strategy to control leprosy , as recommended by the World Health Organization ( WHO ) , is early detection of cases and treatment with multidrug therapy ( MDT ) [5] . Leprosy is diagnosed by clinicians based on the clinical signs and symptoms , along with the use of slit-skin smears and biopsies to respectively detect the presence of acid-fast bacteria and determine type of leprosy histologically [6] . Although the prevalence of leprosy has dropped immensely in the last 30 years , worldwide still more than 200 , 000 new cases of leprosy are detected annually [5] . This number has remained fairly stable over the last decade , indicating that transmission has not yet been interrupted . Global elimination has been a target since 1991 , and more recently the target for leprosy has been set to achieve zero transmission [7 , 8] . However , it is clear that the current strategy is not sufficient to achieve the goals within a reasonable time frame [9 , 10] . For this reason , alternative strategies should be considered . Previous modeling studies have shown that treating people during the subclinical stage of leprosy has a larger impact on the new case detection rate ( NCDR ) than early ( clinical ) diagnosis and treatment [11] . Therefore , interventions such as the provision of chemoprophylaxis ( antibiotics ) or immunoprophylaxis ( vaccination ) to contacts of leprosy cases could substantially further reduce the NCDR [12–14] . Nevertheless , these are not yet routinely available nor accepted . A more efficient approach would be the use of a diagnostic test that allows identification of M . leprae infected individuals who are at risk of developing leprosy and constitute the major source of transmission . The identification and validation of new sensitive biomarkers for M . leprae infection and ( subclinical ) leprosy is currently investigated in several leprosy endemic areas [15–19] . Host immunity after M . leprae infection is determined by host genetics , leading to a complex immuno-pathological spectrum associated with either dominant cellular or humoral immunity . The immune-mediated pathological leprosy spectrum compels detection of M . leprae infection to be based on multiple , diverse biomarkers specific for cellular as well as humoral immunity [15 , 19] . The use of serological proteomics can help to unravel the biological pathway in the immunomodulation of leprosy for diagnostic purposes [20] . In addition , pathogen-based approaches identifying the presence of M . leprae in skin smears of contacts and patients may offer additional tools for prophylactic targeting [21 , 22] . The availability of a specific and robust diagnostic test to detect infected individuals lacking clinical symptoms , would not only be beneficial to identify and treat cases at early stages before irreversible damage occurs but may also be crucial for breaking the transmission chain . As the prevalence of leprosy decreases , leprosy health care has been integrated into general health care causing decreased clinical expertise for diagnosing leprosy , leading to extended delays of diagnosis and as a result maintenance or re-emergence of infection . Implementation in general health care of a user- and field-friendly diagnostic test specific for leprosy may accommodate for the lack of leprologists in the field . In this study we aim to identify the minimum requirements of an as yet hypothetical diagnostic test for subclinical leprosy ( i . e . identifying infected individuals who will progress to disease ) and its potential impact on the NCDR . In order to predict the added value , non-linearity of infectious disease patterns in the transmission process needs to be taken into account . For this purpose , we will use the individual-based model SIMCOLEP , which models M . leprae transmission and control of leprosy in a population structured by households . The model has previously been used to estimate future NCDR trends in Bangladesh , India , Brazil , and Indonesia , and to test the impact of various interventions targeting household contacts [9–11 , 23 , 24] . We use SIMCOLEP to assess the impact of a diagnostic test on NCDR under various assumptions of sensitivity , ranging from 50 to 100% . Furthermore , we investigate the optimal strategy of using such a test: household contact testing or a population survey . As the impact of test strategies might be dependent on the endemicity level , all analysis were conducted in a high ( 25 per 100 , 000 ) , medium ( 5 per 100 , 000 ) as well as low ( 1 per 100 , 000 ) endemic setting [25] .
We used the individual-based model SIMCOLEP that simulates the spread of M . leprae in a population structured in households . It models life-histories of individuals , which are born and placed into households . Over time , individuals can create their own household or move to another household after marriage , during adolescence or after becoming a widow ( er ) . Deaths of individuals are determined by death rates at birth [23 , 26] . In the model , M . leprae transmission occurs when a susceptible individual has contact with an infectious individual . In the model , susceptibility of an individual to leprosy is randomly assigned . We assumed that 20% of the population is susceptible , implying that 80% will not develop leprosy , although the proportion of susceptibles is likely to be lower [1] . This assumption was made because a previous modeling study showed that assuming 20% susceptibles provided the best fit and that results did not significantly differed from assuming 5% or 10% susceptibles [23] . Two transmission processes are modeled separately: transmission in the general population and within-household transmission . The latter can be regarded as an additional probability of acquiring the infection if household contacts are infected . Infectivity is determined by the product of the contact rate , both in the general population and within households , and the probability of infection during a contact . An infected individual develops either paucibacillary ( PB ) or multibacillary ( MB ) leprosy , which is randomly determined based on the distribution of the type of leprosy . We assumed that only MB leprosy is infectious . After infection , an individual enters the asymptomatic state , which on average lasts 4 . 2 years for PB and 11 . 1 years for MB . Afterwards the individual proceeds to the symptomatic state , which lasts on average 5 years for PB leprosy after which self-healing occurs . An MB leprosy case remains symptomatic until treatment or death . The natural history of leprosy is modeled following Meima et al . [27] . The model also replicates control measures including treatment with multidrug therapy ( MDT ) , passive case detection , and household contact tracing . All detected cases receive MDT treatment , and will not be infectious from then on . Relapses occurred with a rate of 0 . 001 per year . 90% relapses to MB and 10% to PB . A full description of the model can be found in Fischer et al . 2010 and Blok et al . 2015 [9 , 23] . We used previously published quantifications of the model based on the population and leprosy epidemiology in Nilphamari and Rangpur districts in Bangladesh . A full description of the data used to parameterize the birth , household movements and deaths of individuals , and leprosy epidemiology can be found in Fischer et al . 2010 [23] . Transmission of M . leprae was previously calibrated using data from the COLEP study [4] . The COLEP study population consisted of contacts of 1037 consecutively found new patients with leprosy in Nilphamari and Rangpur districts in Bangladesh . The data contain information about the genetic distance ( i . e . kinship ) of almost 22 , 000 contacts and were used to fit the prevalence of cases among contacts of different household sizes , the prevalence of cases among different types of relatives in 2003 . Quantifications were not updated to more recent numbers , because a trial with chemoprophylaxis started afterwards . Our main focus was to estimate the impact of a diagnostic test for detection and subsequent effective treatment of M . leprae infected individuals progressing to disease , in addition to the mainstay control program , as recommended by the WHO [25] . The transmission contact rate in the general population and within household was set to 1 . 33 and 0 . 98 , respectively [23] . The baseline NCDR of Nilphamari and Rangpur was 27 per 100 , 000 and the MB/PB ratio 20/80 in 2003 [4] . The leprosy control program in the modeled area was well-organized [28] . It consisted of passive case detection with an annual detection delay of on average of 2 years ( standard deviation 1 . 4 years ) [29] and continuous household contact tracing with a coverage of 90% [23] . During contact tracing only clinical leprosy cases can be diagnosed . We further included the protective effect of BCG vaccination prior to the infection , which was set to 60% [13] . In order to reflect a high , medium and low endemic setting , we ran the model given the current control until it reached a NCDR level of 25 per 100 , 000 , 5 per 100 , 000 and 1 per 100 , 000 . This was obtained after 2 , 18 and 40 years , respectively . In different scenarios , we evaluated the impact of using a diagnostic test for subclinical leprosy on the NCDR trend under various sensitivity levels , ranging from 50% to 100% . We neglected lower levels of sensitivity , because it is not expected that such a diagnostic test will come on the market . Individuals testing positive will be treated with MDT [30] . We evaluated four testing strategies with a diagnostic test for subclinical leprosy in addition to the current leprosy control: All scenarios were compared to the no-testing strategy which only consisted of the current leprosy control program . Leprosy cases under treatment were excluded from testing . Testing compliance in individuals was assumed to be 100% . All strategies were evaluated in terms of its impact on the NCDR and its benefit and costs . The benefit of a testing strategy was measured by the number of prevented new leprosy cases calculated as the difference between the new cases ( detected and undetected ) of a testing strategy and the no-testing strategy . The costs of a testing strategy were measured in terms of number needed to test and treat . As tests that are not completely predictive may produce false positives , we assessed the number needed to treat given a test specificity of 100% , 95% and 85% . The impact on NCDR , and the benefits and costs of all strategies were evaluated in a high , medium and low endemic setting . The modeled time span was a 50-year period . In a sensitivity analysis , we varied the passive case detection delay and MB/PB ratio . First , we increased the passive case detection delay to six years , representing an area with a less well-organized leprosy control program in place . Second , we increased the MB/PB ratio to 65/35 , representing an area with relatively more MB than PB cases . The latter is relevant because we assume that only MB leprosy is contagious .
Fig 1 presents the impact of a diagnostic test used in household contacts without follow-up and with a 3-year follow-up , and a one-time population survey with a coverage of 50% and 100% on the NCDR under various assumptions of test sensitivities . In all strategies , the use of a diagnostic test further reduces the NCDR of leprosy compared to the no-testing scenario . Household contact testing without follow-up decreases the NCDR gradually over time with slightly larger effects at higher levels of test sensitivities . If household contacts are additionally followed-up for 3 years , the impact of lower test sensitivities increases to the level of the highest test sensitivity . In a population survey of 50% and 100% , a substantial reduction can be seen in the short run followed by a gradual decline afterwards . The impact varies with sensitivity levels of the test: a higher sensitivity level of the test corresponds with a larger impact on the NCDR . In the short run , both population survey testing strategies result in a larger decrease in the NCDR than household contact testing . In a population survey with a coverage of 100% , the NCDR could be 40% lower within 10 years with a test sensitivity of at least 60% ( See S1 Fig ) . In the long run , the impact of household contact testing on NCDR may be larger than that of a population survey , depending on the coverage and test sensitivity . In a high endemic setting , a NCDR of 10 per 100 , 000 could be reached within ten and eight years in household contact testing without and with follow-up , respectively . In a population survey testing strategy with a coverage of 50% and 100% this level could be reached within six and two years , respectively . The relative impact clearly varies with endemicity level with the high endemic setting showing the largest relative decrease , followed by medium and low endemic setting . The number needed to test in order to prevent one new leprosy case decreases with level of sensitivity and increases with the endemicity level of the setting ( see Fig 2 ) . Household contact testing without and with follow-up require substantially fewer individuals to be tested to prevent one leprosy case compared to both population survey strategies . In a one-time population survey , testing in a high endemic setting requires the least number of people to be tested to prevent one new leprosy case . In a medium and low endemic setting this number is up to four and twenty-three times higher , respectively . Table 1 summarizes the benefits and costs of all testing strategies with an assumed test sensitivity of 70% in a population of 1 million after 10 years . The numbers of prevented cases are approximately up to ten times higher in a population survey than in household contact testing . The highest number of prevented cases could be achieved in a high endemic setting . However , testing in a population survey would require substantially more people to be tested and treated than household contact testing . If the test is not completely specific , the number needed to treat further increases as a result of an increased number of false positives . This increase is much larger in a population survey than household contact testing , as more people are tested in a population survey . The costs of household contact testing decrease with the level of endemicity , whereas the costs of a population survey do not differ much across endemic settings . S1 Table provides the results of our sensitivity analysis . The number of prevented new leprosy cases is smaller in a setting with a less well-organized leprosy control program compared to a well-organized setting ( i . e . Nilphamari and Rangpur districts in Bangladesh ) . In a setting with a high MB/PB ratio , more new leprosy cases can be prevented in a 10-year period . The number needed to test and treat is to a large extent comparable across all leprosy settings .
This paper assessed the impact of the use of a ( hypothetical ) diagnostic test for subclinical leprosy cases with a sensitivity that ranged between 50% and 100% on the NCDR in household contact testing and one-time population survey strategies . All strategies showed an additional reduction in the NCDR over time compared to the current control for all levels of sensitivity in a high , medium and low endemic setting . Testing in a population survey yields a higher impact on the NCDR in the short run compared to household contact tracing . In terms of prevented cases , a population survey is preferred over household contact testing . However , a population survey requires much more people to be tested and treated , especially in medium and low endemic setting , compared to household contact testing . Our findings indicate that a test with a sensitivity as low as 50% could already result in a significant reduction of the NCDR . This suggests that to reduce transmission the availability of a diagnostic test for subclinical cases is more important than the level of sensitivity , which is very promising . Moreover , the impact of a test with a low test sensitivity could be increased by repeating the test in individuals testing negative . We showed that a test with a sensitivity of 50% used in a household contact testing strategy with a 3-year follow-up could reach a similar impact as a test with a sensitivity of 100% . Testing in a population survey is most favorable to achieve short term reductions of NCDR in a high , medium and low endemic setting . The lower impact of household contact tracing compared to a population survey , especially in the short run , is primarily due to the limited exposure of the test that is confined to a household with an average size between 4 to 5 persons . A population survey would also result in a higher number of prevented new leprosy cases . However , it requires testing of many individuals and thereby commitment by the national leprosy control programs . Such an approach is only favorable and feasible in high endemic settings . Our results show that the numbers needed to test to prevent one new leprosy case in a medium and low endemic setting are approximately fourfold and twenty-threefold that of a high endemic setting , respectively . Although tests specificity is of less importance if we aim to reduce transmission of M . leprae , it is relevant for determining the optimal testing strategy . If a test is not completely specific , the number needed to treat may increase dramatically . As the number of people tested is higher in a population survey than in household contact testing , it would also produce many more false positives ( See Table 1 ) . A solution to reduce the number of false positive is to apply a two-step approach , whereby a second test with high specificity is added [30] . Our study also highlights that the target of zero transmission might be difficult to achieve , even with a well-organized control program and the use of a diagnostic test for subclinical cases . Over a 50-year period , our model predicted that the NCDR in the high , medium and low endemic setting would be at most reduced to very low levels: 0 . 10–0 . 50 , 0 . 01–0 . 10 , and 0 . 005–0 . 025 per 100 , 000 in a high , medium and low endemic setting , respectively ( Fig 1 ) . This is mainly the result of the combination of a relative long incubation period of leprosy and detection delay . Areas that reach very low levels of NCDR require continuous monitoring for many more years to prevent maintenance or even re-emergence of M . leprae . In this study we did not consider the impact of a test for asymptomatic infection for future NCDR , because such a test is only relevant if we assume that all infected individuals are infectious ( i . e . can infect other people ) . In our model , we assumed that only infected individuals who progress to MB leprosy are infectious , implying those who progress to PB leprosy and those who do not develop leprosy do not contribute to the transmission . This assumption was made because it is still poorly understood whether and to which extent asymptomatic infected are infectious . This study used previously published quantifications of the model based on the population and leprosy epidemiology in Nilphamari and Rangpur districts in Bangladesh . The advantage of using this quantification is that it was based on the COLEP study , which includes a large amount of very detailed information on this population , including data on the prevalence of cases among contacts of different household sizes and the prevalence of cases among different types of relatives [4] . The downside is that quantifications are based on 2003 data , which does not truly reflect current leprosy epidemiology . However , for the purpose of assessing potential impact of a diagnostic test this is less of an issue . The primary concern of this study is about the extent to which our results are generalizable to other regions or countries with leprosy in the world . The leprosy control program in the Nilphamari and Rangpur districts of Bangladesh is more extensive than usual . The relative short detection delay of two years and active household contact tracing with a coverage of 90% is not common in leprosy control programs in other regions or countries . For that reason , we conducted a sensitivity analysis in which we increased the passive case detection delay to six years . Results show that fewer leprosy cases were prevented ( S1 Table ) . The qualitative findings of our main results ( the importance of sensitivity and the impact and efficiency of household contact tracing versus population survey ) did not differ when implemented in a setting with a less well-organized control program . Another concern with respect to generalizability is the distribution of the MB/PB leprosy . In Bangladesh the MB/PB ratio is approximately 20/80 . Since we assumed in our model that only MB cases are infectious , the impact of a diagnostic test might differ in areas with strikingly different MB/PB ratios , such as in Brazil ( 65/35 ) or Indonesia ( 80/20 ) [5] . In a sensitivity analysis , we showed that in a setting with a MB/PB ratio of 65/35 , the benefit of a diagnostic test is larger compared to our main results ( S1 Table ) . This is because of the earlier detection of relative more MB cases . Finally , this study did not look into combining the use of a diagnostic test for subclinical leprosy with additional novel strategies , such as a chemoprophylaxis , as this is beyond the scope of this study . Earlier modeling studies have shown that providing contacts with a single-dose of rifampicin ( SDR ) chemoprophylaxis can reduce the transmission of leprosy over time [11 , 24] . It can be expected that adding chemoprophylaxis to our testing strategies would further reduce the transmission of leprosy , especially if the test sensitivity is not optimal . In that case , the contacts that were false negative might benefit from SDR .
We showed that a diagnostic test for subclinical leprosy could substantially reduce transmission in a high , medium and low endemic population . The test sensitivity influences the impact on transmission in a population survey , but even with levels as low as 50% a substantial reduction could be achieved . To effectively reduce the NCDR in the short run , a population survey is preferred over household contact tracing . However , this is only favorable in high endemic settings , as in medium and low endemic settings testing in a population survey requires many more people to be tested and treated to prevent one new leprosy case .
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The annual number of new leprosy cases has been stable in the past decade , indicating that transmission has not been yet been interrupted . As current control seems to be insufficient to bring down the number of cases , there is a need for novel tools to interrupt transmission . A diagnostic that permitted diagnosis of subclinical cases will likely be fundamental to achieve elimination and ultimately eradication . In this study we assessed the minimum sensitivity level of such a ( hypothetical ) diagnostic test and the optimal testing strategy in order to effectively reduce the new case detection rate ( NCDR ) of leprosy . We showed that a diagnostic test for subclinical leprosy could substantially reduce the NCDR in a high , medium and low endemic population . A significant impact could already be achieved at a test sensitivity level of 50% . To effectively reduce the NCDR in the short run , a population survey is preferred over household contact tracing . However , this is only favorable in high endemic settings , as in medium and low endemic settings testing in a population survey requires many more people to be tested and treated to prevent one new leprosy case .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion",
"Conclusions"
] |
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"humoral",
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2018
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Minimum requirements and optimal testing strategies of a diagnostic test for leprosy as a tool towards zero transmission: A modeling study
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To investigate the underlying mechanisms of T2D pathogenesis , we looked for diabetes susceptibility genes that increase the risk of type 2 diabetes ( T2D ) in a Han Chinese population . A two-stage genome-wide association ( GWA ) study was conducted , in which 995 patients and 894 controls were genotyped using the Illumina HumanHap550-Duo BeadChip for the first genome scan stage . This was further replicated in 1 , 803 patients and 1 , 473 controls in stage 2 . We found two loci not previously associated with diabetes susceptibility in and around the genes protein tyrosine phosphatase receptor type D ( PTPRD ) ( P = 8 . 54×10−10; odds ratio [OR] = 1 . 57; 95% confidence interval [CI] = 1 . 36–1 . 82 ) , and serine racemase ( SRR ) ( P = 3 . 06×10−9; OR = 1 . 28; 95% CI = 1 . 18–1 . 39 ) . We also confirmed that variants in KCNQ1 were associated with T2D risk , with the strongest signal at rs2237895 ( P = 9 . 65×10−10; OR = 1 . 29 , 95% CI = 1 . 19–1 . 40 ) . By identifying two novel genetic susceptibility loci in a Han Chinese population and confirming the involvement of KCNQ1 , which was previously reported to be associated with T2D in Japanese and European descent populations , our results may lead to a better understanding of differences in the molecular pathogenesis of T2D among various populations .
Type 2 diabetes ( T2D ) affects at least 6% of the world's population; the worldwide prevalence is expected to double by 2025 [1] . T2D is a complex disorder that is characterized by hyperglycemia , which results from impaired pancreatic β cell function , decreased insulin action at target tissues , and increased glucose output by the liver [2] . Both genetic and environmental factors contribute to the pathogenesis of T2D . The disease is considered to be a polygenic disorder in which each genetic variant confers a partial and additive effect . Only 5%–10% of T2D cases are due to single gene defects; these include maturity-onset diabetes of the young ( MODY ) , insulin resistance syndromes , mitochondrial diabetes , and neonatal diabetes [3]–[5] . Inherited variations have been identified from studies of monogenic diabetes , and have provided insights into β cell physiology , insulin release , and the action of insulin on target cells [6] . Much effort has been devoted to finding common T2D genes , including genome-wide linkage , candidate-gene , and genome-wide association studies ( GWAS ) . Whole-genome linkage scans have identified chromosomal regions linked to T2D; however , with the exception of regions 1q [7]–[13] and 20q , which have been repeatedly mapped , linkage results vary from study to study [14]–[19] . Candidate-gene studies have provided strong evidence that common variants in the peroxisome proliferator-activated receptor-r ( PPARG ) [20] , potassium inwardly-rectifying channel J11 ( KCNJ11 ) [21]–[23] , transcription factor 2 isoform b ( TCF2 ) [24] , [25] , and Wolfram syndrome 1 ( WFS1 ) [26] genes are associated with T2D . These genes all have strong biological links to diabetes , and rare , severe mutations cause monogenic diabetes . GWAS have accelerated the identification of T2D susceptibility genes , expanding the list from three in 2006 to over 20 genes in 2009 . There are now at least 19 loci containing genes that increase risk of T2D , including PPARG [27] , KCNJ11 [27] , KCNQ1 [28] , [29] , CDKAL1 [27] , [29]–[33] , CDKN2A-2B [27] , [32] , [33] , CDC123-CAMK1D [34] , MTNR1B [35]–[37] , TCF7L2 [31] , [38] , [39] , TCF2 ( HNF1B ) , HHEX-KIF11-IDE [27] , [32] , [33] , [38] , JAZF1 [34] , IGF2BP2 [27] , [29] , [32] , SLC30A8 [27] , [32] , [33] , [38] , THADA [34] , ADAMTS9 [34] , WFS1 [26] , FTO [27] , [31] , NOTCH2 [34] , and TSPAN8 [34] . Variants in these genes have been identified almost exclusively in populations of European descent , except for KCNQ1; individually , these variants confer a modest risk ( odds ratio [OR] = 1 . 1–1 . 25 ) of developing T2D . KCNQ1 was identified as a T2D susceptibility gene in three GWA scans in Japanese individuals , highlighting the need to extend large-scale association efforts to different populations , such as Asian populations [28] , [29] , [40] . The association of other previously reported loci ( CDKAL1 , CDKN2A-2B , IGF2BP2 , TCF7L2 , SLC30A8 , HHEX , and KCNJ11 ) with T2D were also replicated in the Japanese population [29] , [40] , [41] . To date , a GWA scan for T2D has not been conducted in the Han Chinese population , although the association of some known loci have been confirmed , including KCNQ1 and CDKAL1 , CDKN2A-2B , MTNR1B , TCF7L2 , HNF1β , and KCNJ11 [42]–[47] . Therefore , we conducted a two-stage GWA scan for T2D in a Han Chinese population residing in Taiwan . There were a total of 2 , 798 cases and 2 , 367 normal controls ( 995 cases and 894 controls in stage 1 , 1 , 803 cases and 1 , 473 controls in stage 2 ) . Our accomplished objective was to identify new diabetes susceptibility loci that were associated with increased risk of T2D in a Han Chinese population .
We conducted a two-stage GWAS to identify genetic variants for T2D in the Han-Chinese residing in Taiwan . In the first stage , an exploratory genome-wide scan , we genotyped 995 T2D cases and 894 population controls using the Illumina Hap550duov3 chip ( Figure 1 and Table S1 ) . For each sample genotyped in this study , the average call rate was 99 . 92±0 . 12% . After applying stringent quality control criteria , high-quality genotypes for 516 , 737 SNPs ( 92 . 24% ) were obtained , with an average call rate of 99 . 92±0 . 24% ( Table S2 ) . The results of principal component analysis in stage 1 revealed no evidence for population stratification between T2D cases and controls ( P = 0 . 111 , Fst statistics between populations <0 . 001 ) ( Text S1; Figure S1 ) . Multidimensional scaling analysis using PLINK [48] produced similar results ( Text S1; Figure S2 ) . Furthermore , genomic control ( GC ) with a variance inflation factor λ = 1 . 078 ( trend test ) did not substantially change the results of this GWAS ( Table S3 ) . We selected eight SNPs in seven regions: rs9985652 and rs2044844 on 4p13 , rs7192960 on 16q23 . 1 , rs7361808 on 20p13 , rs1751960 on 10q11 . 23 , rs4845624 on 1q21 . 3 , rs391300 on 17p13 . 3 , and rs648538 on 13q12 . 3 . These SNPs had association P values of <10−5 at stage 1 with any of the genotype , allele , trend , dominant , and recessive models for subsequent cross-platform validation using Sequenom ( Table 1; Table S3 ) . For SNPs with weaker associations ( P value between 10−4 and 10−5 ) , we searched for novel susceptibility candidates for T2D as implicated by ( 1 ) gene function identified by a bioinformatics approach and ( 2 ) an animal model showing defects in glucose homeostasis caused by genes within the same subfamily . Therefore , we selected SNP rs17584499 ( P = 2 . 4×10−5 under best model ) for further investigation . rs17584499 lies within protein tyrosine phosphatase receptor type D ( PTPRD ) . We hypothesized that PTPRD might play a role in the regulation of insulin signaling , because its subfamily members leukocyte common antigen-related ( LAR ) and protein tyrosine phosphatase sigma ( PTPRS ) exhibit defects in glucose homeostasis and insulin sensitivity in knockout and/or transgenic mice [49]–[51] . We also evaluated the most significant SNP ( rs231361 ) within KCNQ1 , which was previously reported to be a diabetes susceptibility gene in a Japanese population , as well as in populations of Korean , Chinese , and European ancestry [28] , [29] . Together , these ten SNPs—the 8 SNPs with association p<10−5 , rs17584499 , and rs231361—were cross-platform validated and yielded consistent results using both Illumina and Sequenom . The concordance rate for stage 1 samples typed on the Illumina and Sequenom platforms was 99 . 1%±0 . 84% ( Table S4 ) . We took these ten SNPs and an additional 29 neighboring SNPs within the linkage disequilibrium ( LD ) block forward to replicate in 3 , 803 additional samples ( stage 2; 1 , 803 cases and 1 , 473 controls ) . The average call rate for each sample was 96 . 13%±4 . 66% . After applying stringent quality control criteria , high-quality genotypes for 35 SNPs ( 89 . 7% ) were obtained , with an average call rate of 98 . 96%±0 . 24% ( Table S2 ) . Of the ten SNPs selected in stage 1 , only three SNPs still showed a strong association in the stage 2 analysis: rs17584499 in PTPRD at 9p24 . 1-p23 , rs231359 in KCNQ1 at 11p15 . 5 , and rs391300 in serine racemase ( SRR ) at 17p13 . 3 ( Table 1 ) . We were unable to replicate the association between T2D and the remaining seven SNPs in ATP8A1/GRXCR1 , MAF/WWOX , SIRPA , LYZL1/SVIL , RORC/TMEM5 , and KATNAL1 in the stage 2 analysis ( Table 1 ) . Joint analysis of stage 1 and stage 2 data revealed consistent results with stage 2 . The most significant associations were found for rs391300 , rs17584499 , and rs231359 ( Table 1; Figure 2 ) . These associations remained significant after calculating P values using 108 permutations of the disease state labels . Joint association analysis was performed with all of the 2 , 798 T2D cases and 2 , 367 controls; this could achieve a power of 0 . 85 to detect a disease allele with a frequency of 0 . 15 and an OR of 1 . 5 , assuming a disease prevalence of 0 . 06 , at a significant level of 0 . 05 ( Table S5 ) . Two previously unknown loci were detected in our joint analysis of GWAS data . The strongest new association signal was found for rs17584499 in intron 10 of PTPRD ( P = 8 . 54×10−10 [trend test]; allelic OR = 1 . 57 , 95% confidence interval [CI] = 1 . 36–1 . 82 ) ( Table 1; Figure 2 ) . The second strongest signal was found with rs391300 ( P = 3 . 06×10−9 [trend test]; OR = 1 . 28 , 95% CI = 1 . 18–1 . 39 ) . The nearby SNP rs4523957 also demonstrated a significant association ( P = 1 . 44×10−8; OR = 1 . 27 , 95% CI = 1 . 17–1 . 38 ) . SNPs rs391300 and rs4523957 were in tight LD with one another ( r2 = 0 . 942 in HapMap HCB ) , and were located within the serine racemase gene ( SRR ) . SNP rs231361 , located in intron 11 of KCNQ1 , had a less significant association with T2D , and was selected in stage 1 ( P = 1 . 49×10−4 [trend test]; OR = 1 . 39 , 95% CI = 1 . 17–1 . 64 ) ( Table 1 ) . We further genotyped eight additional SNPs within the same LD block from the HapMap Asian group data: rs231359 yielded a P value of 4 . 56×10−4 with a trend test ( OR = 1 . 36 , 95% CI = 1 . 14–1 . 61 ) ( Figure 2 ) . rs231361 and rs231359 were in strong LD with one another ( r2 = 1 in HapMap HCB ) , and were located approximately 164 kb upstream of SNP rs2237897 , which was previously reported to be significantly associated with T2D in a Japanese population [28] , [29] . We took rs231361 , rs231359 , and neighboring SNPs within the LD block forward to replicate in stage 2 . Joint analysis of stage 1 and stage 2 data revealed that rs231359 had an even stronger association with T2D than did rs231361 ( rs231359: P = 3 . 43×10−8 , OR = 1 . 33 , 95% CI = 1 . 2–1 . 48; rs231361: P = 2 . 89×10−7 , OR = 1 . 3 , 95% CI = 1 . 18–1 . 44 ) . Additional SNPs that were reported to be significantly associated with T2D in a Japanese population were further genotyped [28] , [29] . The average call rate for each sample was 99 . 12%±7 . 21% . After applying stringent quality control criteria , we obtained high-quality genotypes with an average call rate of 99 . 16%±0 . 18% ( Table S2 ) . SNP rs2237895 showed the strongest association with T2D of all the genotyped SNPs in KCNQ1 ( P = 9 . 65×10−10; OR = 1 . 29 , 95% CI = 1 . 19–1 . 40 ) ( Figure 2 and Figure S3; Table S6 ) . Conditioning on the rs2237895 , the statistical significance of rs231361 ( or rs231359 ) disappeared . It seems the same underlying biological effect between the 2 SNPs ( Table S7 ) . Subsequently , we sequenced all of the exons , intron–exon boundaries , and up to 1 . 2 kb of the promoter region of the KCNQ1 gene in 50 individuals with T2D , and identified 42 polymorphic variations , including one nonsynonymous P448R polymorphism and two novel SNPs with minor allele frequency >0 . 03 . We then genotyped the two novel SNPs and one nonsynonymous polymorphism; however , none of these SNPs showed an association with T2D ( Table S6 ) .
Our GWAS for T2D in a Han Chinese population found two previously unreported susceptibility genes . All of the significant variants detected in our study showed modest effects , with an OR between 1 . 21 and 1 . 57 . Two loci with less-significant associations in our primary scan ( stage 1 ) , PTPRD and KCNQ1 , were selected for further replication; both showed compelling evidence of association in joint analysis . The susceptibility loci we identified in this study need to be further replicated in additional populations . Of the 18 loci previously reported to be associated with T2D ( with the exception of KCNQ1 ) , none of the P values for any of the SNPs within or near the genes reached 10−5 using allele , genotype , trend , dominant , or recessive models ( Table S8; Figure S4 ) . Three SNPs within CDKAL1 , JAZF1 , and HNF1B had the lowest P values , ranging from 5×10−4 to 10−5 , among the 18 known loci ( Table S8 ) . No significant associations were found within these regions in our Han Chinese population . The strongest new signal was observed for rs17584499 in PTPRD . The overall Fst among 11 HapMap groups for rs17584499 was estimated to be 0 . 068 [52] , which indicated a significant difference in allele frequencies among the populations ( P<0 . 0001 , chi-square test ) ( Table S9 ) . PTPRD is widely expressed in tissues , including skeletal muscle and pancreas , and is expressed highest in the brain . PTPRD-deficient mice exhibit impaired learning and memory , early growth retardation , neonatal mortality , and posture and motor defects [53] . Multiple mRNA isoforms are expressed by alternative splicing and/or alternative transcription start sites in a developmental and tissue-specific manner [54] , [55] . PTPRD belongs to the receptor type IIA ( R2A ) subfamily of protein tyrosine phosphatases ( PTPs ) . The R2A PTP subfamily comprises LAR , PTPRS , and PTPRD . The R2A family has been implicated in neural development , cancer , and diabetes [56] . Although the complex phenotype including neurological defects seen in knockout mice could obscure the roles of these genes in glucose homeostasis , LAR- and PTPRS-deficient mice were demonstrated to have altered glucose homeostasis and insulin sensitivity [49]–[51] . Transgenic mice overexpressing LAR in skeletal muscle show whole-body insulin resistance [57] . Because R2A subfamily members are structurally very similar [54] , PTPRD could play a role in T2D pathogenesis and should be further characterized . The second new association locus was found for rs391300 and rs4523957 in the biologically plausible candidate gene SRR . SRR encodes a serine racemase that synthesizes D-serine from L-serine [58] , [59] . D-serine is a physiological co-agonist of the N-methyl D-aspartate ( NMDA ) class of glutamate receptors , the major excitatory neurotransmitter receptors mediating synaptic neurotransmission in the brain [60] , [61] . NMDA receptor activation requires binding of glutamate and D-serine , which plays a neuromodulatory role in NMDA receptor transmission , synaptic plasticity , cell migration , and neurotoxicity [62] . D-serine and SRR are also present in the pancreas [63] . Glutamate signaling functions in peripheral tissues , including the pancreas , and positively modulates secretion of both glucagon and insulin in pancreatic islets [64]–[66] . The nearby SNP rs216193 also showed significant association ( P = 2 . 49×10−6 ) ; this SNP resides 3 . 8 kb upstream from SRR , within Smg-6 homolog , nonsense mediated mRNA decay factor ( C . elegans ) ( SMG6 ) . rs216193 was in tight LD with rs391300 ( r2 = 0 . 942 in HapMap HCB ) . Based on their biological functions and the association results , neither SMG6 nor any of the nearby genes TSR1 , SGSM2 , MNT , and METT10D were compelling candidates for association withT2D . However , SRR was significantly associated with T2D; thus , we suggest that dysregulation of D-serine could alter glutamate signaling and affect insulin or glucagon secretion in T2D pathogenesis . rs7192960 also had a suggestive association with T2D ( P = 1 . 32×10−5; OR = 1 . 21 , 95% CI = 1 . 11–1 . 33 ) . This SNP which lies approximately 211 kb downstream of v-maf musculoaponeurotic fibrosarcoma oncogene homolog ( avian ) ( MAF ) and 170 kb downstream of WW domain containing oxidoreductase ( WWOX ) . WWOX is a tumor suppressor gene that spans the second most common human fragile site FRA16D [67] , [68] , and is disrupted in many tumors , including pancreatic carcinoma [67] , [69]–[73] . MAF encodes the transcription factor c-Maf , a member of the Maf family of basic-Zip ( bZip ) transcription factors . c-Maf is involved in development and differentiation of the lens [74] , [75] , kidney [76] , immune system [77] , adipose tissue [78] , and pancreas [79] . It is expressed in α cells of the pancreatic islets [80] , and is a strong transactivator of the glucagon promoter that regulates glucagon gene expression [80] , [81] . c-Maf is also associated with early-onset and morbid adult obesity [82] . Our GWAS revealed that KCNQ1 , which was previously reported to be associated with T2D in several populations , was also associated with T2D in a Han Chinese population residing in Taiwan . KCNQ1 encodes the pore-forming α subunit of a voltage-gated K+ channel ( KvLQT1 ) , which is involved in repolarization of the action potential in cardiac muscle [83] , [84] . Mutations in KCNQ1 cause long QT syndrome [85] , [86] and familial atrial fibrillation [87] . KCNQ1 is widely expressed , including in the heart , brain , kidney , liver , intestine , and pancreas [88]–[90] . It is also expressed in pancreatic islets , and blockade of the KvLQT1 channel stimulates insulin secretion in insulin-secreting INS-1 cells [91] . KCNQ1 knockout mice have cardiac dysfunctions [88] , [92] and enhanced systemic insulin sensitivity [93] . In our study , variants in the coding region did not show an association with T2D . The functional variant ( s ) could be located in the regulatory element of KCNQ1 , rather than in the coding region . We did not find an association between either CDKAL1 or IGF2BP2 and T2D , in contrast with the results described in a previous study [29] , nor did we find T2D associated with various other genes identified in populations of European descent . In conclusion , we identified two previously unknown loci that are associated with T2D in a Han Chinese population , and confirmed the reported association of KCNQ1 with T2D . The novel T2D risk loci may involve genes that are implicated in insulin sensitivity and control of glucagon and insulin secretion: PTPRD may participate in the regulation of insulin action on its target cells , while SRR variants may alter glutamate signaling in the pancreas , thus regulating insulin and/or glucagon secretion . Our study suggests that in different patient populations , different genes may confer risks for diabetes , which may lead to a better understanding of the molecular pathogenesis of T2D .
The study was approved by the institutional review board and the ethics committee of each institution . Written informed consent was obtained from each participant in accordance with institutional requirements and the Declaration of Helsinki Principles . A total of 2 , 798 unrelated individuals with T2D , age >20 years , were recruited from China Medical University Hospital ( CMUH ) , Taichung , Taiwan; Chia-Yi Christian Hospital ( CYCH ) , Chia-Yi , Taiwan; and National Taiwan University Hospital ( NTU ) , Taipei , Taiwan . All of the T2D cases were diagnosed according to medical records and fasting plasma glucose levels using American Diabetic Association Criteria . Subjects with type 1 diabetes , gestational diabetes , and maturity-onset diabetes of the young ( MODY ) were excluded from this study . For the two-stage GWAS , we genotyped 995 T2D cases and 894 controls in the first exploratory genome-wide scan ( stage 1 ) . In the replication stage ( stage 2 ) , we genotyped selected SNPs in additional samples from 1 , 803 T2D cases and 1 , 473 controls . The controls were randomly selected from the Taiwan Han Chinese Cell and Genome Bank [94] . The criteria for controls in the association study were ( 1 ) no past diagnostic history of T2D , ( 2 ) HbA1C ranging from 3 . 4 to 6 , and ( 3 ) BMI<32 . The two control groups were comparable with respect to BMI , gender , age at study , and level of HbA1C . All of the participating T2D cases and controls were of Han Chinese origin , which is the origin of 98% of the Taiwan population . Details of demographic data are shown in Table S10 . Genomic DNA was extracted from peripheral blood using the Puregene DNA isolation kit ( Gentra Systems , Minneapolis , MN , USA ) . In stage 1 , whole genome genotyping using the Illumina HumanHap550-Duo BeadChip was performed by deCODE Genetics ( Reykjavík , Iceland ) . Genotype calling was performed using the standard procedure implemented in BeadStudio ( Illumina , Inc . , San Diego , CA , USA ) , with the default parameters suggested by the platform manufacturer . Quality control of genotype data was performed by examining several summary statistics . First , the ratio of loci with heterozygous calls on the X chromosome was calculated to double-check the subject's gender . Total successful call rate and the minor allele frequency of cases and controls were also calculated for each SNP . SNPs were excluded if they: ( 1 ) were nonpolymorphic in both cases and controls , ( 2 ) had a total call rate <95% in the cases and controls combined , ( 3 ) had a minor allele frequency <5% and a total call rate <99% in the cases and controls combined , and ( 4 ) had significant distortion from Hardy–Weinberg equilibrium in the controls ( P<10−7 ) . Genotyping validation was performed using the Sequenom iPLEX assay ( Sequenom MassARRAY system; Sequenom , San Diego , CA , USA ) . In the replication stage ( stage 2 ) , SNPs showing significant or suggestive associations with T2D and their neighboring SNPs within the same LD block were genotyped using the Sequenom iPLEX assay . The neighboring SNPs in the same LD were selected from the HapMap Asian ( CHB + JPT ) group data for fine mapping the significant signal . T2D association analysis was carried out to compare allele frequency and genotype distribution between cases and controls using five single-point methods for each SNP: genotype , allele , trend ( Cochran–Armitage test ) , dominant , and recessive models . The most significant test statistic obtained from the five models was chosen . SNPs with P values less than a = 2×10−8 , a cut-off for the multiple comparison adjusted by Bonferroni correction , were considered to be significantly associated with the traits . The joint analysis was conducted by combining the data from the stage 1 and 2 samples . We also applied Fisher's method to combine P values for joint analysis . The permutation test was carried out genome-wide for 106 permutations , in which the phenotypes of subjects were randomly rearranged . For better estimation of empirical P values , the top SNPs were reexamined using 108 permutations . Each permutation proceeded as follows: ( 1 ) the case and control labels were shuffled and redistributed to subjects , and ( 2 ) the test statistics of the corresponding association test was calculated based on the shuffled labels . The empirical P value was defined as the number of permutations that were at least as extreme as the original divided by the total number of permutations . Detection of possible population stratification that might influence association analysis was carried out using principle component analysis , multidimensional scaling analysis , and genomic control ( Text S1 ) . Quantile–quantile ( Q–Q ) plots were then used to examine P value distributions ( Figure 3 and Figure S5 ) .
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Type 2 diabetes ( T2D ) is a complex disease that involves many genes and environmental factors . Genome-wide and candidate-gene association studies have thus far identified at least 19 regions containing genes that may confer a risk for T2D . However , most of these studies were conducted with patients of European descent . We studied Chinese patients with T2D and identified two genes , PTPRD and SRR , that were not previously known to be involved in diabetes and are involved in biological pathways different from those implicated in T2D by previous association reports . PTPRD is a protein tyrosine phosphatase and may affect insulin signaling on its target cells . SRR encodes a serine racemase that synthesizes D-serine from L-serine . Both D-serine ( coagonist ) and the neurotransmitter glutamate bind to NMDA receptors and trigger excitatory neurotransmission in the brain . Glutamate signaling also regulates insulin and glucagon secretion in pancreatic islets . Thus , SRR and D-serine , in addition to regulating insulin and glucagon secretion , may play a role in the etiology of T2D . Our study suggests that , in different patient populations , different genes may confer risks for diabetes . Our findings may lead to a better understanding of the molecular pathogenesis of T2D .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
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"genetics",
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"traits",
"genetics",
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"genomics/genetics",
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"disease",
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2010
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A Genome-Wide Association Study Identifies Susceptibility Variants for Type 2 Diabetes in Han Chinese
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Histone tails play an important role in nucleosome structure and dynamics . Here we investigate the effect of truncation of histone tails H3 , H4 , H2A and H2B on nucleosome structure with 100 ns all-atom molecular dynamics simulations . Tail domains of H3 and H2B show propensity of -helics formation during the intact nucleosome simulation . On truncation of H4 or H2B tails no structural change occurs in histones . However , H3 or H2A tail truncation results in structural alterations in the histone core domain , and in both the cases the structural change occurs in the H2A3 domain . We also find that the contacts between the histone H2A C terminal docking domain and surrounding residues are destabilized upon H3 tail truncation . The relation between the present observations and corresponding experiments is discussed .
Eukaryotic DNA is organized into nucleosomes [1] , in which about 150 bp of DNA are wrapped in left-handed superhelical turns around an octameric histone protein complex [2] . The histone octamer has a tripartite structure composed of a tetramer flanked by two H2A–H2B dimers . The Å resolution structure of the nucleosome core particle revealed interactions between the histone core , histone tails and DNA at atomic detail [2] . In the structure the four histone dimers ( two each of H3 , H4 , H2A and H2B ) are arranged about a two-fold dyad symmetry axis , which also intersects with the middle of the DNA fragment . Each of the histone proteins consists of a structured core and a unstructured tail domain . The core domains consist of three -helices ( , and ) , connected by short loops L1 and L2 and are composed mainly of basic residues , except for an acidic patch of H2A near the center of the nucleosome . All four histones have an N-terminal tail domain but only histone H2A has a long C-terminal tail with a large interface with the histone H3–H4 core domains . Positively-charged histone tails make specific interactions with negatively-charged DNA [2] . There are positions on the nucleosomal DNA at which histone residues make contact with DNA by hydrogen bond formation . The positions of DNA around the nucleosome ( also referred as super helical locations or SHLs ) are often described with respect to the position of the dyad , as shown in Fig . 1 . The nucleosomal organization of eukaryotic chromatin presents a physical barrier to DNA access and also acts as a repository of epigenetic marks controlling chromosomal behavior during different periods of the cell cycle [3] . Chromatin remodelling enzymes can read these epigenetic marks and use ATP to assemble , reposition or evict nucleosomes [4] , [5] . Several eukaryotic organisms employ isoforms of histone proteins to regulate DNA genomic access during different periods of the cell cycle [6] . Post-translational modifications of histones play a key role in the regulation of gene access in eukaryotes [7] , [8] . The majority of these modifications occur in the N-terminal extensions of the histones in the form of methylation , acetylation or phosphorylation of amino-acid residues [9] . A major challenge in chromatin research is to characterize the effect of tail modifications on nucleosome mobility and stability . Evidence suggests that the modifications may recruit chromatin-binding proteins [10] or may act as a switch between different chromatin states [11] . Mutation or deletion of tail domains has been shown to result in transient unwrapping of DNA near the edge of the nucleosome , variation in the rate of nucleosome sliding on DNA and variation in the rate of H2A–H2B dimer exchange in vitro [12] . Deletion of certain tails also prohibits the formation of condensed chromatin fiber [12] , [13] . Truncating the end of the H2A C terminal domain results in a 2 . 4 fold increase in the nucleosome sliding rate [12] . Also , a number of mutations in the H3–H4 histone fold region that lies close to the H2A C terminal extension , which has a large interface with H3–H4 tetramer , have been found to result in higher nucleosome mobility [12] , [14] , . Some of these mutations destabilize dimer-tetramer association to the extent that the histone octamer cannot be formed in vitro [16] . The above findings suggest that the destabilization of the H2A C terminal tail may affect nucleosome mobility by altering C terminus-DNA contacts or by modifying histone dimer-tetramer association at the interface with H3/H4 . Furthermore , a comparison between wild type and histone variant H2A . Z , an essential histone variant found in all higher eukaryotes with altered histone dimer-tetramer interaction [17] , has shown that regions of the H2A . Z sequence essential for biological activity are clustered at the H2A C terminus [18] . Although progress has been made [19] , it is not clear how tail modification is related to nucleosomal stability . An atomic level understanding of interactions within the nucleosome that explains experimental findings [12] , [20] upon tail truncation is lacking . Here , as a step towards this understanding , we report on a total of 800 ns of all-atom molecular dynamics simulation of intact and tail-truncated nucleosomes and examine the effect of tail truncation on nucleosome structure at atomic detail . We find that both histone H3 and H2A tail truncation destabilize nucleosome structure and that the destabilization involves the same domain of H2A in both cases . We also find that modified interactions at the H2A C terminal interface are related to nucleosome destabilization in a manner that may help explain experimental findings .
The starting structure of all molecular dynamics ( MD ) simulations was taken from the Å resolution crystal structure of the nucleosome core particle ( PDB ID: 1KX5 ) [2] . All simulations used the CHARMM27 force field [21] in the NAMD program [22] . The structures were immersed in a cubic box of TIP3P water molecules and there was at least Å of separation between the solute and the edge of the box . The system was then neutralized with ions and appropriate amount of NaCl was added to keep the systems at 150 mM salt concentration . Each solvated system contains about 200 , 000 atoms . Periodic boundary conditions were used and the long range electrostatics was treated with the particle mesh Ewald method [23] with a grid size of and sixth order interpolation to compute potential and forces between the grid points . For the van der Waals interactions a switching function was applied at Å and the cut-off was set to Å . The SHAKE algorithm [24] was used to constrain bonds containing hydrogen atoms . The integration time step was 2 fs and coordinates were saved every 1000 steps during the simulations . The pressure was kept constant at atmospheric pressure at sea level with the Nosé-Hoover Langevin piston pressure control [25] , [26] in NAMD . The temperature was maintained at 300 K with a Langevin damping coefficient of 2 . For each of the simulations , the water molecules and ions were first energy minimized and equilibrated at 300 K for 160 ps with the solute kept fixed . The whole system was then energy minimized for 10000 steps using the conjugate gradient method and keeping the positions of the protein backbone atoms fixed . Harmonic restraints on the backbone atoms were then relaxed stepwise during 30 ps heating and 160 ps equilibration of the whole system . It was observed that H3 and H2A tail truncation destabilizes the nucleosome structure . To verify these results 2 additional independent simulations of H3 tail-truncated nucleosome and 1 additional simulation of H2A tail-truncated nucleosome were performed . Each of the simulations was 100 ns long , making a total of 8 simulations and 800 ns of combined trajectory . On the Kraken Cray XT5 machine ( http://www . nics . tennessee . edu/computing-resources/kraken ) generating 1 ns of trajectory took approximately 1700 CPU hours on 504 cores generating about 50 gigabyte of raw data . The VMD [27] and PyMOL [28] program was used for visualization of the trajectories and preparation of most of the figures . The root mean square deviation ( RMSD ) of the trajectories was calculated using the GROMACS [29] program . The 3DNA program [30] was used for the calculation of DNA structural parameters . The electrostatic potential map was computed using the pmepot plugin in VMD with a grid resolution of 1 and Ewald factor value of 0 . 25 .
In the intact nucleosome simulation nucleosomal DNA remains more mobile than the histone fold regions ( Fig . 3A ) as also observed in the crystal structure [2] . In the histone fold region , amino acids from and domain of histone H2A show higher mobility . DNA phosphate atom mobility is minimal at histone-DNA contact points and is in good agreement with crystallographic B-factors in Supplementary Fig . S1 ( also see Text S1 ) . In the crystal structure of the nucleosome core particle ( 1KX5 . pdb ) histone tails adopt disordered conformations with many amino acids having occupancy zero . In the simulation these tail domains primarily remain bound to DNA with a low degree of ordering . The overall secondary structure conservation in histones is plotted in Fig . 3B which shows that amino acids 14–20 of histone H3 ( copy 1 ) and amino acids 11–15 of histone H2B ( copy 1 ) have propensity to form -helices . To examine the overall stability of the histones during the 100 ns intact nucleosome trajectory we calculated the RMSD of each histone ( excluding tails ) from the equilibrated structure ( Fig . 4A ) . The plot shows that histone core domains stay close to the equilibrated structure during the 100 ns long production simulation . To quantify the overall effect of tail truncation on the nucleosome structure , we define an order parameter , , as the ratio of truncated nucleosome root mean square deviation ( RMSD ) to the intact nucleosome RMSD averaged over the entire trajectory , i . e . implies destabilization of a histone domain . To illustrate how tail truncation affects nucleosome structure is plotted for each copy of the four histones over the 100 ns trajectories obtained from the four tail-truncated nucleosome simulations ( Fig . 4B ) . Tail truncation does not affect the stability of most of the histone monomers . However , when the H3 or H2A tails are truncated , one of the two copies of histone H2A exhibits a persistently increased RMSD relative to the other cases ( see the box in Fig . 4B ) . We call this monomer H2A ( 2 ) from now on . This indicates that H3 or H2A tail truncation destabilizes the nucleosome structure . Two further independent simulations of the H3 tail-truncated nucleosome confirmed the structural changes in H2A ( 2 ) upon H3 tail removal ( Fig . 5B upper panel ) . To locate the structural domain of the H2A monomer that is responsible for the increased RMSD , we computed for each structural domain of H2A ( 2 ) from the H3 tail-truncated simulation . The domain of H2A was found to exhibit an increase in the value of similar to that observed for the entire H2A monomer from the same trajectory ( Fig . 5B ) . The RMSD of the same domain also increases when the H2A tail is truncated ( Fig . 5C ) . The simulations reported here are non-equilibrium simulations each of which can follow a different pathway . However , we found that certain changes involving the H2A ( 2 ) 3 domain are common to all the simulations which gives statistical significance to our result . The differences in order parameters among replicate simulations in Fig . 5B and 5C are commented in the Discussion . Realistic parametrization of force fields for the nucleic acids has been a long-standing problem [31]–[33] and CHARMM22 force field is known to overstabilize A form of DNA [34] . Major shortcomings of the CHARMM22 force field have been overcome in CHARMM27 [31] , [32] which is used here . In Fig . 6 we plot the probability distribution for the phosphodiester backbone dihedrals from the intact nucleosome simulation and compare them with the distributions obtained from the crystal structure . The relative smoothness of the simulation distributions originates from the dynamics of the system that is not taken into account by the crystallographic average structure . Most of the structures show BI type ( ) conformation . Furthermore , the dihedral states present in the crystal structure also persist during the simulation . It is of interest to note that the above parameters , calculated using the CHARMM27 force field are in agreement with those obtained using the AMBER force field on the same molecule [19] . The DNA helical parameter fluctuations are also in good agreement with those extracted from the crystal structure ( Supplementary Fig . S2 ) . While analyzing the DNA dihedral parameters we did find that one nucleotide base ( Cyt49 ( J ) ) is unstacked and the neighboring bases ( Gua98 ( I ) -Cyt50 ( J ) ) show unusual ( non Watson-Crick ) base-pairing in the intact nucleosome simulation . However , this did not lead to any unusual fluctuation in the neighboring amino-acid residues . Furthermore , since the DNA backbone and helical parameter values derived from all other base pairs are in agreement with x-ray and simulation data , this may not be artefactual . DNA major and minor groove characteristics are plotted in Fig . 6B and are again in agreement with the crystal structure . The minor grooves exhibit a periodic variation of width with the minima corresponding to the base-pair steps contacting histone arginines that is of smaller amplitude than the crystal structure , again possibly due to the dynamics . A larger fluctuation in DNA groove width is observed for the base pair steps 1–40 ( chain I ) with the largest fluctuation near base-pair step 40 being caused by Arg11 of histone H2A ( 1 ) probing the DNA binding sites . In the intact nucleosome simulation the DNA RMSD stabilizes around 3 . 2 Å ( Supplementary Fig . S3 ) , which is a relatively small value for a system of large radius of gyration ( 50 Å ) . The effect of tail truncation on the nucleosomal DNA was also examined and it was found that only the H3 tail truncation affects DNA stability ( Fig . 7A ) . Further , independent simulations also confirmed DNA destabilization upon H3 tail truncation ( Fig . 7B ) . To determine to which segment of the DNA the increased RMSD corresponds , the nucleosomal DNA was divided into 14 segments based on the SHLs shown in Fig . 1 . The results indicate that H3 tail truncation destabilizes the segment of DNA between the dyad and SHL +1 . 5 ( Fig . 7A inset ) . We analyzed the alteration of residue interactions upon tail truncation for the H2A ( 2 ) domain and the H2A C terminal domain for the following reasons - i ) the order parameter plots showed destabilization of the H2A ( 2 ) domain and , ii ) it has been suggested that the H2A C terminus contacts with surrounding residues may play a key role in nucleosome stability [20] .
The aim of the present study was to provide atomic-level information on interactions within the nucleosome that are altered upon tail truncation . This was accomplished by multiple all-atom MD simulations of intact and tail-truncated nucleosomes , with each trajectory covering a time frame 5 times longer than previously-reported simulations comparing intact and tail-truncated nucleosomes [19] . Several independent simulations were performed , totalling 800 ns of combined trajectory . A comparison of the key results obtained from the 20 ns [19] and 100 ns trajectories are given in the Supplementary Table S1 . The formation of -helical structure in one of the H3 tails may be a result of specific histone-DNA interactions ( Fig . 3B ) . The starting structure for our simulations is the 1 . 9 Å crystal structure of the nucleosome core particle [2] in which residues from the two copies of the same type of histone tail domains have been assigned different order values . This affects the structural changes in the nucleosome during the simulation and it is likely that the asymmetric structural changes in histone H3 occur due to the chosen starting structure . Recent studies indicate that amino acid residues in this -helical domain are crucial post-translational modification sites , e . g . , Lys14 is an essential p300 acetylation substrate required for dissociation of the histone octamer from the promoter DNA [35] , and methylation at Arg17 is linked to gene activation [36] . Hyperacetylation of lysine residues neutralizes its positive charge by transferring an acetyl moiety onto the -amino group which reduces the lysine-DNA interactions . In the simulations similar change of electrostatic environment may arise from removal of lysine residues . This is indirectly realized in our H3 tail-truncated simulations as mentioned below: in the intact nucleosome simulation we observed -helix formation by residues 14–20 of H3 tail . Interestingly , Lys14 and Lys18 of this alpha-helix domain belong to the known acetylation sites of histone [37] . Thus , the removal of H3 tail ( residues 1–26 ) disrupts interactions between these lysines and the DNA in a way similar to acetylation of lysines and likely induces change of interaction in argines through the electrostatic mechanism described here ( Fig . 10 ) . The reason for observing interaction changes for one H2A monomer in H3 tail-truncated simulations is not clearly understood ( a comparison of interaction changes between histone monomers is provided in Supplementary Table S2 ) . We note that the structural changes were observed in the H2A monomer lying close to the H3 tail which showed a propensity for -helix formation during the intact nucleosome simulation . Thus , disruption of H3 tail -helix-DNA interaction is likely correlated with the structural changes in H2A . Alternatively , the effect may also be due to incomplete conformational sampling and similar structural changes would be observed in the other H2A monomer on longer time scale . On truncation of H2A tail , a similar change of interactions in the H2A docking domain is observed for both the H2A monomers in the molecule . However , this case is different from H3 tail-truncated simulations , because the effects observed here are likely due to truncation of H2A tails , whereas in other case it is likely due to disruption of -helix-DNA interactions in the simulation . The H2A docking domain provides the interaction surface between the histone H3–H4 tetramer and the H2A–H2B dimer ( Fig . 14A ) . Destabilization of the H2A docking domain is likely to weaken the dimer-tetramer interaction and affect nucleosome stability . Recent work has shown that the C-terminally truncated nucleosome is less stable and has higher mobility than the wild type H2A-containing nucleosome [20] , [38] , [39] . Since stability of the docking domain depends largely on the amino acids in close contact with this region , any disruption of the interactions with these amino acids would alter nucleosome stability . In the present simulations weakening of docking domain contact interactions ( Fig . 12 ) gives a possible explanation for increased histone dimer exchange rate upon H3 tail truncation [12] . Furthermore , the simulations provide insight into two possible mechanisms of destabilization of the docking domain ( Fig . 14 B–C ) . The direct mechanism involves altering the residues in contact with the docking domain , exemplified by alanine mutagenesis in vitro studies [12] . In a cellular environment direct alteration of H2A docking domain contacts might also be achieved by incorporation of histone variants , such as H2ABbd or H2A . Z , which differ in amino acid sequence from the canonical H2A . The alternative mechanism involves breaking of specific contacts between the H3 tail and the DNA , thus triggering destabilization of the H2A docking domain through changes of interaction of the arginines in the H2A domain . In the simulations we observe a correlation between the breaking of contacts of the H2A docking domain with close by amino acid residues and the change of interaction of Arg88 of the H2A ( 2 ) domain . Hydrogen bond formation between Arg88 ( H2A ) and Glu105 ( H3 ) is simultaneous with the breaking of contacts of Ile51 and Gln55 with the H2A docking domain . It is likely that the change of interaction of Arg88 also changes the electrostatic environment in the vicinity of the H2A docking domain and new polar contacts between Arg88 ( H2A ) , Glu105 ( H3 ) and Gln112 ( H2A ) are formed ( Fig . 14C ) . When the Arg88-Glu105 contact is not formed in the simulations ( H2A tail-truncated simulation 2 ) minimal loss of contact of Ile51 and Gln55 with the H2A docking domain is observed ( Fig . 13 ) . We also found that certain changes involving the alteration of sidechain hydrogen bonding of Arg88 are common to all the H3 and H2A tail-truncated simulations ( see Supplementary Fig . S4 ) . Replicate simulations in Fig . 5B and 5C follow different pathways between the beginning ( when Arg88 is hydrogen bonded to Asn94 , Gly98 and Val100 ) and the end ( when Arg88 is hydrogen bonded to Glu105 and Ala135 ) states which results in the differences in the order parameters in the plots . In Fig . 5C upper panel , the data seem different among replicates since the end state is different ( Arg88 is hydrogen bonded to Ala135 only ) in this case . It has been proposed that transient opening of outer turns of DNA facilitate nucleosome sliding by capturing loops on the nucleosome surface [40] , [41] and experimental work has reported that the DNA ends open up upon H3 tail deletion [12] , [42] . However , we do not observe opening of DNA ends in the present simulations . This is likely to arise from the fact that whereas in the experiments of Ref . [12] the nucleosomes were assembled onto 181 bp of DNA fragments derived from 601 . 3 strong nucleosome positioning sequence [43] , the structure simulated here consists of only 147 bp of nucleosomal DNA . Hence the present structure does not contain DNA ends extending into the solvent . In the simulations a segment of DNA between the dyad and SHL +1 . 5 is destabilized as a result of H3 tail truncation ( Fig . 6A inset ) . Truncation of the H3 tail ( amino acids 1–26 ) removes the hydrogen bonds between the tail residues and the DNA found in the crystal structure . Charged residues from remaining part of the H3 tail ( amino acids 27–45 ) then probe between possible DNA binding sites in the simulations which is observed as an increase the value of the order parameter for this part of the DNA . It has been proposed that histone core domain modifications may alter nucleosome mobility [7] . The allosteric change of interactions in the H2A domain and C terminal tail upon H3 and H2A tail truncation probed here may reveal potential ways in which dynamical properties of the nucleosome can be manipulated . Truncation of the C terminal tail affects the binding of ATP-dependent chromatin remodelling factors [20] , further suggesting an important role of this domain in nucleosome mobility . The H2A C terminus has also been found to be crucial for binding of the linker histone H1 to nucleosome [20] . Core domain modification , by providing marks for recruitment of chromatin binding proteins , have the potential to play a vital role in gene regulation .
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Histone tails are the most common sites of post-translational modifications . Tail modifications alter both inter and intra nucleosomal interactions to disrupt the condensed chromatin structure , thereby playing crucial role in gene access . Here we investigated histone tail functions on the stability of a single nucleosome in atomic detail by selectively truncating tail domains in molecular dynamics simulations . Our study revealed that truncation of H3 or H2A tail results in structural alterations in the nucleosome core whereas truncation of H4 or H2B tail does not . A potential role of H2A C terminal tail in regulating nucleosome stability is discussed . Finally , an -helical domain formation was observed in one of the H3 tails and , upon truncation of this tail , structural changes occurred in closely lying histone domains . The correlation between tail-truncation and structural changes likely sheds light on allosteric regulation of nucleosome stability .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"physics",
"biophysic",
"al",
"simulations",
"macromolecular",
"assemblies",
"biology",
"computational",
"biology",
"biophysics",
"simulations",
"biophysics"
] |
2011
|
Role of Histone Tails in Structural Stability of the Nucleosome
|
In an effort to suppress microbial outgrowth , the host sequesters essential nutrients in a process termed nutritional immunity . However , inflammatory responses to bacterial insult can restore nutritional resources . Given that nutrient availability modulates virulence factor production and biofilm formation by other bacterial species , we hypothesized that fluctuations in heme-iron availability , particularly at privileged sites , would similarly influence Haemophilus biofilm formation and pathogenesis . Thus , we cultured Haemophilus through sequential heme-iron deplete and heme-iron replete media to determine the effect of transient depletion of internal stores of heme-iron on multiple pathogenic phenotypes . We observed that prior heme-iron restriction potentiates biofilm changes for at least 72 hours that include increased peak height and architectural complexity as compared to biofilms initiated from heme-iron replete bacteria , suggesting a mechanism for epigenetic responses that participate in the changes observed . Additionally , in a co-infection model for human otitis media , heme-iron restricted Haemophilus , although accounting for only 10% of the inoculum ( 90% heme-iron replete ) , represented up to 99% of the organisms recovered at 4 days . These data indicate that fluctuations in heme-iron availability promote a survival advantage during disease . Filamentation mediated by a SulA-related ortholog was required for optimal biofilm peak height and persistence during experimental otitis media . Moreover , severity of disease in response to heme-iron restricted Haemophilus was reduced as evidenced by lack of mucosal destruction , decreased erythema , hemorrhagic foci and vasodilatation . Transient restriction of heme-iron also promoted productive invasion events leading to the development of intracellular bacterial communities . Taken together , these data suggest that nutritional immunity , may , in fact , foster long-term phenotypic changes that better equip bacteria for survival at infectious sites .
Nontypeable Haemophilus influenzae ( NTHI ) is an obligate commensal that asymptomatically colonizes the human nasopharynx . Permissive risk factors , which include upper respiratory tract viral infection and Eustachian tube dysfunction , influence NTHI migration from the nasopharynx to sterile sites of the upper and lower respiratory tract [1] , [2] . At these privileged sites , NTHI is a common causative agent of diseases such as otitis media ( OM ) , conjunctivitis , sinusitis , pneumonia , and exacerbates disease severity in patients with chronic obstructive pulmonary disease and cystic fibrosis [3]–[8] . As observed with many other chronic , recurring infections , NTHI-mediated otitis media results from the development of robust organized biofilms [9]–[12] . However , the host and bacterial elements that dictate the complexity and fitness of NTHI biofilms are not completely understood . As a mechanism to resist microbial outgrowth , the host tightly sequesters essential metals ( i . e . iron , zinc , manganese ) [13] . This process , termed nutritional immunity , results from the nearly universal requirement of specific host-derived nutrients for bacterial growth . One such molecule is the iron-containing metalloporphyrin , heme . Since NTHI lacks biosynthetic enzymes to generate protoporphyrin IX , the structural backbone of heme , import of an exogenous source of heme is necessary to support aerobic growth [14] . Low levels of heme-protein complexes can be released into serum upon erythrocyte lysis; however , free heme is quickly bound with albumin , hemopexin or haptoglobin molecules [15] . Heme sequestration ensures that the level of freely available iron within the host is far less than is required for microbial growth [16] , [17] . Yet , NTHI utilizes multiple mechanisms to efficiently acquire heme from host heme sequestration complexes [18]–[21] . Intracellular heme is incorporated into cytochromes for respiration , catalase for enzymatic neutralization of oxygen radicals , and is essential for sequestration of free iron to minimize oxygen radical production [22] . Progression of NTHI disease is coincident with microenvironmental changes associated with migration to heme-iron restricted privileged sites . Studies investigating the effects of iron-restriction upon bacteria have demonstrated a range of physiological responses including increased antibiotic resistance , virulence factor expression as well as induction of biofilm formation [23]–[26] . We and others have demonstrated that NTHI upregulates iron uptake systems in the middle ear and that NTHI deficient in iron acquisition are less fit to survive in this environment [27]–[30] . Further , loss of Sap transporter function , an inner membrane ABC transporter critical for heme-iron homeostasis , results in clearance of NTHI from the chinchilla middle ear and nasopharynx [29] , implicating a critical role for heme-iron acquisition in NTHI survival at these host sites . Moreover , loss of the SapF ATPase , required for energy dependent import of heme-iron , leads to the formation of biofilms with a lace-like architecture [31] , suggesting a role for heme-iron availability in modulation of biofilm structure . These data indicate that lack of available essential nutrients ( nutritional immunity ) may significantly contribute to the NTHI pathogenic lifestyle . The rapid transcriptional responses of planktonic NTHI following restoration of heme-iron have been described [32] . However , the consequences of transient heme-iron restriction on biofilm development and influences on disease progression have yet to be determined . We demonstrated that transient heme-iron restriction resulted in physiological changes that modified NTHI biofilm architecture , directly influenced by changes in bacterial morphology . These bacteria were primed for survival in the mammalian middle ear , due in part , to an observed reduction in host inflammation coinciding with a striking reduction in host mucosal epithelial damage , compared to the significant disruption in mucosal integrity observed in response to heme-iron replete NTHI . Our findings significantly advance our understanding of how host immune pressure and nutrient availability influence pathogenic behaviors that impact disease severity .
Scavenging and sequestration of heme-iron restricts the availability of this essential nutrient to bacteria . However , progression of disease coincides with increased inflammation and edema which , although also sequestered in host proteins , can provide a source of heme-iron to the bacterium . The consequence of these temporospatial fluctuations in heme-iron availability on NTHI virulence and persistence remains undetermined . We previously demonstrated that inactivation of the SapF ATPase , required for heme-iron import in NTHI , influenced biofilm architecture [31] . Whereas the use of engineered heme-iron deficient NTHI strains has contributed to the identification of a host microenvironmental cues that contribute to NTHI pathogenesis [31] , [33] , we sought to evaluate the effect of transient heme-iron restriction on wild type NTHI biofilm architecture and pathogenesis . To this end , we devised a culture-based system as a means to model temporospatial fluctuations of heme-iron availability during disease progression . Specifically , we cultured the prototypical NTHI strain , 86-028NP , in a defined medium in the absence or presence of heme-iron for 24 hours , to generate heme-iron ‘restricted’ and ‘replete’ individuals , respectively ( Fig . 1A ) . Restricted or replete NTHI were subcultured into a medium containing 2 µg heme mL−1 to generate transiently restricted and continuously exposed NTHI , respectively . After 48 hours of growth , NTHI continuously exposed to heme-iron formed biofilms with peaks that reached heights of up to 30 µm ( Fig . 1C ) . In contrast , we observed a difference in both frequency and height of peaks , extending up to 90 µm , following transient restriction of heme-iron ( Fig . 1E , D , F; Fig . S1A , B ) . Notably , the differences in biofilm architecture were not due to changes in viability , adherence to an abiotic surface or growth rates following heme-iron restriction ( Fig . S2A , B ) . To better define the differences in the biofilms observed between the two experimental conditions , we quantitatively evaluated the architecture of the biofilms . The biofilms were comprised of two structurally distinct components , namely the biofilm base and the peaks . The biofilm base was defined as the distance from the abiotic surface to the lowest valley of the peaks in each of 20 randomly selected fields of view and represents the consistent coverage of biomass . The peaks were measured from the glass surface to the highest biofilm-associated signal observed in the field of view . There was no difference in average height ( 10 µm ) of the base of the biofilms formed from transiently restricted or continuously exposed cultures ( Fig . 1B ) . Peaks were observed with a range of 10–30 µm in overall height ( Fig . 1B , C , D ) in continuously exposed cultures . However , we observed that transient heme-iron restriction resulted in the formation of taller mushroom-shaped stalks up to 90 µm in height ( Fig . 1B , E , F; p = 0 . 0003 ) . This phenotype was maintained for at least 72 hours ( Fig . S1C , D , E ) , suggesting that the transient restriction of heme-iron has long-standing physiological changes ( i . e . transmission to daughter cells ) that enhance the complexity and extent of biofilm development of NTHI . Thus , transient heme-iron restriction provokes developmental changes of biofilms grown on abiotic surfaces . We further investigated the propensity of heme-iron availability on biofilm architecture when grown on cultured chinchilla middle ear epithelial cells . We observed that transient heme-iron restriction resulted in the formation of biofilms with increased peak height ( Fig . 2A ) that range up to 84 µm in height as compared with a peak height up to 43 µm with heme-iron replete NTHI ( Fig . 2B; p<0 . 0001 ) . Therefore , the changes in biofilm architecture in response to heme-iron availability are observed on both abiotic and biotic surfaces . We next sought to assess the distribution of daughter cells within a biofilm formed from mixed cultures of transiently restricted and continuously exposed NTHI . In order to follow the lineage of each NTHI within the biofilm with regard to the heme-iron status at the time of biofilm initiation , we utilized two fluorescent reporter strains , one producing GFP and the other producing mCherry ( both under control of the constitutively expressed outer membrane porin P2 promoter ) [31] . Specifically , GFP-producing NTHI restricted of heme-iron for 24 hours were mixed in a 1∶1 ratio ( based upon colony forming units; CFU ) with mCherry-producing NTHI replete of heme-iron . Distribution of NTHI that arose from each culture condition was assessed following biofilm formation for 48 hours . We observed distinct segregation of the NTHI that originate from each culture condition . The biofilm peaks arose primarily from the transiently heme-iron restricted NTHI ( Fig . 3A , C ) . The phenotype was independent of the fluorescent reporter used for transient restriction ( Fig . 3B ) . Notably , equivalent numbers of each reporter strain were recovered at 48 hours of biofilm growth , indicating that the architectural differences observed were not due to changes in bacterial growth or viability ( Fig . 3D ) . The biofilm base arose primarily from the NTHI that was continuously exposed to heme-iron ( Fig . 3C ) . Given that the towers arose primarily from the transiently restricted NTHI , yet both reporter strains shared the same external environment , these data suggest that there is no transmission of phenotypes between NTHI of each origin . To further test this hypothesis , we co-cultured one transiently heme-iron restricted NTHI for every 1000 continuously heme-iron exposed NTHI ( 1∶1000 ) and assessed biofilm architecture and distribution of daughter cells . Again , we observed that the transient heme-iron restricted NTHI formed the towers of the biofilm regardless of which reporter strain experienced transient restriction , while the NTHI continuously exposed to heme-iron remained in the biofilm base ( Fig . 3E , F ) . These data indicate that transient heme-iron restriction primes NTHI for enhanced biofilm formation that is transmitted to daughter cells during bacterial community development in a manner that is independent of growth rate , adherence to a surface or a soluble signal . We next sought to determine the ramifications of physiological changes associated with fluctuations in heme-iron availability on persistence and disease in a model of human otitis media . To this end , the heme-iron restricted mCherry reporter strain was mixed with the heme-iron replete GFP reporter strain ( 1∶10 ) and inoculated ( total CFU = ∼2500 bacteria/ear ) via transbullar injection into the chinchilla middle ear . Viable bacteria were enumerated one and four days post infection by homogenization of middle ear mucosa and the associated biofilm . Despite comprising only 10% of the inoculum , the originally heme-iron restricted population strongly out-competed the heme-iron replete population when enumerated from middle ear mucosa homogenates with a mean of 65 percent of NTHI recovered on day one and 75 percent on day four ( with 3 of 5 ears over 90% ) ( Fig . 4A ) . This observation is in marked contrast to a similar experiment performed in vitro ( Fig . 3D ) , whereby there was no competitive advantage for either population . Taken together , these observations indicate the originally restricted NTHI population has a significant survival advantage under host immune pressure . The architecture of the bacterial biofilms formed in the middle ear during experimental otitis media was evaluated by hematoxylin and eosin staining of thin sections taken from fixed middle ears . We observed that the biofilms that fill the middle ear space are composed of bacteria and host immune cell infiltrate ( Fig . 4B; Bio ) . We compared the biofilm architecture within middle ears that were previously infected with heme-iron restricted NTHI , heme-iron replete NTHI , or in combination ( 1∶10 , respectively ) . We observed that prior heme-iron status resulted in changes in architecture during experimental OM . Within the middle ears that were infected with heme-iron restricted NTHI in combination with heme-iron replete NTHI , biofilms appeared to form an open lace-like architecture ( Fig . 4B ) . Similar biofilm architecture was observed in the middle ears infected with only heme-iron restricted NTHI ( Fig . 4D ) . In contrast , we observed condensed biofilm architecture within middle ears infected with heme-iron replete NTHI ( Fig . 4C ) . The observation that heme-iron restriction results in biofilms with a lace-like architecture in the middle ear ( Fig . 4B ) , combined with our previous observations [31] , suggested that these architectural changes may be related to heme-iron availability . To more closely examine the changes in biofilm infrastructure due to heme-iron restriction , we investigated individual bacterial interactions within the biofilm peaks in vitro . Microscopic evaluation at higher magnification revealed that biofilms derived from NTHI that were continuously exposed to heme-iron formed a dense , mat-like architecture , similar to that observed in vivo ( compare Fig . 4C and Fig . 5A ) . In fact , individual bacteria are tightly associated and difficult to distinguish within the biomass . In marked contrast , the biofilm towers derived from transiently heme-iron restricted NTHI formed an open , lace-like architecture , similar to that observed in vivo ( Compare Fig . 4D and 5B ) . The biofilm towers appeared to be constructed from strands of NTHI consisting of multinucleate filamentous morphotypes that interweave to form the lace-like architecture . Filamentous NTHI have been observed on the middle ear mucosa of chinchillas with experimental OM [34] . In addition , filamentation in other bacterial species has been shown to occur in response to environmental stressors [35] . Taken together with our observation of the filamentous NTHI within the biofilms , we hypothesized that the transition from a heme-iron restricted to a heme-iron rich microenvironment would induce the filamentous morphotype observed . In order to determine the specific contribution of the filamentous populations to biofilm architecture , we evaluated the outcome of seeding for biofilm formation with cultures enriched for the filamentous or bacillary morphotype . The filamentous population was significantly enriched by retention on a 5 µm pore filter ( Methods and Materials; [36] ) while the bacillary population was obtained from the culture that passed through the filter . Biofilms that arose from the bacillary population were reminiscent of the biofilms composed of NTHI subjected to continuous exposure to heme-iron ( Fig . 5C ) forming a confluent , mat-like architecture . In contrast , the biofilms that arose from the population enriched for the filamentous morphotype displayed the open lace-like architecture that is characteristic of the transiently restricted population ( Fig . 5D ) . Collectively , these data indicate that transient heme-iron restriction influences NTHI morphological changes in a subpopulation that contribute to biofilm architectural changes including the lace-like architecture and tower formation ( Fig . S3 ) . SulA is a component of the SOS response and is responsible for the inhibition of septation during repair of DNA damage [37] . This classically described SulA binds to the FtsZ monomer to prevent FtsZ polymerization at midcell resulting in the formation of non-septate filaments [38]–[40] . Septation is restored by Lon-mediated degradation of SulA , leading to polymerization of FtsZ at midcell [37] . We demonstrated that the NTHI sulA gene is part of an operon encoding the arginine repressor , argR ( Fig . 6 ) . This genetic context is unlike other SulA orthologues and is conserved amongst all sequenced Haemophilus strains . The NTHI SulA-related ortholog is almost twice as large as the classical SulA , yet retains sequence identity ( 70% ) in the regions including the binding sites for FtsZ and the Lon protease , suggesting a functional role for NTHI SulA in bacterial filamentation . We reasoned that this cell division inhibitor could be responsible for the filamentation observed following transient heme-iron restriction . To evaluate the contribution of filamentation to biofilm architecture , we constructed an unmarked deletion of sulA ( Methods and Materials ) . Growth of the sulA mutant was indistinguishable from the wild type strain under laboratory conditions ( Fig . S2C ) . In contrast to the lace-like architecture of the biofilms produced by the wild type strain ( Fig . 5E ) , we observed a mat-like architecture of the biofilms formed following transient heme-iron restriction in the absence of SulA ( Fig . 5F ) . In addition , there was a significant decrease in the overall peak height of biofilms in the absence of SulA ( p = 0 . 0001 ) ( Fig . 5G ) . Hence , the morphological changes and architecture of biofilms grown under transient heme-iron restriction required the activity of SulA , suggesting that filamentation is an important component of the architectural attributes associated with fluctuations in heme-iron availability . Although the regulation of the argR-sulA transcript is not characterized , we have previously shown that the promoter for argR is upregulated during NTHI-mediated experimental otitis media indicating a potential role during pathogenesis [41] . This observation is consistent with evidence demonstrating a role for ArgR during pathogenesis in other bacterial species [42]–[44] . Previously it has been shown that argR expression , in NTHI strain 86-028NP , is not induced upon exposure to hydrogen peroxide [45] . These data suggest that regulation of argR-sulA in Haemophilus may be independent of the SOS DNA damage repair response , hallmarks of the classical E . coli SulA ortholog . To determine genes that are induced in other bacterial species as part of the SOS response ( including sulA ) , other groups have used Mitomycin C ( MMC ) or other DNA damaging agents ( e . g . UV light ) [46]–[50] . To assess whether DNA damage induces expression of argR-sulA in NTHI , we exposed an NTHI GFP reporter strain ( GFP under control of the argR-sulA promoter ) to increasing concentrations of MMC and monitored fluorescence intensity as a measure of promoter activity . As previously published , MMC induces expression of the E . coli sulA gene in a dose dependent manner ( Fig . S4A ) . In contrast , expression of NTHI argR-sulA in response to MMC is similar to fluorescence observed in the absence of promoter and absence of MMC ( Fig . S4A ) . Consistent with these observations , we did not detect increased expression of argR or sulA in response to MMC exposure ( Fig . S4B ) . Thus , these data suggest that the SulA ortholog is not a component of the SOS regulon and may represent a novel class of cell division inhibitors . The contribution of SulA-mediated filamentation towards the persistence of NTHI was evaluated in an animal model of human otitis media . Chinchillas were co-infected with equivalent numbers of wild type NTHI and the sulA mutant . We observed significant loss of the sulA mutant in middle ear effusions as early as 4 days following infection ( Fig . 5H ) . Although the wild type strain continued to persist at high levels ( approx . 107cells/ml effusion ) at 10 days following infection , all effusions were devoid of detectable sulA mutant NTHI ( Fig . 5H ) . Moreover , the sulA mutant was not recovered from disrupted epithelial mucosa at 12 days post infection . These data demonstrate that SulA contributes to the persistence of NTHI during pathogenesis . We demonstrated that heme-iron restriction imparts a survival advantage for NTHI in vivo ( Fig . 4A ) . Moreover , we observed that a change in bacterial morphology and subsequent influence on NTHI biofilm architecture and persistence during disease ( Fig . 5 ) suggests that prior heme-iron status may also contribute to disease severity . Thus , we sought to characterize changes in disease progression following middle ear infection with NTHI , either restricted or replete for heme-iron at the time of infection . We first monitored middle ear pressure by tympanometry over the first seven-days following introduction of NTHI . A change in middle ear pressure is a clinical hallmark of otitis media [51] . During the acute phase of the infection and prior to any detectable middle ear effusion ( day 1 ) , we observed a rapid decrease in middle ear pressure that was detected in the cohort that received heme-iron replete NTHI ( Fig . 7A ) . In contrast , we observed a delayed decrease in middle ear pressure in the cohort infected with the heme-iron restricted NTHI ( day 2–4 ) . These data are indicative of a change in the kinetics of disease progression based upon the prior heme-iron availability to NTHI . Notably , the difference in kinetics was not due to differences in bacterial burden per gram of tissue under these experimental conditions on day 7 [restricted NTHI 5 . 2×107CFU/g; replete NTHI 3 . 8×108 CFU/g: N = 6: p = 0 . 285] . To better characterize changes in bacterial-host interactions between the two cohorts , middle ear inferior bullae were examined histologically seven days following infection with either heme-iron replete or heme-iron restricted NTHI . We observed severe edema , vasodilatation , erythema , hemorrhage and host immune cell infiltrate with evidence of mucosal epithelial destruction in middle ears challenged with heme-iron replete NTHI ( Fig . 7C , D ) , hallmarks of middle ear disease not observed in the naïve middle ear ( Fig . 7B ) . In stark contrast , middle ears infected with heme-iron restricted NTHI were edematous with host immune cell influx; however , there was a clear absence of other hallmarks of disease severity ( Fig . 7E , F ) . In fact , we observed an intact mucosal epithelial barrier to the lumen of the middle ear ( Fig . 7E , F ) . These observations , concurrent with the changes in middle ear pressures observed between the two cohorts , support differential disease severity , dependent upon prior heme-iron status of NTHI . Collectively , our observations indicate that transient restriction of heme-iron influences NTHI morphology and biofilm architecture that imparts a survival advantage in the middle ear . Examination of bacteria-host interaction in the middle ear suggests that this survival advantage may be due , in part , to a differential host response , one of decreased disease severity , as a consequence of the prior heme-iron status of NTHI . We have previously demonstrated that NTHI defective in heme acquisition due to a mutation in the Sap transporter were hyperinvasive and were observed in the cytoplasm of epithelial cells [33] . In contrast , the wild type strain primarily colonized the apical surface of the epithelial cells and demonstrated reduced invasion events which were associated with vacuolar localization and NTHI clearance [33] . During our microscopic evaluation of mucosal tissue integrity as a consequence of prior heme-iron status , we observed what appear to be populations of NTHI within the epithelium of the chinchilla middle ear ( Fig . 8A , B ) . NTHI populations were not readily observed in the epithelium of middle ears infected with heme-iron replete NTHI ( Fig . 7 ) . To confirm that the prior heme-iron status contributes to the invasion of NTHI , we investigated the frequency of invasion using our cultured epithelial cell model of infection . Following 48 hours of infection with heme-iron restricted NTHI , we observed numerous intracellular bacterial populations that appear to fill the volume of the cell ( Fig . 8C , E ) . In contrast , when epithelial cells were infected with heme-iron replete NTHI , we observed fewer infected cells and those infected cells contained fewer bacteria ( Fig . 8D , F ) . The ability of NTHI to fill the volume of the cell following restriction of heme-iron is suggestive that nutritional conditioning allows NTHI to escape vacuolar trafficking to gain access to the cytoplasm . This intracellular localization provides an additional compartment for bacterial growth and protection from host immune clearance mechanisms . Moreover , intracellular lifestyles are commonly associated with chronic and recurrent infections . Our experimental evidence demonstrated the establishment of a productive intracellular population as a consequence of enhanced invasion rates and fates due to nutrient limitation of wild type NTHI .
The phenotypic changes associated with transient heme-iron restriction invoke an interesting paradox in which host nutritional immunity , which serves to scavenge essential nutrients [52] and thus prevent bacterial growth , conversely enhances persistence and reduces disease severity . Transition to the middle ear exposes NTHI to an iron-restricted environment , yet progression of disease coincides with a potential increase in availability of iron sources due to host inflammatory responses ( Fig . 9 ) . Given the near global bacterial requirement for iron , there have been many investigations into the mechanisms required for bacterial survival in iron-restricted microenvironments [53]–[55] . Interestingly , we observed that prior heme-iron restriction had dramatic impact on NTHI persistence in the middle ear as evidenced by enhanced survival in direct competition with heme-iron replete NTHI in an animal model for human otitis media ( Fig . 4 ) . The bacterial burden was indistinguishable when middle ears were infected with only heme-iron replete or heme-iron restricted NTHI . However , disease severity was attenuated in response to infection with heme-iron restricted NTHI ( Fig . 7 ) . We observed a lack of middle ear pathology ( i . e . vasodilatation , hemorrhage , erythema , loss of mucosal epithelial integrity ) and unremarkable tympanometry during infection with heme-iron restricted NTHI . Moreover , heme-iron restricted NTHI were more invasive of host epithelium , forming communities of intracellular bacteria within the epithelial cell cytoplasm ( Fig . 8 ) . Taken together , our data indicate that prior heme-iron restriction influences biofilm architecture , modulates the host immune response , and provides multiple mechanisms to persist in the face of the proinflammatory response . Distinct from acute otitis media associated with middle ear inflammation , fever and pain , studies have identified an asymptomatic subclinical variant of otitis media that is primarily associated with middle ear effusion in the absence of appreciable tympanic pathology [56] , [57] . This variant of otitis media with effusion can lead to chronic otitis media associated with bacterial persistence . We interpret the clinical parameters and histological data obtained from chinchilla middle ears infected with heme-restricted NTHI to closely mimic this asymptomatic variant of otitis media , and thus serve as a useful model to study this clinically relevant variant of middle ear disease . Moreover , the experimental conditions explored within provide tools to define the ramifications of host-pathogen interactions on disease progression and severity . Although classically considered an extracellular , opportunistic pathogen , there is increasing evidence of intracellular and paracellular niches for NTHI in vitro [58] . Invasion into epithelial cells could provide NTHI with an environment rich in nutrients and a refuge from immune pressures and thus provide a temporary or long-term respite from nutrient limitation and innate immune components . It is plausible that bacterial responses to fluctuations in nutrient availability modulate host pathogen interactions with the epithelium . In fact , NTHI that exhibit heme-iron restriction due to genetic manipulation ( i . e . loss of Sap-mediated heme-iron transport ) are hyper-invasive in a polarized epithelial model of infection [33] . Moreover , this hyperinvasion coincides with a change in intracellular trafficking that promotes Haemophilus survival in the cytoplasm [33] . We now demonstrate that heme-iron restriction influences NTHI invasion resulting in the formation of infected cells or pods that contain intracellular bacterial communities [59]–[61] . In addition to the potential for intracellular populations within the middle ear , the presence of NTHI within adenoids and bronchial epithelium suggests that an invasive phenotype may coincide with the chronic nature associated with NTHI-mediated diseases [62]–[66] . The chronic nature of NTHI infections including recalcitrance to antibiotic therapy , persistence in the presence of bactericidal antibodies and culture-negative clinical analysis are suggestive of biofilm formation and development of intracellular bacterial reservoirs within host cells [58] , [62]–[64] , [66]–[70] . Invasion and subsequent emergence of a viable reservoir of NTHI from the intracellular and paracellular niches could serve as a seed for recurrent and chronic OM . Future studies will determine whether intracellular pools of Haemophilus provide a reservoir for persistence and recurrent infection . The biological complexity of NTHI lifestyles contributes to pathogenesis and , importantly , to the prolonged , recurrent and difficult-to-treat nature of NTHI-induced disease . Co-culture of transiently restricted and replete NTHI during biofilm formation demonstrated that the spatial distribution of NTHI is a consequence of prior heme-iron status , which provides insight into the mechanisms that dictate biofilm architecture . We demonstrated that these changes are only transmitted from mother to daughter cells and are not transmitted to other bacteria within the same culture . This suggests that the observed changes in community structure are not likely mediated by a released soluble factor as all bacteria are exposed to the same microenvironment , yet only a subset proceed through a unique developmental program . Moreover , we observed a phenotypic change that persisted for at least 72 hours both in vitro and in vivo . The long-standing phenotypic changes observed both in vivo and in vitro following transient heme-iron restriction suggest that epigenetic changes accompany the fluctuations in nutritional availability . To begin to evaluate these long-term changes , the protein profiles of NTHI transiently restricted of heme-iron were compared with NTHI continuously exposed of heme-iron . Proteins known to be involved in heme-iron acquisition were present at similar levels , indicating that the transiently restricted NTHI have returned to heme-iron homeostasis within 6 hours following restoration of heme-iron ( Table S1 ) . We did observe increased levels of proteins involved in metabolism and oxidation-reduction ( Table S1 ) , suggesting that these changes could contribute to the enhanced persistence phenotype observed during otitis media ( Fig . 4 ) . Iron starvation triggers a DNA methylase-dependent epigenetic program to control gene expression in Escherichia coli [71] . We are currently investigating the potential role of DNA methylation in the epigenetic programs that promote distinctly different NTHI phenotypes that subsequently modulates disease manifestation . Subpopulations have been observed in response to iron availability during biofilm development of E . coli [72] . We also observed that transient heme-iron restriction resulted in two phenotypically distinct populations that could be separated based upon the bacterial morphology ( Fig . 5 ) . We further demonstrated that the SulA-related ortholog was necessary to support the morphological plasticity , biofilm architectural changes and persistence associated with transient heme-iron restriction . We and others have demonstrated that morphological changes promote survival in the face of antimicrobial agents as well as the killing by professional phagocytes and predation [35] , [36] , [73] , [74] . We propose that the morphological changes observed with NTHI would provide similar mechanisms for survival within the middle ear . The observation that sulA is co-transcribed with argR , and that the genetic context is conserved in Haemophilus strains , suggests a previously undescribed regulatory mechanism for expression . We have shown that argR-sulA is not a constituent of the SOS regulon . Our data indicate that transient heme-iron restriction leads to NTHI filamentation , and that this morphological change is SulA-dependent . These observations raise the question whether sulA-argR regulation is heme-iron dependent , whether direct or indirect . We have shown that neither argR nor sulA genes are regulated by the iron-responsive regulator Fur [28] , indicating a Fur-independent mechanism of iron regulation . In addition the role of arginine metabolism in Haemophilus persistence is unknown and under investigation . Our observations highlight the impact that immune pressure and host microenvironments have on bacterial lifestyles . Here , we demonstrated that these changes in the developmental program equip NTHI to more successfully persist during disease progression . The nutrient limitation we imposed in vitro mimics nutritional immunity in the host and can be exploited to further elucidate the molecular details of bacterial developmental programs in response to host pressures during disease progression .
All animal experiments were carried out in strict accordance with the accredited conditions in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . The protocol was approved by the Institutional Animal Care and Use Committee ( Welfare Assurance Number A3544-01 ) at The Research Institute at Nationwide Children's , AR13-00026 . All experimental procedures were performed under xylazine and ketamine anesthesia , and all efforts were made to minimize suffering . NTHI strain 86-028NP is a minimally passaged clinical isolate which has been extensively characterized in the chinchilla model of human otitis media , and sequenced [45] , [75] . Construction of a nonpolar unmarked deletion of the sulA gene was performed by the recombineering strategy as previously described [31] , [76] . Briefly , primers 5′-CAGCCGCAATCGCAACAGTAGTAT-3′ and 5′-TTGGGTAAACGTGAAGAAAATG-3′ were used to amplify sulA and 1 kb of the flanking DNA both 5′ and 3′ to sulA . The subsequent amplicon was ligated into the pGEM-T Easy vector ( pDH001 ) and transformed into E . coli strain DY380 . In parallel , primers , 5′-GCTGAATTATTGCAAAATATCCAACGTTTATTTGAAAACGCATTGTAATGATTCCGGGGATCCGTCGACC-3′and 5′-TCACCGCATTAAAAAAGTGCGGTGATTTTTTATTTGTTTTTTAGGATTTCTGTAGGCTGGAGCTGCTTCG-3′ , each containing 50 bp of DNA homologous to the 5′ and 3′ ends of the sulA gene , were used to amplify the spectinomycin cassette from pRSM2832 [76] . This amplicon was then electroporated into strain E . coli DY380/pDH001 to form strain DY380/pDH002 , in which the sulA gene in pDH001 was been replaced by the spectinomycin cassette . The plasmid pDH002 was then used to transform NTHI 86-028NP , and transformants were selected by growth on spectinomycin-containing chocolate II agar plates . To generate a nonpolar deletion mutant , the sulA mutant was transformed with plasmid pRSM2947 and grown at 32°C , and FLP expression was induced using anhydrotetracycline . The cells were cured of the plasmid by growth at 37°C . The mutant strains were screened for growth on chocolate agar II ( Becton Dickinson , Sparks , MD ) and in parallel , susceptibility to spectinomycin , generating strain NTHI 86-028NP::sulA and were confirmed by sequence analysis . Green fluorescent protein ( GFP ) reporter parent and sulA mutant strains were created by electroporation of pGM1 . 1 as published previously [31] . In order to generate an mCherry reporter strain , plasmid ( pGM1 . 1 ) was isolated from 86-028NP/pGM1 . 1 and digested with BamH1/EcoR1 to remove the gfp open reading frame . The open reading frame encoding mCherry was isolated as a BamH1/EcoR1 restriction fragment from pRSET-B ( Gift of Dr . Roger Tsien ) [77] cloned into the BamH1/EcoR1 site of pGM1 . 1 generating plasmid pKM1 . 1 . The plasmid was used to transform NTHI 86-028NP , generating strain NTHI 86-028NP/pKM1 . 1 . To distinguish strains in a competition model of otitis media , 86-028NP and 86-028NPΔsulA were marked as previously described [28] . 86-028NP was transformed with pGZRS-39A , a Haemophilus-Actinobacillus pleuropneumoniae shuttle vector that contains the kanamycin resistance gene from Tn903 [78] . 86-028NPΔsulA was transformed with pSPEC1 , a variant of pGZRS-39A , in which the kanamycin resistance gene was replaced by a spectinomycin resistance gene [30] . NTHI was grown overnight on chocolate II agar ( Becton Dickinson , Sparks , MD ) at 37°C in 5% CO2 . Individual colonies were adjusted to an OD490 of 0 . 65 into chelated defined iron-source medium ( DIS ) , diluted 10-fold and then subcultured into nitric acid-washed 15 ml round bottom glass tubes containing pre-warmed DIS medium with either 0 , or 2 µg heme mL−1 , and grown statically for 24 hours to deplete or maintain internal stores of heme , as previously described [18] , [31] . Following depletion of heme-iron , bacteria were subsequently inoculated into DIS supplemented with 0 , 2 , or 20 µg heme mL−1 ( Sigma Aldrich , St . Louis , MO ) and statically grown at 37°C in 5% CO2 as previously described [18] , [31] . Following incubation , the cultures were equilibrated to an OD490 of 0 . 37 and diluted appropriately into DIS containing 2 or 20 µg heme mL−1 to match cultures to 1×107 CFU/ml as described for each experiment . Cultures ( 50 µl ) were added to each well of a Nunc Lab-Tek 8-well chamber slide containing 200 µl ( 1∶5 dilution ) ( 2 . 5×106 CFU/ml ) of DIS supplemented with 2 , or 20 µg heme mL−1 . The chamber slide was then incubated at 37°C with 5% CO2 and biofilms were grown for 48 or 72 hours with a media change every 24 hours . Following incubation , biofilms were prepared and visualized as previously described [31] . Biofilm structure and organization were imaged with an Axiovert 200M inverted epifluorescence microscope equipped with the Apotome attachment for improved fluorescence resolution and an Axiocam MRM CCD camera ( Carl Zeiss Inc . , Thornwood , NY ) . Three-dimensional renderings were performed in Image J [79] to generate orthogonal views and surface plots . Experiments were repeated in triplicate and representative samples of each replicate are shown . For co-culture of heme-iron restricted and heme-iron replete 86-028NP , 86-028NP/pGM1 . 1 and 86-028NP/pKM1 . 1 strains were either continuously exposed or transiently restricted for heme-iron , mixed at either a 1∶1 or 1∶1000 ratio , and added ( 25 µl of each ) to each well of a Nunc Lab-Tek 8-well chamber slide containing 200 µl of DIS supplemented with 2 , or 20 µg heme mL−1 for a total inoculation of 2 . 5×106 CFU/well . The chamberslide was incubated , washed and fixed and visualized as described [31] , [33] . The biofilms were stained with live/dead DNA dye ( Molecular Probes , Grand Island , NY ) for the strains not containing a reporter plasmid . Experiments were repeated in triplicate and representative samples are shown . Chinchilla middle ear epithelial ( CMEE ) cells were grown to confluence on a Nunc Lab-Tek 8-well chamber slide as previously described [33] . Epithelial cells were inoculated with 86-028NP/pGM1 . 1 bacteria that were either restricted or replete for heme-iron ( 106 CFU in 200 µL DIS containing 2 µg heme mL−1 or CMEE media ) [33] , [80] . Seven hours after inoculation , the chambers were washed with DPBS and replaced with appropriate medium . The medium was replaced 24 hours after initial inoculation and cultured for an additional 24 hours . After 48 hours total culture , the cells were fixed in 4 . 0% paraformaldehyde ( Electron Microscopy Sciences , Hatfield , PA ) in DPBS ( Mediatech , Manassas , VA ) for fluorescence microscopy . For fluorescence microscopy , epithelial cell membranes were labeled with wheat germ agglutinin ( WGA ) -Alexafluor 594 ( LifeTechnologies , Grand Island , NY ) . Biofilm structure , organization and NTHI localization were imaged with an Axiovert 200M inverted epifluorescence microscope equipped with the Apotome attachment for improved fluorescence resolution and an Axiocam MRM CCD camera ( Carl Zeiss Inc . , Thornwood , NY ) . Experiments were repeated in triplicate and representative samples are shown . NTHI cultures , either continuously exposed or transiently restricted for heme-iron , were assessed for changes in cellular morphology as a result of heme-iron restriction by filter enrichment as previously described [36] . Briefly , the 86-028NP/pGM1 . 1 was either continuously exposed or transiently restricted for heme-iron for 24 h , and was added to each well of a Nunc Lab-Tek 8-well in DIS containing 2 µg heme mL−1 as described above to assess biofilm architecture after 48 hours of growth . Statistical analyses were performed using a two-tailed t-test or Mann-Whitney U-test as indicated ( Graphpad Prism , LaJolla , CA ) .
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Clinical management of upper and lower respiratory tract diseases caused by nontypeable Haemophilus influenzae ( NTHI ) is a significant socioeconomic burden . Therapies targeting the pathogenic lifestyle of NTHI remain non-existent due to a lack of understanding of host microenvironmental cues and bacterial responses that dictate NTHI persistence . Iron availability influences bacterial virulence traits and biofilm formation; yet , host sequestration of iron serves to restrict bacterial growth . We predicted that fluctuations in availability of iron-containing compounds , typically associated with infection , would impact NTHI pathogenesis . We demonstrated that transient restriction of heme-iron triggered an epigenetic developmental program that enhanced NTHI biofilm architecture , directly influenced by induced morphological changes in bacterial length . Heme-iron restricted bacteria were primed for survival in the mammalian middle ear , due in part to an observed reduction in host inflammation coinciding with a striking reduction in host mucosal epithelial damage , compared to that observed in response to heme-iron replete NTHI . Moreover , transiently restricted NTHI were more invasive of epithelial cells resulting in formation of intracellular bacterial communities . Our findings significantly advance our understanding of how host immune pressure and nutrient availability influence pathogenic behaviors that impact disease severity .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2013
|
Haemophilus Responses to Nutritional Immunity: Epigenetic and Morphological Contribution to Biofilm Architecture, Invasion, Persistence and Disease Severity
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It is generally acknowledged that biological vision presents nonlinear characteristics , yet linear filtering accounts of visual processing are ubiquitous . The template-matching operation implemented by the linear-nonlinear cascade ( linear filter followed by static nonlinearity ) is the most widely adopted computational tool in systems neuroscience . This simple model achieves remarkable explanatory power while retaining analytical tractability , potentially extending its reach to a wide range of systems and levels in sensory processing . The extent of its applicability to human behaviour , however , remains unclear . Because sensory stimuli possess multiple attributes ( e . g . position , orientation , size ) , the issue of applicability may be asked by considering each attribute one at a time in relation to a family of linear-nonlinear models , or by considering all attributes collectively in relation to a specified implementation of the linear-nonlinear cascade . We demonstrate that human visual processing can operate under conditions that are indistinguishable from linear-nonlinear transduction with respect to substantially different stimulus attributes of a uniquely specified target signal with associated behavioural task . However , no specific implementation of a linear-nonlinear cascade is able to account for the entire collection of results across attributes; a satisfactory account at this level requires the introduction of a small gain-control circuit , resulting in a model that no longer belongs to the linear-nonlinear family . Our results inform and constrain efforts at obtaining and interpreting comprehensive characterizations of the human sensory process by demonstrating its inescapably nonlinear nature , even under conditions that have been painstakingly fine-tuned to facilitate template-matching behaviour and to produce results that , at some level of inspection , do conform to linear filtering predictions . They also suggest that compliance with linear transduction may be the targeted outcome of carefully crafted nonlinear circuits , rather than default behaviour exhibited by basic components .
Animals constantly submit environmental signals to neural operations designed to extract useful information for guiding behaviour . Whether their sensory apparatus is considered in its entirety as a behavioural machine or in relation to hardware components like individual nerve cells , it can be described as an input-output transformation that maps external stimuli onto neural representations . The simplest way to characterize the operation of such a sensory device is to assign a set of weights to different elements of the incoming stimulation , then sum across all elements , and finally convert this weighted sum into a number compatible with the scale and units of the output variable [1 , 2] . For input stimulus s , this simple operation can be written as g ( ⟨s , w⟩ ) ( w is the weighting function , ⟨ , ⟩ inner product , and g a static nonlinearity ) . To provide an example , w may be the receptive field of a simple cell , and g the nonlinearity that maps membrane voltage onto average spike rate [3] . For another example , more relevant to the present study , we can think of w as the perceptual impact associated with different portions of a visual display presented to a human observer , and g the decisional transducer that maps aggregate perceptual impact onto a binary decision of the kind ‘I saw the stimulus’ or ‘I did not see it’ [4] . The above linear-nonlinear cascade model has been applied to innumerable phenomena in neuroscience [5 , 6] , to the extent that it would be impossible to summarize them here . Particularly when referring to perceptual processes , it is often termed ‘template-matching’ [7] to indicate that an internal template ( the filter ) is matched against the incoming stimulus ( via linear weighting ) before a decision is made as to whether the stimulus does or does not contain the template signal [8 , 9] . We will use the terms ‘linear-nonlinear’ ( abbreviated LN ) and ‘template matcher’ interchangeably . We will also occasionally refer to this process as ‘linear filtering’ or ‘linear transduction’ and contrapose it to a ‘nonlinear’ process , with the understanding that in these instances we are specifically referring to the processing stage that precedes the decisional nonlinearity ( g in the previous paragraph ) . The latter element is an integral part of all psychophysical models ( and those considered here are no exception ) , but can be largely bypassed to access the preceding layers using the methods employed in this study [4 , 10] . Qualitative thinking about sensory processing almost invariably refers back to the LN model [11] , not least because its explanatory scope can be greatly extended by adopting arbitrary descriptors for s , effectively remapping the stimulus onto a space that is available for inner-product treatment [12] . The well-known dipper function for contrast discrimination , for example , is often accounted for by a specific choice of g for a human observer modelled as a LN cascade [13] , and the link to neural activity can also be inferred via this simple framework [14] . Apparently counter-intuitive phenomena such as stochastic resonance can be accommodated via the LN model [15] . Furthermore , certain methodological approaches ( notably reverse correlation ) often rely on the assumption that the system of interest is well approximated by the LN operator [4 , 16 , 17] . Notwithstanding such widespread applicability , there are well-known instances when the LN model is unable to provide a satisfactory account of relevant phenomena . The operation of a complex cell , for example , cannot be described by the LN cascade acting directly on the stimulus image [18] . Several neural systems exhibit pronounced gain control properties [19] , and these too fall outside the explanatory reach of barebone LN operators . In human vision , detection under uncertainty represents a classic example of the inapplicability of simple template-matching models belonging to the LN family [20 , 21] . Adaptive phenomena , e . g . learning-mediated plasticity , can only be partially approximated by LN descriptors [10] . It is therefore uncontroversial that LN models are sometimes inadequate . The critical issue is to recognize when they are adequate or not and , whenever they appear adequate for a specific application of the system under interest , how far their applicability can be generalized to other applications of that same system . We can illustrate this issue with the following example . In experiment 1 , we characterize the response of a neuron , or a whole observer , to visual orientation by selectively manipulating the orientation content of a simple visual stimulus ( e . g . a textured object; see [22] for an example from literature ) . In experiment 2 , we characterize the response of the same system to pattern size by selectively manipulating the spatial frequency ( SF ) content of the same stimulus ( see [23] for examples ) . First , we ask whether the manner in which the system operates under the conditions of experiment 1 can be approximated by the LN operator applied to the input stimulus defined with respect to orientation content: s is a vector specifying orientation energy in the stimulus for different orientations , and w is the orientation tuning function of the system; is g ( ⟨s , w⟩ ) adequate ? We can ask the same question with reference to experiment 2 , except s is now the vector specifying stimulus energy across SF , and w is the SF tuning function of the system . An altogether different question is to ask whether the operation of the system with relation to both orientation and SF can be accounted for by the same LN cascade [24 , 25] . For this purpose , the visual stimulus must be projected onto a space s that encompasses both orientation and SF , because the LN cascade must be applied to one common input space; at the same time , it must be able to make predictions for the two different spaces probed in the two separate experiments . The natural space of choice in this case is that of the image itself , i . e . the 2D pixel array detailing stimulus intensity at each spatial location on the display . If we call this image 2Ds , the question is whether we can identify linear filter 2Dw and nonlinear transducer g so that g ( ⟨ 2D s , 2D w ⟩ ) will capture the results of experiments 1 and 2 . As we demonstrate in this study , a positive answer to the question posed in the previous paragraph ( i . e . both experiments falling within the explanatory power of the LN family ) does not guarantee a positive answer to this latter question: the system may appear to operate in the manner of the LN cascade with relation to a number of different probes defined within substantially different spaces ( orientation , SF , 2D space ) , yet its behaviour may not be collectively captured by a single LN cascade . Our results have important implications for the applicability of LN cascades to visual perception , and establish some general notions/tools relating to both the potential and limitation of this modelling family for capturing human sensory processing .
Ethics approval was obtained from the College Ethics Review Board ( CERB ) at Aberdeen University ( http://www . abdn . ac . uk/clsm/working-here/cerb . php ) . All participants gave written informed consent . Stimuli lasted 80 ms and consisted of 3 regions ( ∼3×3 deg each ) : a central ‘probe’ region at fixation ( the fixation marker consisted of a dark pixel in the centre measuring ∼3×3 arcmin and never disappeared ) ; above and below it , two identical ‘reference’ regions containing the template ( see S1 Video ) . The template signal consisted of a cosine-phase ( peaking at centre ) vertical Gabor wavelet ( standard deviation ( SD ) of Gaussian envelope 0 . 5 deg , spatial frequency 1 cycles/deg ) , and was presented at 17% contrast within the reference regions ( background luminance 30 cd/m2 ) . On each trial , observers saw two instances of the stimulus separated by a 500-ms gap . Reference regions were identical on both instances ( and across all trials ) , thus providing no useful information for performing the task; their purpose was to remind observers of the target signal shape , so as to facilitate a template-matching strategy [21 , 26] . The probe region contained target signal plus noise mask on one instance , and non-target signal plus noise mask on the other instance . Observers were asked to select the instance ( first or second ) that contained the target signal by pressing one of two buttons , after which they received immediate trial-by-trial feedback ( correct/incorrect ) . The target signal was simply the template Gabor wavelet described above ( see also Fig 1A ) , presented at 8% ( alternatively 4% ) contrast in the detection ( alternatively discrimination ) task . The non-target signal was blank for the detection task ( Fig 1F ) , and a horizontal variant of the target signal for the discrimination task ( see icons to the left of Fig 2E ) . Except for taking on a different orientation , the non-target signal in the discrimination task was identical to the target-signal . Data for the two tasks were collected in separate blocks of 100 trials each . We also collected separate data for a ‘symmetric’ variant of the discrimination task . In this additional experiment , two identical reference regions containing non-target templates were presented to the left and to the right of the central probe region . The noise mask could be 1 of 4 different types in the detection task , and 1 of 2 different types in the discrimination task ( thus explaining why the detection data in Figs 2B and 2D and 3C and 3E are not matched by equivalent data for discrimination ) . Mask type was randomly selected on every trial with equal probability for each mask . In the detection task , the noise mask could be ‘2D’ ( Fig 1B and 1G ) : each pixel ( within a 65×65 array ) was separately assigned a random luminance value from a zero-mean Gaussian distribution with SD ∼16% contrast ( we use the ∼ symbol because this value was tailored to each observer to target threshold performance of d′ ∼1 , see abscissa values in Fig 4C ) ; ‘1D’ ( Fig 1C and 1H ) : each column of pixels spanning the probe region was separately assigned a random value from a zero-mean Gaussian distribution with SD ∼9% contrast , and the vertical profile of each column was modulated by the envelope ( Gaussian window ) of the Gabor target signal; ‘Θ’ ( Fig 1D and 1I ) : a set of 12 Gabor wavelets spanning the 0-π orientation range ( Fig 1K ) , all identical to the target signal except for rotation , were assigned a random contrast value from a Gaussian distribution with mean ∼3% ( alternatively ∼5% ) contrast and SD ∼0 . 7% ( alternatively ∼1 . 2% ) contrast in the detection ( alternatively discrimination ) task; ‘SF’ ( Fig 1E and J ) : a set of 12 Gabor wavelets ranging in spatial frequency ( SF ) from 0 . 2 to 3 . 5 cycles/deg in logarithmic steps ( Fig 1L ) , all identical to the target signal except for SF , were assigned a random contrast value from a Gaussian distribution with mean ∼3% contrast and SD ∼0 . 7% contrast . Noise masks were specified as detailed above so that each mask type was associated with a non-zero probability of realizing the target signal . Because only 2D and Θ masks present a non-zero probability of realizing the non-target signal in the discrimination task , only these two masks were adopted for the discrimination condition . Furthermore , due to their vertical characteristic and the limited contrast range afforded by a combination of design and hardware constraints , 1D and SF noise probes did not effectively mask the horizontal non-target signal and introduced spurious cues ( e . g . cross-oriented regions ) for performing the task . 2D/1D noise was fully orthogonal ( each pixel was modulated independently and did not overlap with other pixels ) ; Θ/SF noise was not fully orthogonal because the underlying wavelets ( Fig 1K–1L ) were not themselves orthogonal except for specific instances . In relation to the logic of this study ( see Results ) orthogonality was important only between vertical and horizontal components of orientation noise ( indicated by peak and trough respectively in Fig 2G–2H ) ; these two components were very nearly orthogonal ( within hardware precision ) . All aspects of the study were validated via explicit implementation of fully specified computational models ( see below ) . We tested 10 observers in the detection task ( ∼3 . 3K trials per noise type per observer , total of ∼130K trials ) ; 5 of those 10 observers also participated in the discrimination task ( ∼4 . 4K trials per noise type per observer , total of ∼44K trials ) , and 1 of those 10 observers also participated in the symmetric variant of the discrimination task together with an additional 5 observers who did not belong to the original pool of 10 observers ( ∼4K trials per noise type per observer , total of ∼49K trials ) . All observers were naive to the purpose and methodology of the study; they were paid 7GBP/9EUR per hour for their participation . The total number of trials collected for this study ( single-pass and double-pass , see below for description of the latter type ) was 269700 . Following completion of data collection and acquisition of observer responses , we can classify each stimulus type z [ q , r ] as being of type type presented in the target-present ( q = 1 ) or target-absent ( q = 0 ) interval on a specific trial , to which observers responded correctly ( r = 1 ) or incorrectly ( r = 0 ) . It is a matrix of dimension 65×65 for type 2D , a 65-element vector for type 1D , a 12-element vector for type Θ and SF . It was constructed by summing a signal s to a noisy sample n: type z [ q , r ] = type s [ q ] + type n [ q , r ] . s and n were specified as detailed above . When projected onto 2D space s is the same across type ( see Fig 1 ) , however its vector representation with respect to each type differs: it is the Gabor wavelet specified above for type 2D , a horizontal slice through said wavelet for type 1D ( green trace in Fig 2B ) , a non-zero entry for the 7th element of a 12-element vector for type Θ and SF . The first-order target-present PF ( i . e . computed only from noise fields containing the target ) was type p 1 [ 1 ] = avg ( type n [ 1 , 1 ] ) − avg ( type n [ 1 , 0 ] ) where avg ( . ) indicates average across the subset of trials indexed by the assigned type and [q , r] values [4]; the target-absent PF was type p 1 [ 0 ] = avg ( type n [ 0 , 0 ] ) − avg ( type n [ 0 , 1 ] ) . The full PF was simply the sum of target-present and target-absent PF’s: type p 1 = type p 1 [ 1 ] + type p 1 [ 0 ] [29] . The second-order PF is similarly computed as typep 2 = cov ( type n [ 1 , 1 ] ) + cov ( type n [ 0 , 0 ] ) − cov ( type n [ 1 , 0 ] ) − cov ( type n [ 0 , 1 ] ) where cov ( . ) indicates covariance across trials of the specified classification [10] . Because we found an inevitable degree of variability across observers , it is difficult to draw conclusions from simply inspecting individual PF’s . We therefore performed additional analyses that captured relevant aspects of filter structure , and quantified each aspect using a single value ( scalar metric ) for each PF . This approach made it then possible to perform simple population statistics and confirm or reject specific hypotheses ( against unambiguously defined null benchmarks ) about the overall shape of the filters . Our conclusions are therefore based on individual observer data , not on the aggregate observer; aggregate descriptors ( e . g . Fig 2A–2E and 2G ) are only presented for visualization purposes . Some previous studies using classified noise have relied on qualitative inspection of aggregate data , but this approach is inadequate to draw robust conclusions primarily for two reasons: 1 ) there is no generally accepted procedure for generating an average PF from individual images for different observers [30]; 2 ) we have shown in previous work that effects observed via qualitative inspection of aggregate filters may not survive quantitative inspection using metric analysis , and vice versa [22] . We performed a series of additional experiments specifically designed to measure human-human agreement [39] . During these experimental blocks , observers saw the same stimuli presented under typical data collection conditions , with the only difference that the second half of each 100-trial block ( from trial #51 to trial #100 ) consisted of a repetition of the first half ( from trial #1 to trial #50 ) in randomly permuted order . Human-human agreement ( plotted on the x axis in Fig 4A ) is simply the % of repeated trials on which observers gave the same response . By adopting a minimal signal detection theory ( SDT ) framework , internal noise ( plotted on the y axis in Fig 4C ) can be estimated ( in units of external noise ) from human-human agreement and the % of correct-response trials . Details of this routine procedure have been extensively documented in previous publications [27 , 39 , 40] . Human-model agreement ( plotted on the y axis in Fig 4B ) is the % of trials on which the human response matches the response generated by a computational model to the same stimulus set ( see below for details of models implemented here ) . For a given value of human-human agreement x in a 2AFC task , upper and lower bounds on the maximum achievable human-model agreement are given by ( 1 + 2 x − 1 ) / 2 and x itself [41]; the corresponding region is indicated by green shading in Fig 4B . Human-model agreement may exceed chance for models that are decoupled from the trial-by-trial perturbation delivered by the external noise source; to identify instances where this may be the case , we have developed a ‘decoupled’ baseline ( see S1 Text ) . We collected double-pass data from 9 of the 10 observers in the detection task ( ∼650 trials per noise type per observer , total of ∼23K trials ) , 2 of the 5 observers in the discrimination task ( ∼1050 trials per noise type per observer , total of ∼4 . 2K trials ) , and all 6 observers in the symmetric variant of the discrimination task ( ∼1650 trials per noise type per observer , total of ∼20K trials ) . The first half/pass of this dataset was combined with the original dataset for the purpose of PF estimation and associated analysis .
We asked observers to detect the most common target stimulus in contemporary vision science [43]: the Gabor wavelet ( Fig 1A ) . On each trial , one interval contained this target signal , while the other interval did not ( Fig 1F ) ; observers were asked to select the target interval . We then added four different types of visual noise to both target and non-target stimuli: 2D pixel noise ( Fig 1B and 1G ) , 1D ‘line’ noise ( Fig 1C and 1H ) , orientation ( Fig 1D and 1I ) and spatial frequency ( SF ) noise ( Fig 1E and 1J ) . We applied psychophysical reverse correlation [10 , 17] to retrieve the perceptual filters ( PF ) associated with each noise probe separately ( see Methods ) . The PF can be thought of as the psychophysical equivalent of the physiological receptive field [11 , 41]: it provides an overall picture of the weighting function applied by the observer to the incoming stimulus for the purpose of identifying the assigned target signal [4 , 17] . This description is useful for intuitive purposes , but is inaccurate and possibly misleading upon closer inspection due to important differences between the two processes instantiated by single neurons on the one hand , and human observers on the other [10 , 41] . The different noise probes were randomly mixed within the same block , so that observers did not know which noise type would appear on the next trial ( see S1 Video ) ; furthermore , their task was identical throughout all blocks , regardless of the noise type applied on any given trial . Data from different noise types therefore enable different vantage points on the same underlying elementary visual operation ( extraction of a localized oriented wavelet ) . As expected , the PF’s associated with different noise types resemble the target signal: the 2D spatial PF presents a Gabor-like modulation not dissimilar from the target ( compare Fig 2A with Fig 1A ) , the 1D spatial PF takes on a Mexican-hat shape that largely overlaps with a horizontal slice through the target ( compare black data with green trace in Fig 2B ) , the orientation-tuned PF peaks at target orientation ( indicated by green line in Fig 2C ) , and the SF-tuned PF peaks at target SF ( indicated by green line in Fig 2D ) . In a series of additional experiments , we determined that similar results were obtained for a discrimination variant of the same task where the non-target signal was orthogonal to the target signal ( icons to the left of Fig 2E ) . The PF’s associated with the discrimination task were virtually identical to those returned by the detection task ( compare E with A and G with C in Fig 2; only 2D and Θ masks are applicable to this task , see Methods ) , contrary to the ideal observer prediction that the 2D PF should be an image of the target minus the non-target signal [1]: the estimated PF for discrimination ( Fig 2E ) appears to contain exclusively vertically oriented structure ( across observers and detection/discrimination tasks , PF match with target ( vertically-oriented ) on y axis in Fig 2F is significant at p < 10−3 ( Bonferroni-corrected for multiple comparison ) by two-tailed Wilcoxon signed rank ( WSR ) test for match>0 , while match with rotated target ( horizontally oriented ) on x axis is not significant at p>0 . 05 ) . A feature of specific interest for the purposes of later modelling efforts is that orientation-tuned PF’s ( Fig 2C and 2G ) present clear troughs at the non-target orientation ( ±π/2 on x axis ) of magnitude comparable to their peaks ( across observers/tasks , peak/trough amplitudes on y/x axes in Fig 2H modulate significantly at p < 10−3 ( same test as above ) and are not different ( except for sign ) at p>0 . 05 by paired two-tailed WSR test ) . This result is not trivially expected as other outcomes are possible ( see [22] for examples ) . The lack of any discernible difference between discrimination and detection experiments indicates that the same mechanism supports both operations , prompting us to seek a single model able to account for the entire dataset in Fig 2 . We carried out 4 tests , based on established literature [10 , 34 , 36 , 38 , 39 , 41 , 44] , designed to gauge the applicability of LN transduction . They converge to indicate that , for any given representation of the visual stimulus , the LN framework provides an adequate description of the manner in which the human sensory system operates with respect to that representation . The first test capitalizes on the prediction by the LN model that filter estimates returned by noise fields associated with the target stimulus ( target-present ) must match estimates returned by target-absent noise fields [21 , 31–38] . We observed virtually no difference between the two estimates ( compare left versus right surface plots in Fig 3B and 3F and blue versus red traces in C-E , H ) , consistent with the prediction of the LN model ( see below for individual observer analysis and quantitative corroboration of the above-detailed qualitative observations ) . An additional test relies on nonlinear ( second-order ) operators that capture system properties not conveyed by first-order estimates [33] ( see Methods ) ; the LN model predicts structureless second-order perceptual filters [10] . Fig 3G and 3I plot an index of such second-order nonlinear structure ( y axis ) versus a similar index of structural differences between target-present and target-absent PF’s ( relating to the ‘first-order nonlinear’ test described in the previous paragraph ) across observers ( different data points ) ; neither was significantly different from 0 ( p>0 . 05 whether corrected or not for multiple comparison , one-tailed WSR test; see also overall black/gray distributions in Fig 3A , alongside orange distribution of same structural index applied to overall first-order PF’s to demonstrate that this index can adequately expose filter structure when present ) . Although the two tests are not equivalent in that second-order nonlinearities do not necessarily impact target-present filter estimates [10] , they probe related aspects of the underlying process [26]; they may therefore be expected to correlate in the event of departures from template matching ( we provide one such example from data in Fig 8I ) . Contrary to this expectation , there was no detectable correlation ( p>0 . 05 , robust correlation toolbox [45] ) between the two tests ( see lack of substantial tilt for gray ovals in Fig 3G and 3I ) , further supporting the notion that if any departure from template matching was present in our data , it was too small to measure . A third test compares human absolute efficiency ( plotted on y axis in Fig 4A ) against corresponding predictions for LN models incorporating the empirically estimated PF [44] . There was reasonable agreement between measurements and predictions ( points scatter around diagonal unity line ) , however we report an appreciable tendency for measured efficiency values to exceed corresponding predictions ( this effect is visually demonstrated by the tendency for data points to fall above the unity line in Fig 4A; see also histogram within inset ) . More specifically , when different noise conditions are tested separately , only the 1D condition approaches statistical significance ( two-tailed paired WSR test for measured versus predicted estimates returns p < 0 . 005 but this value does not survive Bonferroni correction for multiple comparison; the remaining three noise conditions return p>0 . 05 whether corrected or not ) . When tested collectively across conditions and tasks ( all data points in Fig 4A collapsed onto one dataset ) , measured values exceed predicted values at p < 10−3 ( two-tailed paired WSR test , survives correction for multiple comparison ) . Similar deviations have been previously documented for a variety of stimuli and discrimination tasks [44] . Our current understanding of relevant phenomena does not allow us to confidently identify the source of this small discrepancy , and it should be noted that the efficiency predictions are based on specific assumptions about the nature of the internal noise source [44] . The additional test detailed below makes no such assumptions . The last test compares the output generated by the LN model on specific trials against the human output on those same specific trials [46] ( this comparison was also independently assessed with respect to decoupled baseline , see Methods and symbol size in Fig 4B ) . The resulting human-model agreement ( proportion of trials on which the two outputs match ) is plotted in Fig 4B ( y axis ) against the proportion of trials on which human observers replicate their own response for two passes of the same stimulus ( human-human agreement , see Methods ) . Collectively , estimates fall significantly above the lower bound of the maximum predictability region [41] ( green shaded area ) on a targeted comparison ( two-tailed paired WSR test for y>x returns p <10−5 ) , they do not differ from the midpoint between upper and lower bound ( indicated by the green dotted line in Fig 4B ) at p = 0 . 26 , but they are smaller than the upper bound ( p < 10−8 ) . These findings indicate that the level of trial-by-trial predictability achieved by the LN model is compatible with optimality , although they do not guarantee this result: it remains possible that the internal noise process operating within observers is such that maximum predictability should be assigned to the upper bound of the green area in Fig 4B [41] , in which case the trial-by-trial predictability achieved by the LN model would be suboptimal . We currently lack effective tools for characterizing the detailed structure of internal noise within human observers , however based on recent attempts [26] it appears reasonable that the typical region of maximum predictability for human vision should lie between the two extremes; this region is compatible with the values generated by the LN model ( see above ) . We also converted these measurements into estimates of internal noise [39] to confirm that its properties are compatible with the notion of a late additive source often assumed by LN models [4] ( Fig 4C–4D , see caption ) . It may appear surprising that human visual processing displayed such compliance with the LN model , particularly with relation to the target-present/target-absent comparison ( Fig 3 ) : a representative survey of relevant literature indicates that these two estimates differ at least as often as they do not [4 , 21 , 36] , and sometimes substantially so [34 , 37 , 38 , 47] . It may therefore be argued on the basis of pevious studies that the lack of any difference , rather than its presence , should be viewed as unexpected and atypical . Our results , however , must be interpreted in light of the consideration that every aspect of the adopted experimental design was optimized to achieve template matching on the part of human observers ( e . g . the stimulus was presented centrally [26] , we placed two noiseless target signals above and below the central probe [48] , we explicitly instructed observers to match the probe against those target replicas [21 , 26] , observers were given trial-by-trial feedback ) . Our objective was to test the applicability of LN modelling under conditions that favoured this processing mode , so that we could gauge the full extent of its explanatory power . Our analyses support the applicability of the LN cascade with respect to each dimension we probed . This result does not imply that the same LN model operating within a common representation also accounts for all results obtained across different representations . We turn to the latter issue below . In attempting to identify a single specific implementation of the LN cascade that may account for our entire dataset , the natural starting point is a LN model that applies a 2D template similar to the estimated 2D PF ( followed by the static decisional nonlinearity , see Methods ) . We implemented the 2D LN model using the target signal as template ( Fig 5A ) rather than the estimated PF ( Fig 2A ) . This choice is motivated by the following considerations: 1 ) it allows us to exploit the full resolving power of the entire dataset by avoiding the need for cross-validation [49]; 2 ) our results will serve future investigations even where data mass is insufficient to support PF estimation [50]; 3 ) model evaluation will be based on ‘structural failures’ of the associated simulations ( i . e . qualitative departures from data that cannot be ameliorated by tweaking model parameters ) , so that fine details of template structure are irrelevant . Unsurprisingly , the 2D LN model generates good PF predictions for 2D/1D probes ( Fig 5B–5C ) , however it is unable to simulate the negative modulations orthogonal to target orientation observed for orientation-tuned PF’s ( compare blue with black traces in Fig 5D and 5I ) ; as we have demonstrated in Fig 2H via quantitative analysis , those modulations reflect genuine structure in the data . No degree of model tweaking would allow the 2D LN model to generate those negative modulations: the underlying human template contains no structure along the orientation orthogonal to the target ( Fig 2F ) , which in turn implies orientation-tuned PF’s lacking modulations within that region of orientation space . The LN model specified above must therefore be rejected as a viable account of our dataset . Because this conclusion is solely based on characteristics associated with the linear filtering stage ( L ) in the LN model ( those characteristics are evaluated via the corresponding PF estimates which , under the LN model , return an image of the linear filtering stage ) , it is valid regardless of the characteristics associated with the static nonlinearity ( N in the LN model ) and therefore generalizes to any such nonlinearity supporting a sensible discrimination model . In other words , even if one were to allow for different characteristics of the static nonlinearity to be associated with different noise types , the 2D LN model just considered would not be compatible with our results . The structural failure detailed above can only be addressed by inserting a model component orthogonal to the target signal; because peak and trough amplitudes were comparable ( Fig 2H ) , it would appear that the orthogonal component should be assigned equal gain to the component aligned with target orientation . We must rule out a push-pull model where the template is obtained by simply taking the difference between target and non-target signals ( we further consider this model later in the article ) , because the associated 2D filter estimate should be itself an image of that difference , contrary to the observed PF ( Fig 2A and 2E–2F ) . The next minimal incremental modification of the push-pull model involves squaring the output of the two templates before they are subtracted [51] ( Fig 5F ) . This variant , which no longer belongs to the LN family , generates orientation-tuned filters fully overlapping with those observed experimentally ( compare red and black traces in Fig 5D and 5I ) , however it fails to generate suitable 2D filter descriptors ( Fig 5B and 5G ) . This failure is once again structural: there is complete symmetry between vertical and horizontal orientations at the level of stimuli , task and model , so that all PF estimates must be similarly symmetric; we observed symmetry for orientation-tuned PF’s ( Fig 2H ) but not for 2D filters ( Fig 2F ) , ruling out the nonlinear push-pull model . To summarize , the push-pull model improves on the LN model in its superior ability to capture orientation-tuned PF’s ( blue data points in Fig 5J fall below the unity line at p < 0 . 0002 by two-tailed paired WSR test for x different than y values ) , however it is poorer at accounting for 2D/1D PF’s ( red/orange data points in Fig 5H fall above the unity line at p < 10−4 ) . An additional canonical operation in the construction of small-scale circuits for neural computing is divisive normalization [19 , 52 , 53] . We implemented the most basic version of this operation: the linear drive is supported by the target-like matched template , and the gain-control operator pools from only parallel and orthogonal filters ( Fig 6A ) . This minimal version of gain-control is sufficient to account , at least qualitatively , for all empirical PF estimates with no identifiable structural failure ( Fig 6 ) . On average , the model accounts for 94% of the variance in the aggregate data across 1D , Θ and SF conditions ( variance accounted for in the 2D condition is inevitably low at 0 . 27 due to the high density of the noise probe , which results in high measurement noise and a sparsely modulated PF; for this condition we rely on the reasonable qualitative match between fitted surfaces indicated by ovals in Fig 6B and 6F ) . This value is more than satisfactory when one considers that the relevant implementation involves no free parameters and can be deployed without prior estimate of the underlying 2D filter . To summarize , the gain-control model is able to rectify the inadequacy of the LN model in capturing orientation-tuned PF’s ( blue data points in Fig 6I fall below the unity line at p < 0 . 0002 ) , while at the same time retaining its ability to account for 2D/1D PF’s ( red/orange data points in Fig 5G scatter around the unity line at p = 0 . 43 ) and characteristic features of the SF-tuning data ( magenta data in Fig 6I scatter around the unity line at p = 0 . 9; see also Fig 6E ) . Despite its highly nonlinear nature , the gain-control circuit in Fig 6A operates in a manner well approximated by linear templates when projected onto and defined across each of the four dimensions probed by our experiments: Fig 7A plots indices returned by the two LN tests previously applied to the human data ( see Fig 3A ) ; there was no detectable sign of departure from template matching for any condition under either test ( black/gray histograms centred around 0 ) . This result is not due to lack of resolving power: when applied to the push-pull model ( Fig 7B ) both tests return positive distributions for the 1D condition , because this model generates mismatched target-present and target-absent estimates ( compare red with blue traces within left inset to 1D condition in Fig 7B; see also clear modulations of its second-order kernel , right inset ) . Neither test detects departures from LN transduction for the 2D condition , however this is not because the PF’s associated with the push-pull model were consistent with template matching , but rather because they were nearly featureless and therefore inconsistent with the human data ( 2D orange distribution in Fig 7B , reflecting first-order PF sructure , is centred around 0; see also Fig 5B and 5G ) . As a final step in cross-checking the applicability of the gain-control model , we computed its trial-by-trial predictive power and confirmed that it falls within the optimal range ( inset to Fig 4B ) : collectively across all estimates , human-model agreement falls significantly above/below the lower/upper bounds of the maximum predictability region ( p < 10−6/10−8 ) and does not differ from the midpoint between the two bounds ( p < 0 . 42 ) . The above detailed experiments were specifically designed with the objective of isolating a single elementary operator for cortical processing of visual signals . One stimulus feature associated with this effort involved placing two vertically oriented signal templates above and below the probe region ( see Methods and S1 Video ) . As explained previously , these templates served the purpose of prompting a matching strategy on the part of the human observers [48]; if observers could not implement a pure matching strategy ( as supported by our results ) , the target templates would at least prompt reliance on the read-out mechanism associated with the target signal , whether the horizontally oriented non-target signal was absent ( detection ) or present ( discrimination ) . In this manner , we hoped to facilitate experimental conditions where observers relied on the same elementary operation across different tasks , so that we could inspect the properties of said elementary operation in the presence of different stimulus conditions . The similarity between PF’s derived from the two tasks ( detection versus discrimination ) provides compelling evidence that we succeeded in this targeted effort . In the discrimination task , the optimal strategy for observers is to engage the difference between the elementary operator associated with extracting the vertically oriented target signal and the elementary operator associated with extracting the horizontally oriented non-target signal . However , because we deliberately steered observers towards relying only on the former operator ( while ignoring the latter ) via placement of the target templates , it is conceivable that the observed departure from the optimal strategy ( Fig 2E–2F ) may be a direct consequence of our targeted effort to bias their read-out machinery towards the just detailed suboptimal strategy . It may therefore be of interest to characterize the system under conditions in which the combined output of the two elementary operators is facilitated by the additional placement of non-target templates to the sides of the central probe region ( Fig 8A , green ) . Although both 2D and orientation PF’s associated with this ‘symmetric’ variant of the discrimination task largely resemble those obtained in the absence of the non-target templates ( compare Fig 8B and 8C with Fig 2E and 2G ) , there are important differences . First , the symmetric variant is associated with a measurable presence of the horizontal component . Although this effect is only mildly visible at the level of the aggregate 2D PF ( Fig 8B , left ) , it is more clearly exposed by the non-target match values obtained from individual observer PF’s . More specifically , all 5 observers who had not participated in the original version of the discrimination task ( that is in the absence of non-target templates ) returned negative match values ( all black data points in Fig 8D fall to the left of the vertical dashed line ) , indicating the presence of the negative non-target image expected under conditions where both vertical and horizontal elementary operators are engaged . This result was not observed for the original variant of the discrimination task ( open symbols in Fig 2F ) . The only observer who did not follow the pattern prompted by the new variant of the discrimination task ( grey data point in Fig 8D ) was also the only observer from this pool who had participated in the original variant; it is reasonable to interpret this finding as reflecting the possibility that this observer retained the read-out strategy she had previously developed and failed to readjust . A second important feature that characterizes the symmetric variant of the discrimination task is the appreciable difference between target-present and target-absent PF’s . Qualitative inspection of the target-present PF ( Fig 8B , top right ) suggests that it contains primarily if not exclusively vertical structure , while the target-absent PF ( Fig 8B , bottom right ) resembles more closely the difference between vertically-oriented and horizontally-oriented operators . These qualitative impressions are quantitatively confirmed by the lack of significant non-target content within target-present PF’s from individual observers ( red data points in Fig 8D scatter around the vertical dashed line ) and by the roughly equivalent content of target and non-target match within target-absent PF’s ( blue data points in Fig 8D scatter around the diagonal negative unity line ) . Orientation-tuned PF’s are suggestive of potentially related differences: although these effects appear slight upon qualitative inspection of aggregate PF’s ( compare red versus blue traces in Fig 8C ) , target-absent PF’s from individual observers present less marked peak values ( corresponding to the target orientation ) and more marked trough values ( corresponding to the non-target orientation ) than target-present PF’s ( blue data points are shifted down and to the left of red data points in Fig 8E ) . These differences between target-present and target-absent PF’s are indicative of a departure from the LN model [21 , 31–38] . We therefore expect that this departure should be measurable using the first-order/second-order nonlinear tests we have applied to previous data . Indeed , we not only find that taken together the two tests return significantly positive values ( at p < 0 . 002 ) , but also that they strongly correlate ( green data points in Fig 8I extend into the upper-right quadrant and display correlated scatter at r = 0 . 77 , p < 0 . 004 ) . No such effects are visible for the original variant of the discrimination task ( black data points in Fig 8I ) . We attempt to model these results by combining the modules developed in the previous section . The primary goal of this exercise is to exclude a role for template matching , not to simulate all details of the dataset . For this reason , we do not attempt to capture the observed differences between target-present and target-absent PF’s . The LN model involves subtracting the output of the horizontal template matcher from the output of the vertical template matcher . Because the empirical 2D PF contains more vertical than horizontal structure ( Fig 8D ) , we halved the output of the horizontal template matcher before applying the subtraction in order to improve its ability to simulate the 2D condition ( see Methods ) . For this model , we know that target-present and target-absent PF’s do not differ [21 , 31–38] ( not shown ) . The output reduction applied to the horizontal component translates into a reduced trough within the orientation-tuned PF ( magenta in Fig 8G ) . If we do not apply output reduction , the LN model successfully returns trough and peak of equal amplitude for the orientation-tuned PF as observed experimentally , but fails to capture the unbalanced structure of the 2D PF . In other words , the LN model is able to account for either condition ( 2D versus orientation ) in isolation , but not both concomitantly ( similar to what we found previously ) . The gain-control model fared substantially better . Again , we subtracted a horizontally-tuned gain-control circuit from the vertically-tuned gain-control circuit developed previously ( Fig 6A ) . As with the LN model , the output of the horizontally-tuned circuit was halved before subtraction . This model captures both 2D and orientation-tuned PF’s , in that it produces trough and peak of equal amplitude and overlaps fully with the human data ( Fig 8F–8G ) . Although it is therefore superior to the LN model ( Fig 8H ) , we find that it does not generate appreciably different target-present versus target-absent PF’s ( top/bottom right in Fig 8F ) , failing to account for this specific feature of the human data . It is conceivable that this failure may be ameliorated by more elaborate variants of the gain-control model , however this is not our goal . As we have explained above , due to its mixed nature , the read-out mechanism engaged by observers in the symmetric variant of the discrimination task is not ideal for achieving the goal of excluding/supporting the LN model using a non-parametric approach . Although it is certainly interesting to consider this variant and how it may lead to partially different results , our core conclusions rely on the main detection/discrimination tasks; under those conditions , a single elementary operator can be characterized in isolation .
It is uncontroversial that human vision often displays highly nonlinear characteristics [54] , yet the linear-nonlinear model retains a paramount role in shaping past and current accounts of this fundamental sensory process . There are at least two reasons for its popularity . First , the presence of an output static nonlinearity combined with a judicious choice of input space for stimulus projection often allow for effective and compact accounts of apparently more complex phenomena . A fitting example is motion detection , an inherently nonlinear process [55] . In the retina , this phenomenon is typically modelled using Reichardt cross-correlation [56] , a nonlinear scheme that combines the output of multiple ( at least two ) elementary units [57] . Although this model does not conform to the LN scheme with reference to its original structure , it can be recast in the form of a linear oriented spatiotemporal filter followed by a static nonlinearity [58]; indeed , the latter scheme is more commonly adopted to account for cortical and behavioural processes [59 , 60] . Second , LN models are often adequate for qualitative thinking and descriptive purposes , particularly when the system is challenged under a limited range of experimental conditions . This approach is exemplified by popular accounts of contrast-based illusions like the Hermann grid phenomenon [61] , where the associated phenomenology is referred back to LN models incorporating front-end linear filters with an inhibitory surround [11] . Under some conditions , these accounts can be exploited to some degree of quantitative interpretation , providing for example a rationale for the broad agreement between filter size estimated from the above model of the Hermann grid illusion , and corresponding neuronal receptive field size measured electrophysiologically at ranging eccentricities [62] . Practically speaking , the LN model is attractive thanks to its analytical tractability . Combined with controlled input stimulation , this model makes simple predictions that have been extensively exploited to support characterization of the front-end linear filter [4 , 16 , 63] . Indeed , transparent interpretability of most measurements presented in this study is largely compromised wherever the LN model becomes inapplicable , although we have demonstrated here and in previous work that adequate tools exist for tackling such situations [10] . It is therefore clear that LN models are both useful and desirable , but it is also clear that they may fail under a range of conditions for quantitative purposes . We currently have no clear indication of when such failures may occur , how widespread they may be , and to what extent they may impact quantitative conclusions regarding human sensory processing . Only a few studies have examined this issue in sufficient detail [26 , 36 , 37 , 44] , and none has carried out an extensive characterization that would deliver a comprehensive picture across substantially different aspects of the same stimulus/task . As we discuss below , an integrated approach of the latter kind does not merely represent a quantitative extension of previous efforts , but rather enables a qualitatively different level of dissection of the relevant mechanisms and supports novel conclusions not available to previous studies . The present study represents an attempt to determine whether there is at least one limited set of identifiable conditions under which human vision engages exclusively LN circuits . It is not intended as an attempt to determine whether the entirety of human visual processing can be reduced to LN transduction: as indicated above , an attempt of this kind would be fool-hearted , because it is inconceivable that the whole of vision would involve no more than template matching . Rather than asking whether all visual operations are template matchers , we ask whether there is at least one visual operation that involves template matching . We reasoned that the strongest and most relevant test of the latter possibility would involve task and stimulus specifications that are not only representative of core interests in vision science [43] , but also probe elementary operations supported by visual cortex [64] using experimental protocols specifically designed to facilitate LN transduction ( see below for further discussion of these points ) . There is a sense , of considerable practical significance , in which our results provide encouraging evidence to support the applicability of LN models for understanding and characterizing human pattern vision: under all the experimental conditions we tested , the human process could be adequately described in the form of a simple LN model applied to the dimension probed by the perturbation associated with a given noise mask . This result enables a wide range of tools that have been tailored to the LN family [4 , 16 , 65] . It must be emphasized that the conditions of the detection/discrimination experiments presented here were carefully adjusted to prompt a template-matching strategy on the part of human participants . As we have demonstrated with the symmetric variant of the discrimination task ( Fig 8 ) , apparently irrelevant methodological details ( e . g . inclusion of target/non-target replicas adjacent to the probe ) may impact the extent to which these experiments are representative of a wider range of specifications: it goes without saying that , as more elementary operators and processing layers are loaded onto the read-out stage , the collective process ( which in the limit will encompass the whole of vision ) will inevitably manifest departures from LN transduction . There is however a different sense , arguably of greater theoretical significance , in which our results are not equally supportive of LN modelling schemes: it is the sense of understanding the deeper structure of the system beyond its superficial compliance with LN transduction in the manner discussed above ( see [63] for related pursuits in neuronal modelling ) . In this sense , we were unable to identify a single implementation of a specific LN model that would capture all aspects of our complex dataset ( Fig 5; see related results from electrophysiological recordings of simple cells [24 , 53] ) . This failure was structural in that it involved qualitative departures from the human data that could not be ameliorated via further exploration of parameter space . Outside the LN family , we successfully identified a viable candidate by incorporating a canonical computation for cortical circuits: gain control via divisive normalization [19] . The applicability of this operation to neural processes is extensively documented [52 , 66] . The success of this model in capturing our own data therefore conforms to current trends in the computational literature . The non-parametric logical/analytical process by which we mustered support fo the gain-control model differs in its outlook from previous attempts based on fitting multiparametric models [67–70]; nevertheless , it is notable that gain control circuits feature prominently in those studies too . Is it conceivable that a comparable set of experiments might have been identified that did not require any modelling tools outside the LN family ? This seems unlikely . As mentioned earlier , extensive piloting went into ensuring that template matching would be encouraged as exhaustively as feasible on the part of human participants , leaving little room for further tailoring of stimulus specifications to favour LN strategies . Furthermore , the function probed by our protocol is elementary: Gabor wavelets represent some of the most efficiently detectable visual patterns [64] , consistent with the notion that their structure resembles neuronal preference in cortex [71] . Any detection/discrimination task relevant to human visual function would presumably involve combinations of analogous operations [72 , 73] , leading to the expectation that it would display at least an equivalent , if not more pronounced , degree of departure from the LN model . This is indeed the result we observed when we altered the discrimination protocol to prompt a strategy whereby observers would combine two elementary operations ( Fig 8 ) . Based on the above considerations , we believe that our experiments are ideally positioned to draw conclusions about the applicability of LN models to human vision: if there exist any conditions , no matter how limited , under which such models are applicable , those conditions would include the specifications probed by our protocols . The outcome of our experiments enables one characterization for the underlying mechanism while excluding a number of alternative scenarios , all nonetheless viable and plausible . In other words , it would have been entirely reasonable to expect and observe a substantially different outcome . Under one scenario , the orientation and SF tuning characteristics returned by our PF measurements may have conformed to those predicted by the specified 2D version of the LN model ( blue traces in Fig 5D and 5E ) , leaving open the possibility that the system was exclusively engaging LN circuits across the board . Under a different scenario , the system may have operated in the manner of the push-pull model outlined in Fig 5F ( see [51] for a concrete example of the applicability of this model to data from a discrimination task closely related to the one used here ) : lack of compliance with LN transduction would have then become apparent from applying the linearity tests to one condition alone ( 1D , see Fig 7B ) , yielding the conclusion ( opposite to the one immediately above ) that the system was engaging more elaborate circuitry than LN components , and furthermore that the design of such circuitry did not support LN behaviour even when restricted to individual probes ( as we observed for the symmetric variant of the discrimination task , see Fig 8D–8E and 8I ) . Both our findings , i . e . that system structure does not conform to LN circuitry and yet complies with LN transduction under varying conditions , are therefore independent contributions that place important constraints on the range of plausible scenarios potentially associated with the visual processes examined here . It is relevant in this respect that our ability to dissect the underlying mechanisms with adequate discriminatory power specifically relied on evaluating different features of our combined dataset: had we considered each condition in isolation ( e . g . only the 2D results or only the orientation-tuning results ) , it would have been impossible to constrain our conclusions to the extent that was enabled by the integrated analysis presented here . A potentially productive way of summarizing our results may be obtained via reference to the simple concept of locally linear approximation for nonlinear functions . As illustrated by the cartoon in Fig 9 , we can think of the visual process as a manifold spanning a space that encompasses all possible dimensions across which the stimulus may be usefully represented ( clearly such high dimensional spaces cannot be adequately represented in a 2D cartoon , so Fig 9 is only intended as an intuitive tool and not an accurate description of the process ) . When projected onto a specific subspace ( e . g . orientation or spatial frequency ) , and when inspected with respect to that restricted subspace , the behaviour exhibited by the process may be satisfactorily approximated by the LN framework , even though this framework may not be adequate to describe the process as a whole , i . e . with respect to its collective characteristics across multiple projections . Our results indicate that the operations of human vision , no matter how elementary and limited in their immediate scope ( e . g . detection of a Gabor wavelet ) , cannot be reduced to a straight pipe through Fig 9: they retain an irreducible level of nonlinear structure possibly reflecting the minimal functional characteristics implemented by cortical circuits [5 , 19 , 52 , 53] . Fig 9 may misleadingly suggest that the introduction of different noise masks in our study is equivalent to the expansion of stimulus range afforded by previous investigations that manipulated e . g . stimulus uncertainty [20 , 21 , 37] and/or pedestal contrast [67 , 74] ( these two factors being potentially intertwined [20 , 74–76] ) . From the perspective of those and related studies , it may seem trivial that linearity breaks down as stimulus range is expanded . There is however a critical difference with respect to our approach , in that most previous studies manipulated the range/specification spanned by the signal to be detected , thereby potentially prompting the system to engage different modules/regimes to perform the assigned task [36] . The target signal to be detected in our experiments was fixed and uniquely specified , regardless of the noise perturbation that was added to it . Under these conditions , observers were prompted to engage a single elementary perceptual operator . Furthermore , the effects of expanding stimulus range have often been modelled via LN cascades with a sigmoidal static nonlinearity and/or one that changes exponent [13 , 74 , 77] , but which nevertheless perform LN transduction; our results exclude LN models regardless of the specific characteristics associated with the static nonlinearity , and regardless of whether those characteristics may differ for different noise types . Finally , the notion that linearity should break down as stimulus range is expanded implicitly relies on the assumption that linearity does apply in the first place within a restricted input range; this assumption has never been adequately checked , at least not to the extent afforded by the experiments/analyses presented here . With these caveats in mind , Fig 9 is best interpreted not as the trajectory of a perceptual system that traverses different stimulus regimes and potentially modifies its characteristics along the way , but rather as the intrinsic structure of an elementary process operating within a minimally defined input range . The combined application of different noise probes delivers a multifaceted view of this process that is not afforded by each individual probe in isolation . It may seem counterintuitive that an inherently nonlinear architecture would be in place for it to behave linearly with respect to substantially different visual dimensions , such as 1D space or orientation as probed by our stimuli . If linear transduction is the goal , why not implement it using the template matcher in Fig 5A ? If conversely nonlinearity is the goal , why build a nonlinear system that retains such degree of linear transduction as we measured here , and not adopt the push-pull circuit in Fig 5F regardless of its highly nonlinear transduction properties ( Fig 7B ) ? Our results suggest that , at least under the conditions of our experiments , the system strives to achieve linear encoding: it seems otherwise difficult to explain why we observed such extensive compliance with template matching for processing 4 different noise probes across 2 separate tasks , when prior studies have exposed clear deviations under more limited conditions [4 , 21 , 34 , 36–38] . Although elementary template matchers like the mechanism in Fig 5A support linear transduction , they may not be adequate for the purpose of versatile stimulus encoding in ways that are both linear and useful; in this context , utility may involve the necessity to represent orientation in a balanced push-pull fashion as we observed in our experiments , a goal that cannot be achieved by the circuit in Fig 5A ( see blue traces in Fig 5D and 5I ) . An alternative strategy , supported by our findings , involves assembling small nonlinear circuits that support effective stimulus encoding ( e . g . push-pull orientation selectivity as in Fig 2C and 2G and sharp bandpass SF tuning as in Fig 2D ) while at the same time retaining linearity across a wide range of tasks and stimulus perturbations [24 , 52] . Divisive normalization has proven an effective tool for efficient transduction while maintaining gain within near-linear regimes [19]; Fig 9 elaborates on this property to encompass the collective space of extended stimulus projection for multi-feature encoding ( with the caveats outlined above ) . Although this interpretation is highly speculative , its proposed mode of operation is known to underlie other aspects of sensory processing [66 , 78] , in particular retinal encoding of ON/OFF signals: in the retina , linearity is a luxury that comes at the cost of carefully assembled nonlinear subunits [79] . Our results suggest that similar principles may apply to some cortical computations [80] .
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Any attempt to model human vision must first ask: can it be approximated by a process that linearly matches the visual stimulus with an internal template ? We often take this approximation for granted without properly checking its validity . Even if we assume that the approximation is valid under specific conditions , does this mean the system operates template matching across the board ? We would not know exactly in what sense and to what extent the approximation may be viable . Our results address both issues . We find that template matchers are locally applicable in relation to a wide range of conditions , providing much-needed justification for several relevant computational tools . We also find , however , that there is no sense in which the system is globally a linear template: it remains inescapably nonlinear . Our findings suggest that linear transduction is not cost-free: it is not a default building block that is used for constructing expensive nonlinear processes . Rather , linear sensory representations arise from carefully constructed nonlinear processes that strike a balanced act between the necessity to retain other important computations , and the desirability of transducing and representing the visual world on a linear scale .
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[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
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The Elementary Operations of Human Vision Are Not Reducible to Template-Matching
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Parasitic nematodes are of medical and veterinary importance , adversely affecting human health and animal welfare . Ascaris suum is a gastrointestinal parasite of pigs; in addition to its veterinary significance it is a good model of the human parasite Ascaris lumbricoides , estimated to infect ∼1 . 4 billion people globally . Anthelmintic drugs are essential to control nematode parasites , and nicotinic acetylcholine receptors ( nAChRs ) on nerve and muscle are the targets of cholinergic anthelmintics such as levamisole and pyrantel . Previous genetic analyses of nematode nAChRs have been confined to Caenorhabditis elegans , which is phylogenetically distinct from Ascaris spp . and many other important parasites . Here we report the cloning and expression of two nAChR subunit cDNAs from A . suum . The subunits are very similar in sequence to C . elegans UNC-29 and UNC-38 , are expressed on muscle cells and can be expressed robustly in Xenopus oocytes to form acetylcholine- , nicotine- , levamisole- and pyrantel-sensitive channels . We also demonstrate that changing the stoichiometry of the receptor by injecting different ratios of the subunit cRNAs can reproduce two of the three pharmacological subtypes of nAChR present in A . suum muscle cells . When the ratio was 5∶1 ( Asu-unc-38∶Asu-unc-29 ) , nicotine was a full agonist and levamisole was a partial agonist , and oocytes responded to oxantel , but not pyrantel . At the reverse ratio ( 1∶5 Asu-unc-38∶Asu-unc-29 ) , levamisole was a full agonist and nicotine was a partial agonist , and the oocytes responded to pyrantel , but not oxantel . These results represent the first in vitro expression of any parasitic nicotinic receptor and show that their properties are substantially different from those of C . elegans . The results also show that changing the expression level of a single receptor subunit dramatically altered the efficacy of some anthelmintic drugs . In vitro expression of these subunits may permit the development of parasite-specific screens for future anthelmintics .
Nematodes of the genus Ascaris are large ( ∼30 cm ) gastrointestinal parasites of swine ( Ascaris suum ) and humans ( Ascaris lumbricoides ) . Ascaris lumbricoides infects ∼1 . 4 billion people globally , and is particularly prevalent in conditions of poor sanitation and poverty [1] , [2] . In addition to directly causing morbidity ( such as malnutrition ) and mortality ( via obstruction of the gut or bile duct ) , A . lumbricoides infection also exacerbates other diseases prevalent in impoverished communities such as malaria , tuberculosis and AIDS [3] , [4] . Ascaris suum is a major parasite of pigs , causing serious economic losses for farmers [5] . It could also be considered as a good model of the human parasite and in some cases may infect humans directly as a zoonosis [6] , [7] . At present , control of helminth infections in both animals and humans relies on administration of anthelmintic chemotherapeutic agents . The nematode nicotinic acetylcholine receptors ( nAChRs ) on muscle are the target of the cholinergic anthelmintics levamisole , pyrantel and oxantel; such compounds cause these ligand-gated ion channels to open , leading to prolonged muscle contraction and spastic paralysis of the parasite [8] . The importance of nematode nAChR as drug targets has been underlined by the recent announcement of a new class of cholinergic anthelmintics , the amino-acetonitrile derivatives , though these do not seem to act on muscle nicotinic receptors [9] . The relative ease with which the large muscle cells of A . suum can be manipulated has permitted electrophysiological characterisation of the native nAChRs . This has shown that 3 distinct pharmacological nAChR subtypes are present on Ascaris muscle cells , with different agonist and antagonist sensitivities: an L-subtype most sensitive to the agonists levamisole and pyrantel , an N-subtype most sensitive to nicotine , oxantel and methyridine , and a B-subtype most sensitive to bephenium [10]–[12] . Nicotine- and levamisole-sensitive nAChR have also been identified in the model nematode C . elegans , where the genes unc-38 , unc-29 , unc-63 , lev-1 and lev-8 encode nAChR subunits involved in levamisole sensitivity [13] . Heterologous expression of unc-38 , unc-29 and lev-1 in the Xenopus oocyte system produces a functional levamisole-sensitive nAChR , but the currents have a small amplitude suggesting the need for additional components [14]–[16] . Recently Boulin et al [17] have reported that eight gene products , the five receptor subunits and three ancillary proteins ( RIC-3 , UNC-50 and UNC-74 ) are required to reconstruct the L-type receptor from C . elegans . This receptor is completely insensitive to nicotine , but nicotine-sensitive receptors are present in C . elegans and are formed by a pentamer of ACR-16 subunits , which express robustly in Xenopus oocytes [18] . However , Ascaris spp . and several other medically important parasites ( for instance Brugia malayi and Onchocerca volvulus , which cause lymphatic filariasis and river blindness ) belong to a phylogenetic group ( clade III ) which is only distantly related to C . elegans , which is a member of clade V [19] . We have previously reported that many nAChR subunit genes found in C . elegans are absent from the B . malayi genome [20] . Of particular relevance is the absence of sequences similar to the nAChR subunit genes lev-1 and lev-8 , which encode subunits of the C . elegans L-type receptor , and a greatly reduced number of genes encoding subunits similar to ACR-16 , which makes up the N-type receptor [21] . This analysis suggests that the subunit composition of the L- and N-type nicotinic receptors may be quite different in the pathogenic clade III nematodes from that found in C . elegans . However , there have been very few molecular studies on parasitic nematode nicotinic receptors , and their subunit composition has not been determined to date . We have therefore been following up this bioinformatic analysis with molecular studies , with the intention of recapitulating the L- and N-subtypes of the A . suum nAChR in vitro . Here we report the identification of A . suum orthologues of unc-38 and unc-29 , and confirm , using antibody labelling , their presence and co-localisation in A . suum muscle cells . When expressed in Xenopus oocytes they form functional levamisole-sensitive nAChR . By altering the ratio at which the two cRNAs encoding these subunits are injected into the oocytes , receptors with different pharmacological profiles can be produced that respond to different nicotinic anthelmintics . The pharmacology of these receptors resembles the L- and N-subtypes present in native A . suum muscle cells .
In order to obtain cDNAs encoding nicotinic receptor subunits from A . suum , we first aligned the amino-acid sequence of the C . elegans UNC-38 and UNC-29 subunits with their predicted orthologues from B . malayi . A partial cDNA sequence from A . suum similar to C . elegans unc-38 was found in the database ( Accession number AJ011382 ) and used to design specific primers to extend the sequence . Degenerate primers based on well conserved regions of amino-acid sequence were used to amplify a partial cDNA clone of the unc-29-like gene from A . suum ( Figure 1 ) . 5′ and 3′ RACE procedures [22] were then used to obtain the remainder of the sequences . Full-length cDNAs encoding the A . suum orthologues of unc-38 and unc-29 were then amplified using primers specific for the 5′ and 3′ termini , cloned , sequenced and deposited in GenBank with the accession numbers EU053155 and EU006073 . The predicted sequences of the subunits , designated Asu-UNC-29 and Asu-UNC-38 ( using the nomenclature proposed in http://www . wormbase . org/wiki/index . php/UserGuide:Nomenclature_nematode ) are shown in Figure 1 , together with an alignment with the C . elegans and predicted B . malayi subunits . The residues within the predicted loops that make up the ligand-binding site [23] , predicted signal peptide and transmembrane domains , and the residues predicted to confer cation specificity on the ion channel are highlighted . The sequences predict that Asu-UNC-38 is an α subunit , since it contains the typical Y-X ( X ) -CC ACh binding motif ( residues 185–190 ) in loop C of the ligand-binding site , whereas Asu-UNC-29 , lacking this motif , has all the characteristics of a non-α subunit , with complementary binding residues . Asu-UNC-38 , like its C . elegans equivalent , also possesses the amino-acid residues reported to be necessary for activation by levamisole at positions 153 ( glutamate ) and 191 ( proline ) of the mature polypeptide [24] . The designation of these subunits as the A . suum equivalents of UNC-29 and UNC-38 is based on the results of BLASTP searches and pair-wise sequence comparisons with the C . elegans nicotinic receptor subunits ( Table 1 ) . If Asu-UNC-29 and Asu-UNC-38 are components of the A . suum L- or N-type nicotinic receptors , they should be expressed on muscle cells . We therefore raised specific anti-subunit antibodies , using synthetic peptides from the N-terminal region of the predicted mature subunits as immunogens ( Figure 1 ) . The resultant antibodies were purified by affinity chromatography and used to perform indirect immunofluorescence on whole muscle cells obtained by collagenase treatment of A . suum . The anatomy of Ascaris spp . muscle cells and neuromuscular junctions is somewhat different from that of mammals [25] , in that the muscle cells send out processes , muscle arms , which make contact with the neurons within the nerve cord; the neuromuscular junctions are thus at the ends of the muscle arms . Figure 2A shows that the anti-Asu-UNC-38 antibody labelled the whole surface of isolated muscle cells , including the muscle arms ( indicated by arrow A ) , the bag region , which is used in the preparation of the patches used in the single channel recordings [10]–[12] , and spindle which is the contractile region . The same antibody also labelled portions of the nerve cord ( Figure 2B ) and portions of the muscle arms that had remained attached to the cord during sample preparation . Antibodies against both subunits labelled the muscle cell membrane in the muscle arm region which leads from the muscle cell to the nerve to form the neuromuscular junction ( Figure 2C ) . The antibody labelling also showed that the expression of the two subunits overlapped , suggesting they may be components of the same receptor . All of these results are consistent with these two subunits being components of a muscle receptor , which is expressed extra-synaptically and synaptically at the neuromuscular junction . Since the two subunits seemed to co-localise on muscle cells and may thus be predicted to co-assemble in the same receptors , we injected cRNAs encoding the two polypeptides into Xenopus oocytes . Two-electrode voltage clamp experiments showed that Asu-UNC-38 and Asu-UNC-29 formed a heteromeric receptor when expressed in these cells . The results are shown in Figure 3: injection of neither cRNA produced functional receptors when injected into Xenopus oocytes alone , but when equal amounts of cRNAs encoding both subunits were injected , functional nAChRs were produced which gave a robust ( ∼800 nA ) response to 100 µM acetylcholine ( Ach ) . The receptors also responded to 100 µM levamisole and 100 µM nicotine , and all of these responses could be reversibly blocked by application of 10 µM mecamylamine , a broad-spectrum antagonist of nAChRs known to inhibit ACh-induced contraction of Ascaris muscle [26] ( Figure 3 ) . Dose-response relationships were established for acetylcholine , nicotine and levamisole , and all responses normalised to the response to 100 µM ACh . Sigmoidal dose-response curves were fitted to the data using GraphPad Prism software ( Figure 3C ) , and show that the order of agonist efficacy is levamisole>ACh>nicotine ( Table 2 ) , consistent with earlier voltage-clamp observations on native A . suum muscle tissue [8] , [26] . The dose-response curves were rather shallow , with estimated Hill numbers of 0 . 63 for acetylcholine , 0 . 64 for levamisole and 0 . 77 for nicotine . Two explanations seemed possible; either the receptors showed negative co-operativity , which would be unusual for nicotinic receptors , or a mixed population of receptors , with differing EC50 for the agonists , was being produced . There is precedence for the second explanation from studies on mammalian neuronal nicotinic receptors composed of α4β2 subunits [27] , [28] . The pentameric nature of the nAChR implies that when two cRNAs encoding different subunits are injected into oocytes at an equimolar ratio , a mixed population of receptors is likely to be produced ( potentially ( Asu-UNC-38 ) 5 , ( Asu-UNC-38 ) 4∶ ( Asu-UNC-29 ) 1 , ( Asu-UNC-38 ) 3∶ ( Asu-UNC-29 ) 2 , ( Asu-UNC-38 ) 2∶ ( AsuUNC-29 ) 3 , ( UNC-38 ) 1∶ ( UNC-29 ) 4 , ( UNC-38 ) 0∶ ( UNC-29 ) 5 ) . We have already shown that molecules containing the subunits in the ratios 5∶0 and 0∶5 Asu-UNC-38∶Asu-UNC-29 do not form functional receptors ( Figure 3A ) . The shallow slope of the dose-response relationships recorded in Figure 3C is also suggestive of a mixed population of receptors . In order to resolve this , we varied the ratio at which the Asu-unc-38 and Asu-unc-29 cRNAs were injected into the oocytes . Based on data from the mammalian neuronal nAChRs , this should cause the formation of a population of receptors with predominantly ( UNC38 ) 3∶ ( UNC29 ) 2 , ( UNC38 ) 2∶ ( UNC29 ) 3 stoichiometries . Oocytes injected with the two cRNAs at ratios of 10∶1 and 1∶10 produced only very small currents in response to acetylcholine , but injection at ratios of 5∶1 and 1∶5 resulted in robust currents in response to agonist . Injection of the cRNAs in both ratios ( 5∶1 and 1∶5 ) produced receptors with comparable responses to ACh , but a striking difference in the agonist efficacy of levamisole and nicotine was observed between the two ratios ( Table 2 ) . Figure 4 shows the dose-response relationships for levamisole and nicotine ( normalised to the response to 100 µM ACh ) for the 5∶1 and 1∶5 ( Asu-unc38∶Asu-unc-29 ) cRNA ratio experiments . When the ratio was 5∶1 Asu-unc-38∶Asu-unc-29 nicotine was a full agonist , whereas levamisole was only a partial agonist , with a maximal response of only 60% of that of ACh ( Table 2 ) . When cRNAs at a ratio of 1∶5 Asu-unc-38∶Asu-unc-29 were used , levamisole was now a full agonist ( with a maximal response of 130% of that of ACh ) , and nicotine was now only a partial agonist with a maximal response of only 45% ( Table 2 ) . Additional experiments were performed to further characterise the pharmacology of the different receptors produced using 5∶1 and 1∶5 ratios of Asu-unc-38∶Asu-unc-29 cRNAs . Pyrantel and oxantel , in addition to their respective specificity for the L- and N- subtypes native nAChR found in A . suum [11] , are anthelmintic compounds of medical and veterinary importance . The dose-response relationships observed using these compounds were more complex than observed for the other agonists as both oxantel and pyrantel are known to be agonists of native A . suum nAChRs at lower concentrations , but open channel blockers at higher concentrations , producing an asymmetric bell-shaped dose-response relationship [8] , [29] . The results for these experiments are shown in Figure 5 , and show dramatic differences in agonist efficacy between the receptors with different stoichiometries . The oocytes injected with a 5∶1 ratio of Asu-unc-38∶Asu-unc-29 cRNAs responded to oxantel , giving a maximal response at ∼10 µM , but gave very little response to pyrantel . The oocytes injected with a 1∶5 ratio of Asu-unc-38∶Asu-unc-29 cRNAs were more sensitive to pyrantel , giving a maximal response at ∼1 µM , but gave a negligible response to oxantel . Maximal responses in both cases were comparable to the maximal response to ACh ( Table 2 ) , though the steep nature of the dose-response curves suggests that full agonist activity was not reached before the channel blocking action began to affect response size . The specificity of levamisole and pyrantel for the receptor population formed from a 1∶5 Asu-unc-38∶Asu-unc-29 cRNA ratio matches the pharmacological properties of the native A . suum L-subtype nAChR , whereas the specificity of nicotine and oxantel for the receptor population formed following injection of a 5∶1 cRNA ratio resembles the pharmacology of the N-subtype nAChR .
The generation of functional levamisole-sensitive nAChRs using the A . suum orthologues of UNC-38 and UNC-29 shows that , as in C . elegans , these subunits are likely to be components of the native levamisole-sensitive nAChR [15] . However there are clear and striking differences between the two species in terms of the number of gene products that need to be expressed in order to reconstitute a robust levamisole response . In C . elegans , 3 other nAChR subunits and three ancillary proteins are necessary for the full levamisole response: LEV-1 , LEV-8 , UNC-63 , RIC-3 , UNC-50 and UNC-74 [17] . We have previously shown using a bioinformatic approach and the genome of B . malayi , a parasite closely related to A . suum , that lev-1 and lev-8 are apparently absent from this group of nematodes; however a sequence related to unc-63 is apparently present in B . malayi [21] , though its function is currently unknown . In addition , Asu-UNC-29 and Asu-UNC-38 form nicotine-sensitive receptors . In C . elegans nicotine sensitivity is conferred by ACR-16 subunit-containing receptors [18] , and the levamisole-sensitive receptor is completely insensitive to nicotine [17] . We have so far been unable to identify any sequences clearly orthologous to acr-16 in A . suum or B . malayi [21] , though it is possible that other nAChR subunits may fulfil an analogous function in the parasites: in C . elegans acr-16 belongs to a fairly large sub-family of nAChR subunit genes [30] , and the other parasite genomes seem to possess at least one member of this sub-family [21] . These results demonstrate very clear and marked differences between the nAChR of C . elegans and an important parasite and emphasise the importance of studying anthelmintic receptors in target species in addition to model organisms . They also raise the possibility that parasite-specific screens for novel cholinergic anthelmintics could be developed via the expression of the parasite receptor subunits . The two subunit sequences possessed all of the features we would expect from functional equivalents of C . elegans UNC-38 and UNC-29 , including the amino-acids thought to confer sensitivity to low concentrations of levamisole [24] . Immunoflourescence labelling of isolated A . suum muscle cells and nerve cords supported the proposal that the two subunits are expressed on muscle cells , including the neuromuscular junction . Nicotinic receptors in A . suum muscle , unlike those in C . elegans , are not clustered solely at the neuromuscular junction , but are present all over the cell membrane , as shown both in Figure 2 and by the fact that it is possible to make single-channel and intracellular recordings from the bag region of the cell [10]–[12] , [31] . We could express cRNAs encoding the two A . suum subunits in Xenopus oocytes to form receptors with many of the properties reported for the muscle nicotinic receptors; they were sensitive to aceylcholine , levamisole and nicotine . However , injection of the two subunit cRNAs into the oocytes at different ratios generated distinct receptor populations . This effect was more obvious when ratios of 1∶5 and 5∶1 ( Asu-unc-38∶Asu-unc-29 ) were injected; injection of the two cRNAs at ratios of 1∶10 and 10∶1 produced very small currents which were impossible to study further . Though several different populations could theoretically be generated in these experiments [32] ( Figure 6 ) , previous data from the mammalian α4β2 nAChR suggests that the most likely combinations of two subunits to yield functional receptors will be in the ratios 2∶3 and 3∶2 [27] , [28] , [33] and this is supported by our difficulty in producing robust responses when injecting at a ratio of 10∶1 , which would theoretically produce more receptors of a 4∶1 than 3∶2 subunit stoichiometry . If the pharmacology of A . suum nAChRs can be altered simply by generating different combinations of two subunits , this may help to explain how sufficient pharmacological and neurological complexity can be found in parasites with remarkably few nAChR genes compared to C . elegans . Pharmacological diversity generated by alternate stoichiometries has not previously been observed in invertebrates , but is well described for heterologous expression of the mammalian neuronal nAChR subunits α4 and β2 . Receptors with the stoichiometry ( α4 ) 3 ( β2 ) 2 have lower agonist affinity and a different pharmacological profile to receptors with the stoichiometry ( α4 ) 2 ( β2 ) 3 , and addition of an accessory subunit to give ( for example ) an ( α4 ) 2 ( β2 ) 2 ( α5 ) combination confers additional pharmacological differences [27] , [28] , [33] . In extrapolating the mammalian α4β2 model to interpret the results presented here , we suggest that we have generated receptor subtypes that have the stoichiometry ( Asu-UNC-38 ) 2 ( Asu-UNC-29 ) 3 , and ( Asu-UNC-38 ) 3 ( Asu-UNC-29 ) 2 . If our results are compared with those obtained from single channel recordings on A . suum muscle [11]–[13] , our data are consistent with a suggestion that ( Asu-UNC-38 ) 2 ( Asu-UNC-29 ) 3 constitutes the L-subtype nAChR and that ( Asu-UNC-38 ) 3 ( Asu-UNC-29 ) 2 forms the N-subtype . The complex mixture of receptors present in the in vivo preparation makes a quantitative comparison with our in vitro data difficult , but there are clear qualitative similarities . The L-subtype of the native receptor is preferentially activated by levamisole , though nicotine does also open L-type channels , and vice versa for the N-subtype; our oocyte data show that nicotine is only a partial agonist at the putative ( Asu-UNC-38 ) 2 ( Asu-UNC-29 ) 3 receptor and levamisole is , similarly , only a partial agonist at the putative ( Asu-UNC-38 ) 3 ( Asu-UNC-29 ) 2 receptor . There are also clear similarities in the effects of the anthelmintics , pyrantel and oxantel , between our results and the single channel measurements; pyrantel acts at the same sub-type that levamisole prefers and oxantel at the one preferred by nicotine . We believe , therefore , that we have recapitulated many of the properties of the L- and N-subtypes by varying the expression levels of the two A . suum subunits within the oocyte system . These results have important implications for the development and management of parasite resistance to cholinergic anthelmintics; it has previously been assumed that parasites resistant to levamisole or pyrantel may still be susceptible to oxantel as different receptor sub-types are involved [11] , but if both receptor types contain the same subunits , then the potential for development of resistance to both classes of cholinergic anthelmintic is greater . Kopp et al [34] have recently reported changes in the expression of hookworm nicotinic receptor subunit mRNAs that may be associated with pyrantel resistance . An important open question concerns the function of the A . suum UNC-63 subunit . Despite exhaustive efforts , so far we have been unable to generate a cDNA clone encoding an obviously full-length UNC-63 subunit . This may be due to purely technical difficulties , or it may that , in A . suum , unc-63 encodes only a truncated mRNA . It is tempting to speculate that the B-subtype nAChR has the composition ( Asu-UNC-38 ) 2 ( Asu-UNC-29 ) 2 ( Asu-UNC-63 ) 1 , by analogy with the ( α4 ) 2 ( β2 ) 2 ( α5 ) 1 receptors found in mammals [33] , but we have no direct evidence to support this speculation . In summary , we have demonstrated that the A . suum homologues of UNC-38 and UNC-29 co-express in Xenopus oocytes to form a functional nAChR , which is the first successful expression in vitro of a nAChR from parasitic nematodes . This will create opportunities for future work to be performed , where for the first time candidate drug resistance mutations in parasite nAChR subunits can be assessed directly for their effect on anthelmintic efficacy and receptor function . This work also paves the way to an accelerated understanding of anthelmintic drug targets and the potential development of parasite-specific target-based screens for new compounds .
All animals used for production of antisera were handled in strict accordance with good animal practice and the conditions defined by the United Kingdom Home Office ( Harlan , UK ) or the United States Department of Agriculture ( Sigma Genosys ) . Partial sequences of Ascaris suum unc-38 and unc-29 were amplified using primers designed on a partial sequence of Asu-unc-38 present in GenBank ( AJ011382 ) ( Primer sequences GTCGCGCTTACCGTTTTCTTCC and CCATCGCCACATATTTCCAGTCTT ) and using degenerate primers based on an alignment of the unc-29 sequences from C . elegans and Brugia malayi ( primer sequences ATCAAYGTNGAYGARAARGAYCA and ATYTCRTTYTCRTTRTANGTCCA ) . The sequences were extended to full length using 5′ and 3′ RACE [22] . For 5′ RACE , an oligonucleotide corresponding to the spliced leader , SL1 ( GGTTTAATTACCCAAGTTTGAG ) was used in conjunction with specific internal primers ( Asu-unc38 TAAAGCACGCTGACACCACC; Asu-unc-29 ATYTCRTTYTCRTTRTANGTCCA ) . For 3′ RACE an ‘anchor’ primer sequence incorporated into the oligo ( dT ) primer used for reverse transcription ( GACCACGCGTATCGATGTCGAC ) was together with an internal primer specific for Asu-unc-29 ( GCACTAAGAGCTATTGACGCG ) ) . Full length cDNAs were amplified using specific primers ( Asu-unc-38 CTGCATTTATTAAGATGTTTGG and ATGTAAATTATTGAGTGACTGG; Asu-unc-29 CACTGAGGGCAGTTATGCACC and CAGTGTGGGCGAGATATTAGATC ) . Full length cDNAs were cloned into pGEM-T ( Promega ) and transformed into XL-1 blue competent cells ( Stratagene ) . Antisera were raised , in rabbit and goat respectively , against subunit-specific N-terminal peptides from Asu-UNC-38 and Asu-UNC-29 ( Harlan , UK and Sigma-Genosys , USA ) then antibodies were affinity purified as described [35] . Adult A . suum collected from a local abattoir were kindly supplied by Professor Aaron Maule and Emma Kidd from Queen's University , Belfast . Muscle cells were prepared by collagenase digestion [36] fixed and antibody labelled [37] . Muscle cell preparations were then mounted on slides and viewed using a Zeiss 510 confocal laser scanning microscope . A . suum unc-38 and unc-29 cDNAs were sub-cloned into the expression vector pT7TS ( obtained from P . Krieg , University of Texas , Addgene plasmid 17091 ) , containing 5′ and 3′ untranslated regions from Xenopus β-globin . Plasmids were linearised , and then used as template for cRNA synthesis using the T7 mMessage mMachine kit ( Ambion , UK ) . Oocytes were obtained from EcoCyte Biosciences , Germany . Oocytes were injected with nicotinic receptor cRNAs in RNAse-free water in a total volume of 50 nl , which was the same for all experiments . When a single cRNA species was injected , 25 ng was used; when both Asu-unc-29 and Asu-unc-38 cRNAs were injected , a total of 50 ng was used . Oocyte injection was carried out using a Drummond ‘Nanoject’ microinjector and electrophysiological experiments were carried out [38] using a GeneClamp 500 amplifier and Digidata 1322A ( Axon Instruments ) with oocytes being voltage-clamped at −60 mV . Pharmacological compounds were obtained from Sigma . Data was recorded and responses measured using pClamp software . GraphPad Prism software was used to analyse the data and fit sigmoid dose-response curves to the equation I/Imax = ( Imax−Imin/[1+10 ( logEC50−[ag]*nH ) ] ) ×Imax . For pyrantel and oxantel , the equation was fitted to the data obtained for the rising phase of the response only . The Accession numbers for proteins mentioned in the text are: C . elegans: UNC-38 – Q23022 , UNC-29 –P48181 , UNC-63 – Q9N587 , ACR-12 – Q9GQU9 , ACR-8 – Q23355 , ACR-6 – Q9N4M3 , LEV-1 – P48181 , ACR-3 – Q93149 , ACR-2 – P48182 , ACR-16 – P48180 . B . malayi: BM_UNC-29 – Bm1_35890 .
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Ascarid nematodes are major pathogens of humans and livestock . The major method of control is by the use of anthelmintic drugs , many of which target the nervous system . Drugs such as levamisole , pyrantel and oxantel target the nicotinic acetylcholine receptors present on muscle . Nematodes have several such receptors , and until now these have been best understood in the model species Caenorhabditis elegans . We have started to characterise the nicotinic receptors of Ascaris suum , and find that the genetics and pharmacology of the A . suum receptors differ from C . elegans . In both species , nicotine and levamisole preferentially activate different forms of the nicotinic receptor , the N- and L-type , respectively . In C . elegans , the L-type receptor is made up of five subunits , whereas the N-type is a homomer of a sixth subunit . We can recapitulate many of the properties of the A . suum N- and L-type receptors , including their sensitivity to two other important anthelmintics , pyrantel and oxantel , by expressing just two subunits at varying ratios . This has implications for the use of drug combinations and for cross-resistance between nicotinic anthelmintics . It may start to give an explanation for the varying effectiveness of nicotinic drugs against different parasites .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"infectious",
"diseases/helminth",
"infections",
"infectious",
"diseases/neglected",
"tropical",
"diseases"
] |
2009
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The Nicotinic Acetylcholine Receptors of the Parasitic Nematode Ascaris suum: Formation of Two Distinct Drug Targets by Varying the Relative Expression Levels of Two Subunits
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Recombination and mutagenesis are elevated by active transcription . The correlation between transcription and genome instability is largely explained by the topological and structural changes in DNA and the associated physical obstacles generated by the transcription machinery . However , such explanation does not directly account for the unique types of mutations originating from the non-canonical residues , uracil or ribonucleotide , which are also elevated at highly transcribed regions . Based on the previous findings that abasic ( AP ) lesions derived from the uracil residues incorporated into DNA in place of thymine constitute a major component of the transcription-associated mutations in yeast , we formed the hypothesis that DNA synthesis ensuing from the repair of the transcription-induced DNA damage provide the opportunity for uracil-incorporation . In support of this hypothesis , we show here the positive correlation between the level of transcription and the density of uracil residues in the yeast genome indirectly through the mutations generated by the glycosylase that excise undamaged cytosine as well as uracil . The higher uracil-density at actively transcribed regions is confirmed by the long-amplicon PCR analysis . We also show that the uracil-associated mutations at a highly transcribed region are elevated by the induced DNA damage and reduced by the overexpression of a dUTP-catalyzing enzyme Dut1 in G1- or G2-phases of the cell cycle . Overall , our results show that the DNA composition can be modified to include higher uracil-content through the non-replicative , repair-associated DNA synthesis .
Transcription , a fundamental cellular process , can incongruously pose a serious threat to genome stability . Highly transcribed genomic regions have been reported to be hotspots for mutagenesis and recombination , phenomena referred to as transcription-associated mutagenesis ( TAM ) and transcription-associated recombination ( TAR ) , respectively [1–4] . Several different ways by which transcription promotes genomic instability have been described . First , the single strand DNA generated by DNA strand-separation during transcription is much more chemically labile than double-stranded DNA , leading to the mutations resulting from the spontaneous base modifications such as deamination [5–8] . Second , transcription necessitates a change in DNA topology and the ensuing accumulation of both negative and positive supercoils promotes the formation of non-canonical secondary structures such as R-loops , the stable hybrids of transcribed DNA and nascent RNA or G-quadruplex DNAs ( G4 DNA ) , the four-stranded DNA configuration held together by Hoogsteen bonds among guanine bases [9 , 10] . These structures leave the non-transcribed DNA in the susceptible single-stranded state and frequently lead to stalling and eventual collapse of the replication fork , which must be restarted/ repaired via homologous recombination [11–13] . Finally , because replication and transcription occur on the same template , collisions between the replication fork and transcription machinery are possible [14 , 15] . These collisions induce helical stress and can trigger replication fork reversal giving rise to non-canonical DNA structures that can be processed into double stranded breaks [16 , 17] . Recently , a novel mechanism of transcription-associated mutagenesis involving the non-canonical DNA nucleotide , uracil , was identified through genetic studies in yeast [18 , 19] . Due to its close structural resemblance to thymine , uracil can be directly incorporated into DNA by DNA polymerases that cannot distinguish between the two bases , leading to U:A base mispairs [20] . Subsequent uracil removal by an uracil-DNA glycosylase ( Ung1 in yeast ) creates potentially toxic apurinic/apyrimidinic ( AP ) sites [21] . AP sites are the most prevalent endogenous DNA lesions produced from either spontaneous or DNA N-glycosylases-catalyzed hydrolysis of the base-glycosidic linkage and can act as a potent block to the transcription machinery and the replicative DNA polymerases . [22] . Blocked DNA synthesis can be rescued by the recruitment of translesion synthesis ( TLS ) DNA polymerases Rev1 and Polζ that together bypass the lesion by typically incorporating a C nucleotide across from the AP site in yeast and metazoans [23–26] . In addition to their misincorporation into DNA by DNA polymerases during replication or repair , uracil in DNA can result from either spontaneous or enzymatic deamination of cytosines to create U:G mispairs , which cause G:C to A:T transitions . Although uracil in DNA leads to deleterious mutations if not properly repaired , at the antibody-encoding immunoglobulin gene , it is an essential intermediate in antibody affinity maturation during the adaptive diversification processes [27] . The detrimental mutagenic outcome of uracil in DNA is prevented by Base Excision Repair ( BER ) pathway , which initiates repair via the AP endonuclease-catalyzed cleavage of the sugar-phosphate backbone at the 5’ side of the AP lesion . In yeast , the major AP endonucleases are Apn1 , which carries out 95% of the repair , and Apn2 [28 , 29] . N-glycosylases such as Ntg1 and Ntg2 with the associated AP lyase activity can also create breaks at the DNA backbone adjacent to the AP site in certain instances such as when the AP endonucleases activity is diminished or overwhelmed [19 , 30] . Subsequent steps in BER involve the removal of the blocked DNA ends , gap filling by a DNA polymerase and ligation of the remaining nick by a DNA ligase . Although Nucleotide Excision Repair ( NER ) usually removes bulky , helix-distorting lesions such as UV-induced DNA damage , the transcription-coupled repair ( TCR ) sub-pathway of NER has been implicated in the repair of AP sites when BER is overburdened or disrupted [19] . TCR specifically repairs the RNA polymerase-stalling lesions in the transcribed strand of active genes encouraging a rapid removal of damage and preventing the accumulation of mutations on the transcribed strand . In yeast , Rad14 is absolutely required for TCR repair of AP sites , while Rad26 and Def1 contribute partially [19 , 31] . Deoxyuridine triphosphatase ( dUTPase ) , a ubiquitous enzyme that is essential for viability in both prokaryotic and eukaryotic organisms , catalyzes the conversion of dUTP to dUMP and pyrophosphate ( Ppi ) , thereby reducing the pool of free dUTP and preventing the incorporation of uracil into DNA [32] . Following the conversion of dUTP to dUMP by the dUTPase , Thymidylate Synthase ( TS ) , using tetrahydrofolate as a methyl donor , converts dUMP to dTMP , an intermediate that is required for dTTP synthesis . dUTPase serves two essential functions; maintaining a low intracellular [dUTP]/[dTTP] ratio , thus minimizing incorporation of uracil into DNA , and providing an important intermediate , dUMP , for the de novo synthesis of thymidylate . In addition to pyrophosphorlysis of dUTP by dUTPase , the pools of dUMP required for TS reaction is partly supplied by the deamination of dCMP by deoxycytidylate deaminase , Dcd1 in yeast [33] . Recent studies in Saccharomyces cerevisiae demonstrated an increase in uracil-derived mutations following the activation of transcription at a defined reporter gene [18] . These mutations were highly elevated by the disruption of BER and eliminated by the deletion of UNG1 gene or the overexpression of yeast dUTPase , Dut1 , suggesting that the uracil-dependent mutations result from the AP sites generated by excision of uracil incorporated into DNA . Repressing the transcription of the reporter gene lowered the uracil-associated mutations , suggesting a link between the extent of uracil incorporation into DNA and the process of transcription . However , a clear demonstration of higher uracil content at highly transcribed genomic loci is still lacking . In addition , how the nucleotide composition is affected by transcription remains to be deciphered . In the current study , we used the mutagenesis reporter with a regulatable promoter to further investigate the link between active transcription and uracil DNA content . Our results show that there are significantly more uracil residues present at the highly transcribed genomic locus and that the DNA glycosylase activity is slightly enhanced when transcription is elevated . Furthermore , we show that the DNA repair synthesis , induced by DNA damaging agents , leads to an increase in uracil residues present in DNA and that the overexpression of Dut1 in G1- and G2-phases of the cell cycle leads to a significant reduction in the uracil-dependent mutations at the highly transcribed site . Overall , our data strongly support a model where various transcription-associated damage induce unscheduled DNA synthesis , particularly in G1 and G2 , subsequently leading to the elevated uracil residues at highly transcribed genomic loci .
In order to investigate whether the main cause underlying the transcription-associated elevation in uracil-dependent mutations is the higher uracil density or the enhanced uracil excision , we used a modified human uracil DNA glycosylase ( UDG ) . The mutant enzyme , hereon referred to as CDG , was generated by introducing Asn204 to Asp mutation in the substrate binding pocket of the UDG [34 , 35] . CDG is able to excise unmodified cytosine residues from oligonucleotide substrates in vitro . Expression of CDG in the TLS-proficient yeast cells was previously shown to induce the accumulation of A:T > C:G and G:C > C:G transversions , resulting from the excision of uracils and cytosines , respectively . We expressed CDG to generate AP sites through excision of cytosines and uracils in yeast cells containing the mutation reporter pTET-lys2-TAG . This reporter contains the in-frame TAG stop codon inserted into the LYS2 ORF and mutations at this stop codon is required for the reversion to Lys+ phenotype . The pTET-lys2-TAG reporter can be transcribed at a high or low level by the absence or presence of doxycycline in the media , respectively . Like the pTET-lys2TAA reporter used in previous studies of uracil-associated mutations under high transcription conditions , the pTET-lys2-TAG reporter is helpful in identifying uracil-associated mutations and in addition allow the detection of mutations arising from the excision of cytosines . As illustrated in Fig 2A , a A>C or T>G mutation is expected when an AP site is generated from the excision of uracil that is in place of thymine . And a G>C mutation is expected when an AP site is generated from the excision of cytosine ( Fig 2B ) . In absence of CDG expression , the deamination of cytosine would result in the generation of uracil leading to the G>C mutations after excision by Ung1 . However , in apn1Δ background , the rate of G>C mutations is >40-fold lower than the rate of uracil-associated A>C and T>G mutations ( S1 Table ) , indicating that the cytosine deamination in the yeast strains used in the study is negligible . Mutations resulting from the methylation of cytosine is also unlikely considering the previous report where methyl-cytosine was not detected in yeast genomic DNA [36] . We expressed CDG in the apn1Δ ung1Δ or apn1Δ rad14Δ ung1Δ strain backgrounds , where the endogenous UNG1 is deleted so that all uracil- or cytosine-associated mutations are resulting from the activity of ectopically expressed CDG . The rates of uracil-dependent , A>C and T>G mutations as well as cytosine-dependent , G>C mutations were determined under the high ( no DOX ) and low ( +DOX ) transcription conditions to determine the effect of transcription on these mutations . Since the number of cytosine residues in DNA should not be affected by the level of transcription , the rate of mutations caused by the AP sites generated by the cytosine excision ( G>C mutations ) should not change whether under the high or low transcription conditions , unless the enzymatic efficiency of cytosine excision by CDG is affected by activated transcription . When CDG was expressed in the apn1Δ ung1Δ strain , the overall mutation rate was ~7-fold higher under the high transcription conditions than under the low transcription conditions ( Fig 2C and S2 Table ) . The mutation spectra showed a ~10-fold increase in the rate of uracil-dependent , A>C and T>G mutations but only a ~1 . 7 -fold increase in the rate of the cytosine-dependent , G>C mutations ( Fig 2D and 2E ) . In a BER/NER- deficient , apn1Δ rad14Δ ung1Δ strain , the CDG expression under high transcription conditions led to ~10- and ~ 2 . 5-fold increases in the rates of uracil- and cytosine-dependent mutations , respectively , compared to the CDG expression under low transcription conditions . Overall , the active transcription resulted in the significantly greater increase in the uracil-associated mutations compared to the cytosine-associated mutations . Altogether , these data suggest that the elevated glycosylase efficiency is only a minor factor contributing to the transcription-dependent elevation in the uracil-associated mutations . To further probe the effect of transcription on the activity of glycosylases , we repeated the CDG expression in apn1Δ top1Δ ung1Δ and apn1Δ rad14Δ top1Δ ung1Δ strains and determined the mutation rates under the high and low transcription conditions . Topoisomerase 1 ( Top1 ) functions to relieve topological stress , including the transcription-associated supercoils , by creating transient strand breaks and then rejoining DNA strands [37] . Deletion of TOP1 leads to an accumulation of negative helical stress , particularly in the highly transcribed areas . Upon CDG expression in the cells of apn1Δ top1Δ ung1Δ background , the rate of overall mutation at the pTET-lys2-TAG reporter was 7 . 9-fold higher under high transcription conditions than under low transcription conditions ( Fig 3A ) . This is comparable to the 7 . 1-fold difference between the rates of CDG-induced mutations under high and low transcription conditions in apn1Δ ung1Δ background . Additionally , we sequenced the pTET-lys2-TAG allele in the Lys+ revertants to identify the specific nucleotide substitutions . In apn1Δ top1Δ ung1Δ background , CDG-expression under the high transcription conditions resulted in 16 . 3- and 27 . 6-fold increases in A>C and T>G mutations , respectively , compared to the CDG-induced mutations occurring under low transcription conditions ( Fig 3B and 3C and S2 Table ) . These are substantially higher than the high-transcription associated elevation in A>C and T>G mutations observed in apn1Δ ung1Δ background , which were 4 . 2- and 11 . 2-fold increases , respectively ( S2 Table ) . Additionally , when comparing the CDG-induced mutations , the transcription-dependent fold elevation ( high/low ) of A>C , T>G , and C>G mutations were significantly higher in apn1Δ rad14Δ top1Δ ung1Δ than in apn1Δ rad14Δ ung1Δ . As illustrated in Fig 2A , the uracil-derived A>C and T>G mutations result from the excision of uracil present on the transcribed ( bottom ) strand and the non-transcribed ( top ) strand , respectively . Since TOP1 deletion resulted in the higher transcription-dependent elevation of both A>C and T>G mutations , the overall DNA topology changes in the Top1-deficient cells appear to increase the access of glycosylase to the uracil residues regardless of whether they are located on the transcribed or non-transcribed strand . To determine whether uracil residues in the single-stranded DNA are more easily accessed by DNA glycosylases , we determined the rates of uracil- and cytosine-dependent mutations in the apn1Δ rnh1Δ rnh201Δ ung1Δ strain following CDG expression . RNH1 and RNH201 encode the RNaseH enzymes that degrade RNA hybridized to DNA [38] . During transcription , RNA-DNA hybrids form when the nascent RNA anneals to the template , transcribed DNA strand leaving the non-transcribed DNA strand single-stranded . In the absence of RNaseH enzymes , the transcription-associated RNA-DNA hybrids persist and accumulate to form “R-loops” [13] . In apn1Δ rnh1Δ rnh201Δ ung1Δ strain backgrounds , the rate of overall mutations induced by the CDG-expression at the pTET-lys2-TAG reporter under the high transcription conditions was not significantly greater than the rate in apn1Δ ung1Δ ( Fig 3D ) . And the transcription-dependent increase in the rates of A>C or C>G mutations was not significantly different in these two backgrounds ( Fig 3E and 3F and S2 Table ) . However , the transcription-dependent fold-increase ( high/low ) in the rate of T>G mutation was 39 . 1 in apn1Δ rnh1Δ rnh201Δ ung1Δ compared to 11 . 2 in apn1Δ ung1Δ ( S2 Table ) . The greater effect of the disruption of RNase H enzymes on the T>G mutations relative to the nominal effect it had on the A>C or C>G mutations indicate that CDG has a greater access to the target ( U or C ) on the single-stranded non-transcribed strand of DNA . To determine whether there is a correlation between the level of transcription and the uracil content in DNA , we sought to measure the density of uracil in DNA at specific genomic sites using the long-amplicon quantitative PCR approach . This method , which measures the reduction in the amplification efficiency resulting from the polymerase-blocking damage to the template DNA , was previously used to quantify the damage in the mitochondrial DNA of various vertebrate species [39] and the uracil residues at the mouse immunoglobulin loci in B lymphocytes [40] . Genomic DNA isolated from yeast cells lacking the endogenous Ung1 was treated in vitro with recombinant UDG to create AP sites specifically at the uracil residues . The resulting AP site was then converted into a single-strand break by the Endo VIII-treatment . The presence of uracil is measured as the relative loss of qPCR signal or the relative reduction in the amplification efficiency when the UDG/Endo VIII-treated DNA is amplified compared to when an untreated DNA sample is amplified . First , to validate our strategy , we measured uracil content in ung1Δ yeast cells treated with 0 , 1 , 5 and 10 μM 5-fluorouracil ( 5-FU ) , an inhibitor of TS that leads to an increase in the cellular dUTP pool and the accumulation of uracil residues in DNA . The DNA samples purified before or after UDG/Endo VIII-treatment were used as the template in qPCR reactions with LYS2 primers targeting ~100 bp , 3 kb , and 4 kb regions ( Fig 4A and S1 Table ) . The Ct values from the “LYS2 100 bp” primers were used as normalizing controls , under the assumption that it is highly unlikely there is a significant number of the polymerase-blocking lesions within the approximately 100 bp region targeted by these primers . For the DNA sample from cells treated with no or 1μM 5-FU , the amplification of the UDG/EndoVIII-treated DNA were about 75% of the untreated DNA . For the samples treated with 5 and 10 μM 5-FU , there was a dose-dependent reduction in the amplification efficiency compared to that of untreated samples ( Fig 4A ) . As expected , the amplification of the 4 kb amplicon ( “LYS2 4kb” primer set ) had a significantly reduced amplification in comparison to the 3 kb amplicon ( “LYS2 3kb” primer set ) since the larger amplicon size increases the likelihood of a lesion being encountered during PCR . We used a Poisson equation to further estimate the average uracil frequency at the LYS2 locus from the long-amplicon amplification efficiency and observed that the uracil frequency is significantly elevated when treated with higher concentrations of 5-FU ( Fig 4B ) . The frequency of uracil in DNA was about 1 per 10 kb in cells treated with 0 or 1 μM 5-FU and was elevated to >2 per 10 kb with the treatment of 5 or 10 μM 5-FU . Overall , these results show that the long-amplicon qPCR strategy can be used to quantitatively measure the uracil frequency in yeast genomic DNA . We extended the long amplicon qPCR method to determine the endogenous uracil levels at several genomic loci . In order to first determine the level of transcription of the genes where we measure the uracil frequency , we isolated RNA from ung1Δ and ung1Δ dcd1Δ strains grown in the presence or absence of doxycycline . DCD1 is a gene that encodes a deoxycytidylate deaminase ( Dcd1 ) , which converts dCMP to dUMP , a substrate for the dTTP production [32] . The deletion of DCD1 increases the [dUTP]/[dTTP] ratio , leading to the increased incorporation of uracil into DNA . As expected from the elevated level of uracil-incorporation into DNA , the deletion of DCD1 in apn1Δ strain led to a two-fold increase in the rate of mutation at the pTET-lys2-TAA reporter under high transcription conditions ( S1A Fig ) . Following mRNA extraction from ung1Δ and ung1Δ dcd1Δ strains , we performed RT-qPCR to determine the expression levels of pTET-lys2-TAA , CAN1 and TDH3 in the presence and absence of doxycycline . While the expression levels of CAN1 and TDH3 genes , which are not regulated by the pTET promoter , was not affected by the presence of doxycycline , the level of pTET-lys2-TAA transcripts from no DOX samples was ~170-fold higher than that from +DOX samples ( S1B Fig ) . For the long-amplicon qPCR analysis , DNA samples were prepared from WT , ung1Δ , and ung1Δ dcd1Δ strains grown in the presence and absence of doxycycline . Under the conditions where the transcription of pTET-lys2-TAA is repressed ( + DOX ) , the density of uracil in DNA as inferred from the amplification carried out with the “LYS2 3kb” primers were 0 . 08 , 0 . 93 , and 1 . 1 per 10 kb in WT , ung1Δ , and ung1Δ dcd1Δ , respectively ( Fig 4C ) . Under high transcription conditions ( no DOX ) , the uracil-density was significantly higher in all three strain backgrounds with 0 . 45 , 1 . 5 , and 2 . 1 per 10 kb in WT , ung1Δ , and ung1Δ dcd1Δ strains , respectively . In WT cells , the activity of endogenous Ung1 accounts for the relatively low level of uracil in DNA in WT as determined by the long-amplicon PCR approach . When “LYS2 4kb” primers were used for the analyses on the same DNA samples , the uracil-densities calculated were 0 . 53 , 1 . 3 , and 2 . 1 per 10 kb in WT , ung1Δ , and ung1Δ dcd1Δ , respectively , under high transcription conditions and statistically the same as those calculated using “LYS2 3kb” primers ( Fig 4D ) . For the CAN1 or TDH3 loci , a set of primers targeting a ~3 kb region encompassing each gene was used in the long amplicon PCR with another set of primers targeting a ~100 bp region within each gene as the control PCR reactions ( S1C Fig ) . In a manner similar to the pTET-lys2-TAA , the density of the uracil in DNA were highest in the ung1Δ dcd1Δ background and lowest in WT background ( Fig 4E and 4F ) . Unlike the pTET-lys2-TAA , the addition of doxycycline did not affect the uracil density at CAN1 or TDH3 loci . In ung1Δ background , the uracil density was about 3-fold lower at CAN1 than TDH3 . However , in ung1Δ dcd1Δ background , the uracil density derived by the long-amplicon PCR approach at TDH3 and CAN1 were not significantly different . In both ung1Δ and ung1Δ dcd1Δ backgrounds , TDH3 is transcribed at ~100-fold higher rate than CAN1 ( S1B Fig ) . We postulate that spontaneous DNA damage associated with the active transcription lead to cycles of unscheduled DNA synthesis ( UDS ) leading to the increase in uracil-associated mutations . In order to test this hypothesis , we induced UDS using three different types of DNA damaging agents and calculated the frequency of uracil-dependent mutations . First , we treated the cells with 5-fluorouracil ( 5-FU ) , which imbalances the [dUTP]/[dTTP] ratio and thus enhance the incorporation of uracil residues in DNA . The frequency of mutations at the reporter was determined in WT , ung1Δ , apn1Δ , and apn1Δ ung1Δ background cells treated with 10μM 5-FU . Compared to the DMSO control , the 5-FU treatment led to a remarkable elevation in the rate of pTET-lys2-TAA mutations in the BER-deficient apn1Δ strain but not in the BER-proficient WT or ung1Δ strain . As would be expected of mutations arising from the uracil-derived AP sites , the rate of mutations was far reduced in the uracil-excision incapacitated apn1Δ ung1Δ compared to that in apn1Δ ( Fig 5A ) . When the Lys+ mutants were sequenced , the majority ( 67/72 ) in apn1Δ strain were A>C or T>G , as expected of uracil-associated mutations ( Fig 5A and 5B ) . In order to determine whether the repair induced by a broad spectrum of DNA damage can increase the uracil-incorporation into DNA and the consequent uracil-derived mutations , we measured the frequency of the pTET-lys2-TAA mutations in yeast cells treated with two DNA damaging agents without previously reported effect on the dUTP/dTTP metabolic pathway -camptothecin ( CPT ) and 4-nitroquinoline 1-oxide ( 4NQO ) . CPT is a Top1 inhibitor that traps Top1-DNA cleavage complex and is known to elevate recombination and copy number variations in the eukaryotic genomes [41 , 42] . Following CPT-treatment , the overall mutations were elevated by ~3 folds in WT , ung1Δ , apn1Δ , and apn1Δ ung1Δ backgrounds ( Fig 5C ) . The mutation spectra and the frequency of the specific type of mutations were determined by sequencing the pTET-lys2-TAA reporter in Lys+ revertants . In apn1Δ cells , the increase in the rate of A>C and T>G mutations due to CPT-treatment was statistically significant with the 95% confidence intervals not overlapping ( Fig 5D ) . In apn1Δ ung1Δ cells , the frequency of A>C and T>G mutations in the CPT-treated samples was slightly higher than the untreated control but the difference was not statistically significant . Also , A>C and T>G mutations in the CPT-treated apn1Δ ung1Δ were significantly reduced compared to those in apn1Δ , indicating that the mutations resulting from Ung1-mediated excision of uracil in DNA do occur at a significant level upon CPT-treatment . 4NQO is a mutagenic heterocyclic chemical that forms covalent bulky adducts to dG or dA , which are predominantly repaired by NER [43] . Following treatments with 4NQO , the overall mutation frequency at the pTET-lys2-TAA reporter was elevated by ~15- to 50-fold in WT , ung1Δ , apn1Δ , and apn1Δ ung1Δ backgrounds ( Fig 5E ) . The mutation spectra revealed that a majority of the mutations elevated by the 4NQO-treatment were A:T > T:A transversions ( S2 Fig ) , the type of mutations that had previously been associated with 4NQO [44] . The uracil-dependent mutations ( A>C and T>G ) were significantly elevated only in the apn1Δ cells treated with 4NQO ( Fig 5F ) . Similar to the observation in the CPT-treated cells , the frequency of 4NQO-induced A>C and T>G mutations in the apn1Δ ung1Δ background was significantly lower than that in apn1Δ , indicating that uracil-incorporation into DNA and the resulting mutations also occur at a significant level following the 4NQO-treatment . In order to quantify the uracil residues present in the genomic DNA , we used an AP-reactive alkoxyamine compound AA3 [45] . This compound contains the alkyne group , through which a variety of compounds can be attached via click chemistry . Our approach to detect uracil-residues in DNA was first to label the uracil-derived AP sites with AA3 and then to attach the fluorescent dye cyanine 5 ( Cy5 ) to AA3-AP conjugates ( S3 Fig ) . To demonstrate its efficacy , we applied the approach to the ung1Δ cells treated with various concentration of 5-FU and observed a dose-dependent increase in the Cy5 signal when yeast cells were treated with 10 , 50 , and 100 μM 5-FU ( Fig 6A ) . Genomic DNA isolated from Hela cells and the AID-expressing Daudi cells was used as negative and positive controls , respectively . Daudi is a B-cell lymphoma cell line with highly elevated level of uracil in DNA resulting from the overexpression of the APOBEC family of cytosine deaminases [46 , 47] . While there was no significant difference between the level of uracil in yeast cells without 5-FU treatment and that in Hela cells , the level of uracil in DNA in yeast cells treated with 100 μM 5-FU increased to the level comparable to that in Daudi cells ( Fig 6A ) . Using the same AA3-Cy5 labeling approach , we measured the level of uracil in DNA in yeast cells treated with 1 , 5 , 10 , and 20 μg/mL 4NQO . The levels of uracil detected in cells treated with 10 or 20 μg/mL 4NQO , but not with 1 or 5 μg/mL 4NQO , were significantly elevated compared to the untreated sample ( Fig 6B ) . To further show that the uracil-incorporation into DNA is a significant component of the repair synthesis associated with 4NQO treatment , we tested whether uracil in DNA is relevant to the cytotoxicity of 4NQO . The main cytotoxic lesion of 5-FU treatment is the AP sites derived from the uracil incorporated into the genomic DNA . When treated with 5-FU , yeast cells in the BER deficient apn1Δ backgrounds are highly sensitive ( Fig 6C ) . But the 5-FU sensitivity is very significantly reduced in apn1Δ ung1Δ background , where uracil in DNA cannot be removed to create the toxic AP sites . If 4NQO treatment elevates the level of uracil in DNA , apn1Δ ung1Δ strains would have a reduced level of sensitivity to 4NQO in comparison to apn1Δ strains . We calculated the number of surviving colony-forming cells after culturing them in the liquid media with or without 0 . 2 , 0 . 5 , or 1 μg/mL 4NQO and observed a slight survival advantage in apn1Δ ung1Δ compared to apn1Δ , although only the 2 μg/mL 4NQO concentration elicited a statistically significant increase ( Fig 6D ) . Together , these results support our hypothesis that uracil incorporation into DNA occurs at a significant level when DNA repair synthesis is induced . We hypothesized that the transcription-associated increase in the density of uracil in DNA as determined by the long-amplicon qPCR or the CDG expression experiments above is due to the incorporation of uracil into DNA during unscheduled DNA synthesis ( UDS ) that can occur outside of the genome duplication in S phase . It has been shown in mammalian cells and plants that DUT1 , a gene that encodes for dUTPase , is cell-cycle regulated with its highest expression in S-phase [48 , 49] . In yeast , a large-scale high through-put analysis previously has shown that the dUTPase-encoding DUT1 gene begins to be upregulated in late G1 phase ensuring that dUTP levels are kept low during replication [50] . Conversely , DNA synthesis occurring outside of S phase ( i . e . G1 and G2 ) will be subject to the dNTP pool with the relatively higher dUTP levels . To confirm the cell-cycle regulated DUT1 expression in yeast , we arrested cells in G1 using the mating pheromone α-factor and collected cells every 15 minutes after release for the RNA isolation and qRT-PCR . The expression level of the histone H2-encoding HTA2 gene , which was previously shown to be upregulated during S phase , was determined as a control [51] . The expression levels of both DUT1 and HTA2 genes were highest at 45 mins after the release from α-factor and declined to the lowest point at 75 mins after the release indicating that the DUT1 expression is cell-cycle regulated in a manner similar to the HTA2 gene ( S4A Fig ) . We previously reported that the plasmid-mediated overexpression of DUT1 from the galactose-inducible pGAL promoter can greatly reduce the uracil-associated mutations at the pTET-lys2-TAA reporter [18] , indicating that the cellular [dUTP] is a critical determinant in the transcription-associated uracil-dependent mutations . When induced by the addition of galactose to the media , the pGAL-regulated genes are highly expressed regardless of the cell cycle . To test whether the uracil-incorporation into DNA during G1 and G2 phases is a significant contributor to the transcription-associated uracil-dependent mutations , we modulated [dUTP] in G1 , S and G2 phases by the cell-cycle specific overexpression of DUT1 gene and measured the effect on the mutation rate at the pTET-lys2-TAA reporter . For the cell-cycle specific expression of DUT1 in G1 , S , or G2 phase , we replaced the pGAL with the promoters of CLN2 , HHO1 , or CLB2 genes , respectively [14 , 52] . In order to reduce the protein half-life and thereby ensure cell-cycle specific presence of the overexpressed Dut1 protein , we added a protein destabilization domain ( PEST ) to the plasmid constructs [53] . We expressed DUT1-PEST from these plasmid constructs in yeast , isolated mRNA from asynchronous cells and performed qRT-PCR to determine the expression level . DUT1 was expressed 38- , 64- , and 25-fold higher than the endogenous level from the G1 , S , and G2 constructs , respectively . For all three constructs , the level of the overexpressed DUT1 mRNA was substantially lower than the DUT1 expression from pGAL-construct , which was 412-fold higher than the endogenous level ( S4B Fig ) . In order to confirm that DUT1 is expressed in the cell cycle-specific manner from these promoters , we performed qRT-PCR with RNA samples isolated every 20 minutes after the release of cells arrested at G1 with α-factor . The DUT1 mRNA expression was highest at 100 , 40 , and ~60–100 mins after release from α-factor ( S4C , S4D , and S4E Fig ) . The S-phase time point under this specific condition was determined to be 60 mins after the release from the α-factor by analyzing the mRNA level of HTA2 gene . We transformed the BER-deficient , apn1Δ ntg1Δ ntg2Δ cells with the plasmids containing pCLN2 ( G1- ) , pHHO1 ( S- ) , or pCLB2 ( G2 ) -constructs and calculated the mutation rates at the pTET-lys2-TAA reporter . We first carried out the fluctuation analysis for the determination of mutation rates in media supplemented with galactose and raffinose under high transcription conditions in order to compare the effect of Dut1 expression from pCLN2 , pHHO1 , or pCLB2 to its expression from the previously studied pGAL-construct . While the pGAL-construct resulted in ~10-fold decrease in the rate of mutations compared to the vector-only control under these conditions , the G1-specific , pCLN2-construct led to a ~ 2-fold reduction in mutation rates in cells ( Fig 7A ) . Although the G2-specific , pCLB2-construct led to a <2-fold reduction , the mutation rate in the cells with this construct was significantly different from that in the cells with vector alone . For the cells with S-specific pHHO1-construct , there was no significant reduction in the mutation rate compared to the vector control . When the fluctuation analysis was repeated in the media supplemented with glycerol and ethanol , the G1- , S- , and G2-specific constructs all resulted in <2-fold , but statistically significant , reductions in the rates of mutation at the pTET-lys2-TAA reporter compared to the vector control ( Fig 7B ) . When the transcription of the pTET-lys2-TAA was repressed by adding doxycycline , the rates of mutation was unchanged with the Dut1-overexpression from the G1- , S- , or G2-specific promoters or from the pGAL promoter ( Fig 7C and 7D ) . These results suggest that the shift in the free dUTP pool affects the uracil-composition and the uracil-associated mutations more substantially at highly transcribed genes .
High levels of transcription have previously been implicated as a major source of genomic instability in various organisms ( reviewed in [4 , 54 , 55] . In yeast , when a reporter construct with the tetracycline-regulatable promoter ( pTET ) was used to determine the rate of mutations at several different levels of transcription , a linear and proportional relationship between the level of transcription and the rate of mutation was observed [56] . Subsequent studies indicated that a majority of these mutations were derived from unrepaired abasic sites [18] . When BER is disabled , as in apn1Δ or apn1Δ ntg1Δ ntg2Δ strains , there was a unique elevation in specific types of mutations . Namely , when the mutations at the pTET-lys2-TAA allele was studied , TAA to GAA , TCA , or TAC mutations were elevated by ~200- and 500-folds in apn1Δ or apn1Δ ntg1Δ ntg2Δ strains , respectively [19] . The highly elevated rates of these T>G and A>C mutations were dramatically reduced by the disruption of the uracil DNA glycosylase Ung1 or by the overexpression of the dUTPase Dut1 , indicating that these types of mutation are originating from uracil in DNA . There are two distinct ways by which the non-canonical uracil residues appear in DNA; by the deamination of cytosine residues present in DNA or by the incorporation into DNA by DNA polymerase utilizing dUTP in place of dTTP . The location of mutations at T:A or A:T pairs suggests the latter route of uracil appearance in DNA . Further genetic studies showed that AP sites generated by the excision of uracil by Ung1 is bypassed by the TLS polymerases Rev1 and Polζ to bring about the T>G and A>C mutations [57] . The most remarkable finding about these uracil-derived T>G and A>C mutations was that they are almost completely suppressed when the transcription of the pTET-lys2-TAA mutation reporter is repressed by the addition of doxycycline . This transcription-dependent elevation of mutations originating from uracil residues in DNA led to the hypothesis that the chemical composition of the DNA can be changed to include a higher number of uracil residues when actively transcribed . We tested the hypothesis of the transcription-dependent elevation of uracil residues in DNA by directly quantifying uracil residues at a defined genomic locus under high or low transcription conditions using the long-amplicon qPCR method . As we demonstrated with cells treated with various concentrations of 5-FU , the cellular balance between dUTP and dTTP concentrations is the key determinant of the density of uracil in DNA ( Fig 4B ) . Therefore , we carried out the measurement of the endogenously present uracil residues in DNA in WT and ung1Δ strain backgrounds rather than in the apn1Δ or apn1Δ rad14Δ backgrounds where the severe defect in BER and/or NER pathways could possibly disrupt the regulation of dNTP pool . In ung1Δ strains , where uracil residues , once incorporated into the DNA , cannot be excised out , there was a statistically significant 2-fold difference between the densities of uracil detected at the pTET-lys2-TAA under high and low transcription conditions ( Fig 4C and 4D ) . The disruption of Dcd1 , a dCMP deaminase , moderately reduces the dTTP production , thereby resulting in imbalance in [dUTP] to [dTTP] ratio . Such a shift in [dUTP]/[dTTP] has been shown to elevate the uracil incorporation into DNA in previous reports [58] and the uracil-associated mutations in the current study ( S1A Fig ) . Significant elevations of the uracil density at the pTET-lys2-TAA under both high and low transcription conditions were observed upon the deletion of DCD1 gene ( Fig 5 ) . The densities of uracil at the CAN1 and TDH3 genes were also elevated by the disruption of Dcd1 . These data again demonstrate that the difference in the uracil density calculated using the long-amplicon qPCR approach is critically dependent on the [dUTP]/[dTTP] balance and adequately reflects the change in the DNA composition . At CAN1 and TDH3 genes , the levels of uracil as well as the rates of transcription did not change when doxycycline was added to repress transcription from the pTET promoter ( Figs 4 and S1B ) . There was a ~170-fold difference in the level of transcription at the pTET-lys2-TAA between high and low transcription conditions while the level of uracil is elevated by ~2-fold . At TDH3 , which is transcribed at ~100-fold higher rate than CAN1 according to the RT-qPCR analysis , the uracil density was detected to be ~3-fold higher than at CAN1 in ung1Δ background , providing further corroboration for the transcription-dependent mechanism of uracil incorporation into DNA . However , when the uracil density at the pTET-lys2-TAA is compared to those at CAN1 and TDH3 genes , we observed that the transcription rate does not have a linear correlation with the level of uracil residues . Under the low transcription conditions , the pTET-lys2-TAA is transcribed at a considerably lower rate than the CAN1 gene . However , under the same conditions , there was no statistical difference in the uracil densities at these two genomic sites . On the other hand , the uracil level at the pTET-lys2-TAA under the high transcription conditions was slightly higher than that at the TDH3 gene although the latter is transcribed at about ~10-higher rate than the pTET-lys2-TAA . These discrepancies suggest that there might be factors additional to transcription that modulate the level of uracil-incorporation into DNA such as position of the replication fork , replication timing , and orientation of the transcription machinery . In apn1Δ strain , the rate of uracil-derived mutations is elevated by ~20-fold when transcription from the pTET promoter is activated [18] . The approximately 2-fold difference in the uracil density cannot wholly account for the dramatic increase in the mutation rate . An alternative , but not mutually exclusive , explanation for the transcription-dependent elevation in the mutations arising from uracil in DNA is that transcription affects the activity of the glycosylase converting the mutation-neutral uracil residues into the mutagenic AP sites . In order to test this hypothesis , we studied the mutations induced by the glycosylase CDG , which excises undamaged cytosines in addition to uracil residues , at the pTET-lys2-TAG mutation reporter under high and low transcription conditions ( Figs 2 and 3 ) . If the base-excision by the glycosylase is not affected by the state of transcription , the rate of those mutations initiated by the excision of undamaged cytosine ( G>C ) should remain the same whether the transcription of pTET-lys2-TAG reporter is activated or repressed . In five different genetic backgrounds , the CDG expression led to significant transcription-dependent elevations of the uracil-dependent ( A>C and T>G ) mutations as expected from the greater uracil density under high transcription conditions . However , we also observed smaller but still significant transcription-dependent elevations in the rate of cytosine-dependent mutations . The fold-difference between the high and low transcription conditions was 1 . 7 and 2 . 5 in apn1Δ ung1Δ and apn1Δ rad14Δ ung1Δ backgrounds , respectively , and increased to 2 . 5 and 7 . 6 when TOP1 gene was deleted from each of these strains ( Figs 2E and 3C and S2 Table ) . Compared to the transcription-dependent elevation in the CDG-induced A>C and T>G mutations , which ranges from 10- to 23-fold , the elevation of CDG-induced mutations at cytosine residues is relatively small but still significant , indicating that the efficiency of glycosylase activity is somehow affected by transcription ( Figs 2D and 3B and S2 Table ) . For the uracil-associated A>C and T>G mutations , the transcription-dependent elevation is further augmented by ~2-fold when the topoisomerase I is disrupted , implicating the transcription-associated change in the local DNA topology as one of the major factors affecting the glycosylase activity ( Fig 3B ) . When RNase Hs were disrupted as in apn1Δ rnh1Δ rnh201Δ ung1Δ strain , the cytosine-dependent mutations at the pTET-lys2-TAG were elevated by 3 . 5-fold when transcription was highly activated ( Fig 3F ) . For the uracil-derived mutations , only the mutations originating from the excision of uracil located on the top , non-transcribed strand ( i . e . T>G ) were further elevated by the disruption of RNase Hs ( S2 Table ) . The R-loop accumulation in the absence of RNase Hs affects the two DNA strands within the transcribed regions asymmetrically; the bottom , transcribed strand forms stable hybrid with the nascent RNA and the top , non-transcribed strand is left unpaired . This asymmetry is directly reflected on the specific elevation of T>G over A>C mutations at the pTET-lys2-TAG and corroborates the biochemical analysis where CDG was shown to excise uracil or cytosine from single-stranded oligonucleotide substrates ~10-fold more efficiently than from double-stranded substrates [34] . The accumulations of helical stress and single-stranded DNA patches associated with active transcription appear to be major factors in enhancing the activity of uracil DNA glycosylase , contributing to the elevated uracil-dependent mutations at highly transcribed genes . With the long-amplicon qPCR and the analysis of CDG-induced mutations , an increase in the density of uracil upon activation of transcription has been demonstrated by two independent approaches ( Figs 2 , 3 and 4 ) . The mechanism underlying such phenomena , however , is still not clear . One plausible explanation can be found in the previously reported evidence of transcription-induced endogenous DNA damage ( reviewed in [55] ) . The topological changes and DNA strand-separation necessitated by transcription is also responsible for the elevated susceptibility to genotoxic agents and the consequent accumulation of base damage as well as promoting the formation of pathological RNA:DNA hybrids or R-loops . The highly transcribed areas of the genome are more prone to the replication fork stalls and collapse , which can be significantly aggravated at repetitive sequences where transcription facilitates the formation of non-B DNA structures . While the DNA polymerases utilizing dUTP in place of dTTP during replication can account for the stochastic presence of uracil throughout the genome , DNA synthesis associated with correcting the damage or resolving the stalled replication fork at highly transcribed regions provides additional opportunities to incorporate uracil into DNA and to affect the locus-specific elevation in uracil content . Whether these involve only the short patch DNA synthesis required during NER/BER-mediated repair of base damages or additionally involve the more extensive DNA synthesis occurring in the processes of homologous recombination or break induced replication is to be determined by further investigation . In support of the model where the repair-synthesis increases the incidence of uracil incorporation into DNA , we demonstrated here that exogenously engendering damage to DNA with CPT and 4NQO , thereby inducing rounds of repair-dependent DNA synthesis , led to an accumulation of uracil specific mutations into DNA in the absence of BER at the pTET-lys2-TAA mutation reporter ( Fig 5 ) . In case of 4NQO-treated cell , we also showed the uracil-accumulation in the genome by chemically probing for the uracil-derived AP sites ( Fig 6B ) . Another important implication of these experiments is that uracil in DNA could be a significant factor in not only the mutations but also the cytotoxicity induced by various DNA damaging chemicals not specifically targeting the pyrimidine biosynthesis pathway . For the TS-targeting drug 5-FU , the main cytotoxic mechanism involves the AP sites generated from the highly frequent uracil residues incorporated into DNA due to the imbalance in [dUTP]/[dTTP] ratio [59] . The yeast cells of apn1Δ background with the severely compromised BER pathway and the inability to efficiently repair AP lesions are acutely sensitive to 5-FU . However , the apn1Δ cells become highly resistant to 5-FU when UNG1 is deleted so that the uracil-to-AP conversion cannot occur ( Fig 6C ) . We showed that UNG1-deletion can also reduce the cell sensitivity to 4-NQO treatment at a low drug concentration ( Fig 6D ) . The repair-associated DNA synthesis occurring in G1- or G2-phase of cell-cycle would be a particularly potent way of uracil incorporation into DNA because the expression of dUTPase is upregulated in S-phase ( S4A Fig and [48–50] ) . This cell-cycle dependent regulation of the available [dUTP] would ensure the minimal uracil-incorporation into DNA during replication ( Fig 8 ) , but comparatively increases the [dUTP]/[dTTP] ratio and thus the possibility of dUTP being used by DNA polymerases during the repair synthesis occurring outside of S-phase . When we lowered the [dUTP]/[dTTP] ratio by overexpressing the dUTPase-encoding gene DUT1 from the G1- or G2-specific promoters , the rates of mutations at the pTET-lys2-TAA reporter under the high transcription conditions in BER-deficient cells were significantly reduced ( Fig 7A and 7B ) , indicating that the uracil incorporated into DNA during G1 or G2 comprise a substantial source of transcription-associated mutations ( Fig 8 ) . The DUT1-overexpresion from the ubiquitous pGAL promoter , however , resulted in much greater reduction in the mutation rate at the pTET-lys2-TAA reporter under high transcription conditions . This effect could largely be explained by the higher overall level of expression from pGAL promoter but also could indicate that a considerable level of uracil-incorporation does occur in all cell cycles including S . The effect of the DUT1-overexpression from S-specific promoter in reducing uracil-associated mutations when the pTET-lys2-TAA reporter under high-transcription conditions was relatively smaller compared to the G1- or G2-specific promoters . And the overexpression of DUT1 from pGAL or S-specific promoter as well as G1- or G2-specific promoters had no effect on the mutation rates when the transcription of the pTET-lys2-TAA was repressed , indicating that the level of uracil incorporated into DNA during replication , which would be uniform throughout the genome and only affected by the DUT1-expression from the pGAL or S-specific promoter , cannot induce uracil-associated mutations to a significant degree . In summary , we found a novel mechanism of introducing uracil into DNA during the damage-induced repair synthesis during G1- or G2-phase of cell cycle . The repair-coupled uracil-incorporation would be a way to non-uniformly alter the nucleotide composition of genomic DNA . The degree of alteration and the extent of uracil-incorporation would depend on the extent of repair synthesis occurrence and would be expected to be greater at regions of frequent endogenous DNA damage i . e . highly transcribed genomic loci . Such a role played by transcription in changing the nucleotide composition locally would apply to other types of non-canonical residues . We speculate that a similar mechanism is involved in specifically elevating the ribonucleotides at highly transcribed regions . Previous studies have shown that ribonucleotide-dependent mutations are highly elevated by transcription [60 , 61] . And similar to DUT1 , RNR1 , the gene encoding the essential , regulatory subunit of the ribonucleotide reductase , is regulated in a cell-cycle dependent manner to ensure the optimal [dNTP]/[rNTP] ratio for the replication during S-phase [62] . There are several remaining questions to be answered through further studies such as whether the correlation between uracil and transcription apply linearly genome-wide and whether other sources of the endogenous DNA damage such as non-B DNA structures could elevate uracil-content through DNA repair synthesis . Further work is also needed to determine the specific repair pathway directing the uracil incorporation at highly transcribed regions .
Yeast strains used in this study were derived from YPH45 ( MATa , ura3-52 ade2-101 trp1Δ1 ) . The construction of strains containing the his4Δ::pTET-lys2-TAA allele was previously described [19] . The his4Δ::pTET-lys2-TAG allele was introduced by the pop-in/pop-out two-step allele replacement method to replace the his4Δ::pTET-LYS2 allele on Chr III using BglII-digested pSR982 . Further gene deletions of the yeast strains containing the pTET-lys2-TAA or -TAG allele were carried out by the standard one-step gene disruption method . The subsequent Cre/loxP-mediated deletion of the marker gene was carried out as appropriate [63] . pCDG is a 2-micron plasmid with a mutant human UDG encoding sequence under the pGAL control with the TRP1 marker [25] and was a gift from Dr . Bruce Demple ( Stony Brooke School of Medicine , Stony Brooke , NY ) . The cell-cycle specific Dut1-overexpression plasmids were constructed by digesting p426-GAL1-DUT1 [18] with BamHI and SacI to remove and replace the pGAL promoter with the promoters of yeast genes CLN2 , HHO1 or CLB2 . Sequences of primers used to amplify the promoters of CLN2 , HHO1 or CLB2 genes from the yeast genome were previously described [14 , 52] . The 537 nt sequence encoding the CLN2 PEST domain was synthesized through the Invitrogen GeneArt Gene Synthesis service and was inserted into the EcoRI/BamHI digested pCLN2-DUT1 , pHHO1-DUT1 , and pCLB2-DUT1 plasmids . Each of these plasmids were further modified by the addition of 3XHA-encoding sequence to the C-terminal ends . Mutation rates and frequency were determined by fluctuation analysis and the method of the median; the 95% confidence intervals were calculated as previously described [64] . For the CDG expression experiments , the indicated strains were transformed with either the empty vector pYES2 or pCDG and selectively plated on the synthetic complete media with 2% dextrose media lacking tryptophan ( SCD-Trp ) . Individual colonies were inoculated into a 1 mL SC-Trp cultures supplemented with 2% galactose and 1% raffinose . After 4 days of growth at 30°C , appropriate dilutions were plated on SCD-Trp to determine total cell numbers and on SCD-Trp-Lys to determine the number of Lys+ revertants in each culture . For the Dut1-overexpression experiments , the indicated strains were transformed with pRS426 or pGAL-DUT1 , pCLN2-DUT1-PEST-HA , pHHO1-DUT1-PEST-HA , or pCLB2-DUT1-PEST-HA . After culturing in SC-Ura media with 2% galactose/1% raffinose or SC-Ura with 2% glycerol/2% ethanol for 4 days at 30°C , the Lys+ revertants were selected on SC-Ura-Lys plates . Where “low transcription” is indicated , 2 μg/mL doxycyline was added to the media . To determine the mutation frequency following drug treatments , overnight cultures in YEPD ( 1% yeast extract , 2% peptone , 2% glucose ) were diluted to an OD of 0 . 2 and grown for 4 hrs at 30°C . 5-FU , 4NQO , or CPT was added to the yeast culture to a final concentration of 10 μM , 0 . 2 μg/mL and 100 μM , respectively , and incubated at 30°C for 20 hours with shaking . Cell pellets were spun and washed twice and plated on SCD-Lys plates and on YEPD plates . Colonies were counted after 48 hrs and the mutation frequency was calculated as described above . The mutation spectra and the 95% confidence intervals for the specific mutation types were determined as previously described [31] . The uracil density in DNA was quantified using the long amplicon quantitative real-time PCR approach as previously described with the following modifications [65] . 5 μg of each DNA samples were digested with 1 unit of UDG ( New England Biolabs ) for 30 mins followed by an incubation with EndoVIII ( New England Biolabs ) for 1 hr at 37°C . After DNA was precipitated and dissolved in water , 100 ng of DNA from each sample was used to carry out qPCR in triplicates . Primers used for the amplification of the yeast LYS2 , CAN1 , and TDH3 are listed in the S7 Table . The amplification was performed using Bioline SensiFAST SYBR No-ROX kit and Biorad CFX Connect Real-Time PCR machine . Cycling parameters were as follows: For the short amplicons: 95°C for 3 min followed by 40 cycles of 95°C for 5 s , 60°C for 10 s and 72°C for 10 s . For the long amplicons: 95°C for 3 min followed by 40 cycles of 95°C for 5 s , 60°C for 10 s and 72°C for 1 min . The frequency of uracil in DNA was calculated by assuming that the UDG/EndoVIII treatment leads to the strand breaks specifically at the location of uracil , which results in the quantitative loss of the template DNA and consequently the reduced qPCR amplification efficiency . The uracil density at pTET-LYS2 , CAN1 , and TDH3 was inferred from the reduction in the amplification of the UDG/EndoVIII-treated samples relative to the untreated samples when amplifying a large 3 to 4 kb regions at each gene . For each gene , qPCR amplification of ~ 100 bp target area was used to normalize for the template DNA loading . Primers used for the amplification of the 100 bp or the 3 to 4 kb regions of the yeast LYS2 , CAN1 , and TDH3 are listed in the S7 Table . Assuming the Poisson distribution of uracil in the large amplicons , the density of uracil was calculated using the following equation where the amplification percent of the large amplicons in the UDG/EndoVIII-treated and in the untreated controls , relative to the amplification of the small ~100 bp amplicons , are represented by At and Au , respectively . Fluorescent labelling of the uracil-derived AP sites was performed as previously described with minor modifications [45] . Briefly , genomic DNA was isolated from the ung1Δ yeast cells treated with the indicated concentrations of 5-FU or 4NQO and treated with 10 mM methoxyamine . Then , 5 μg of each DNA sample was treated with 1 unit of UDG ( New England Biolabs ) for 30 mins at 37°C and labeled by incubation with 5 mM AA3 for an additional hour . Following the addition of Cy5 azide ( Lumiprobe ) to the final concentration of 0 . 5 mM followed and the freshly prepared CuBr/TBTA ( 1:4 in DMSO/t-BuOH 3:1 , 0 . 5 mM , Sigma ) , the mixture was shaken at 37°C for 2 hrs . The Cy5/AA3-labeled DNA was purified by ethanol precipitation , heated at 95°C , and transferred to a positively charged nylon membrane using the Bio-Dot microfiltration apparatus ( Biorad ) . The membrane was scanned using the ChemiDoc MP imaging system ( Biorad ) with a Cy5 filter and quantified using the Image Lab software . Synchronization of yeast cells at G1 was carried out by arresting bar1Δ cells with α-factor as previously described [66] . Briefly , yeast cells were grown in either YEPD or SC-Ura + 2% glucose overnight . The overnight cultures were diluted to an OD600 of 0 . 2 ( for YPD ) or 0 . 4 ( for SC-Ura + glucose ) and grown at 30°C until reaching an OD600 of 0 . 8 . The cells were washed twice followed by the addition of α-factor ( Sigma ) to a final concentration of 50ng/mL and grown for 2 hrs or until ~100% of the cells were unbudded with a typical pear/ schmoo shape characteristic of α-factor arrest . To remove the α-factor , cells were washed twice with water and resuspended in the media containing 50 μg/mL pronase . Cells were collected after every 15 minutes ( YEPD ) or 20 minutes ( SC-Ura + 2% glucose ) following the release and used for RNA analysis . The overnight cultures were diluted to an OD600 of 0 . 2 and grown at 30°C for 4 hrs before adding 5-FU , 4NQO or DMSO to a final concentration of 10 μM , 0 . 2 μg/mL , and 0 . 1% , respectively . Following incubation at 30°C for 20 hrs with shaking , cells were spun down , washed twice , and plated on YEPD plates . Colonies were counted and the percent survival of 5-FU- or 4NQO-treated cultures were calculated relative to the DMSO-added cultures .
|
Uracil in DNA , a major source of spontaneous mutations , can occur through the deamination of cytosine residues or through the direct incorporation of dUTP by DNA polymerases . Recent studies in yeast have shown that the uracil-associated mutations occur more frequently at highly transcribed regions . Because the reduction in dUTP pool decreased these mutations , it was postulated that the extent of uracil-incorporation into DNA is significantly affected by the local transcription activity . We show here that the higher transcription rate does correlate with the higher uracil-density in the yeast genome . We further provide multiple lines of evidence supporting a model of uracil-incorporation into DNA that is dependent on the repair synthesis of transcription-associated DNA damage .
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2018
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Unscheduled DNA synthesis leads to elevated uracil residues at highly transcribed genomic loci in Saccharomyces cerevisiae
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Murine models of urinary tract infection ( UTI ) have provided substantial data identifying uropathogenic E . coli ( UPEC ) virulence factors and assessing their expression in vivo . However , it is unclear how gene expression in these animal models compares to UPEC gene expression during UTI in humans . To address this , we used a UPEC strain CFT073-specific microarray to measure global gene expression in eight E . coli isolates monitored directly from the urine of eight women presenting at a clinic with bacteriuria . The resulting gene expression profiles were compared to those of the same E . coli isolates cultured statically to exponential phase in pooled , sterilized human urine ex vivo . Known fitness factors , including iron acquisition and peptide transport systems , were highly expressed during human UTI and support a model in which UPEC replicates rapidly in vivo . While these findings were often consistent with previous data obtained from the murine UTI model , host-specific differences were observed . Most strikingly , expression of type 1 fimbrial genes , which are among the most highly expressed genes during murine experimental UTI and encode an essential virulence factor for this experimental model , was undetectable in six of the eight E . coli strains from women with UTI . Despite the lack of type 1 fimbrial expression in the urine samples , these E . coli isolates were generally capable of expressing type 1 fimbriae in vitro and highly upregulated fimA upon experimental murine infection . The findings presented here provide insight into the metabolic and pathogenic profile of UPEC in urine from women with UTI and represent the first transcriptome analysis for any pathogenic E . coli during a naturally occurring infection in humans .
Animal models of infection have provided valuable insight into diverse mechanisms of bacterial pathogenesis . Application of microarray technology to these models has further enabled analysis of bacterial global gene expression during infection of a specific host . These studies have included transcriptional profiles of pathogenic Escherichia coli in macrophages [1] , host epithelial cells [2] , and mice [3] , [4] . More recently , a limited number of groups have measured genome-wide expression of bacterial pathogens during infections of a human host , including Vibrio cholerae in rice water stool of cholera patients [5] , [6] , Pseudomonas aeruginosa in sputum from cystic fibrosis patients [7] , and M . tuberculosis in resected lung specimens [8] . When these data were compared to results of animal model transcriptional studies , host-specific differences were observed [8] . The urinary tract is among the most common sites of bacterial infection in humans , and E . coli is by far the most common species infecting this site , accounting for more than 80% of community-acquired infections [9] . Uncomplicated UTIs include cystitis infections in adult women who are not pregnant and do not suffer from structural or neurological dysfunction [10] . Cystitis , a clinical diagnosis presumed to represent infection of the bladder , is defined by the presence of ≥103 bacteria/ml in a midstream , clean-catch urine sample from a patient with symptoms including dysuria , urinary urgency , and increased frequency [11] , [12] . Forty percent of adult women will experience symptoms of cystitis during their lifetime and there is a 25% risk that a second symptomatic episode will occur within 6–12 months [13] . Uropathogenic E . coli ( UPEC ) represent a specific subset of E . coli capable of colonizing the urinary tract and eliciting the symptoms of cystitis and pyelonephritis . Genetically distinct from commensal E . coli found in the intestinal tract , these strains contain numerous genomic insertions into the “backbone” E . coli chromosome , both as pathogenicity-associated islands ( PAIs ) [14] , [15] , [16] and shorter islet sequences . In pyelonephritis isolate CFT073 , for example , genomic islands and islets comprise over 20% of the genome [14] . Acquired by horizontal gene transfer , PAIs often encode proteins that contribute to pathogenesis; loss of these regions may attenuate virulence [17] , [18] . An array of virulence and fitness factors has been described that allow UPEC to access and persist in the urinary tract niche . Flagellin-dependent motility is required for ascension to the kidneys [19] and secreted toxins including hemolysin , cytotoxic necrotizing factor 1 , and secreted autotransporter toxin elicit damage to the host epithelium [20] , [21] , [22] . Polysaccharide capsule [23] and immunosuppressive proteins [24] also contribute to urinary tract colonization and may allow immune evasion . Finally , as the urinary tract represents a unique nutritional niche , TonB-dependent metal acquisition systems are required for UPEC survival in this iron-limited environment [25] and recent evidence suggests that these pathogens metabolize peptides and amino acids as a primary carbon source [26] . Transcriptome analysis of strain CFT073 during murine experimental UTI demonstrated that many of these fitness factors are upregulated during infection [4] . Perhaps the most well-defined UPEC virulence factors are type 1 fimbriae , adhesive structures required for complete colonization of the murine urinary tract [27] , [28] , [29] . Encoded by virtually all E . coli strains , type 1 fimbriae mediate urinary tract adherence via the FimH fimbrial tip adhesin , which binds to mannosylated uroplakins located on the uroepithelium surface [30] . This interaction elicits a host response , including induction of pro-apoptotic and epithelial differentiation factors [31] , as well as secretion of the pro-inflammatory cytokines interleukin-6 and IL-8 [32] . Expression of type 1 fimbriae is phase variable , controlled by an invertible DNA element that contains the promoter for the major structural subunit gene , fimA [33] . During murine experimental UTI , fimA was the fourth most highly-expressed gene and the other fim operon genes were also upregulated in vivo as compared to in vitro culture [4] . To date , much of the work in this field has used E . coli strains isolated from patients with UTI . The investigation of UPEC pathogenesis , however , has largely been conducted using in vitro models and the well established murine model of ascending UTI [34] . Volunteer colonization studies utilizing a non-pathogenic asymptomatic bacteriuria strain have shed light into mechanisms that promote E . coli survival within the human urinary tract [35] , [36] , but do not represent UTIs caused by E . coli with full pathogenic potential . If we wish to fill gaps in our understanding of this widespread human pathogen , it is crucial to focus on UPEC gene expression during naturally-occurring human UTI . In this study , we measure gene expression in E . coli isolated immediately following collection from the urine of eight women experiencing symptoms of UTI . The data presented provide insight into the metabolic and virulence profile of multiple UPEC strains during human infection . We propose that E . coli utilizes an array of iron acquisition systems and has access to plentiful carbon sources during human UTI , allowing for robust replication and may downregulate or transiently express a major virulence factor , type 1 fimbria .
Thirty-six urine samples were collected from 34 female patients ( ages 21–89 , mean = 47 ) attending a urology clinic with presumptive bacteriuria . Nineteen urine samples were culture-negative and six specimens were culture-positive for bacterial species other than E . coli , including coagulase-negative Staphylococcus sp . ( n = 2 ) , Acinetobacter baumannii ( n = 1 ) , Enterobacter cloacae ( n = 1 ) , Enterococcus sp . ( n = 1 ) , and Klebsiella pneumoniae ( n = 1 ) . Eleven women were culture-positive for E . coli and 10 of these specimens were suitable for our study , as poor RNA yield prevented analysis of one E . coli specimen . Of these ten E . coli-positive samples , two urine specimens contained mixed infections of two different E . coli strains . O and H serotyping conducted on these 12 E . coli isolates indicated that O6 and O25 , which are frequently associated with UTI isolates [37] , were the most common serogroups , representing 7 of 12 strains ( Table S1 ) . Two isolates ( 121 and 361 ) , obtained from the same patient on separate clinic visits ( 5 months apart ) , had identical serotypes , but could not be conclusively identified as the same strain . Antibiotic susceptibility testing indicated that these clinical isolates also showed high frequencies ( up to 7 of 12 isolates ) of resistance to common UTI therapies , including trimethoprim-sulfamethoxazole ( Table S2 ) . Of the eight isolates ultimately used in our study ( see below ) , all were collected from pyuria-positive patients who were not catheterized at the time of collection and 6 of 8 were collected from patients reporting a previous UTI . Although these isolates were obtained from urology patients , some with histories of UTI or kidney stones , extensive genotype analysis compared against a collection of over 300 E . coli isolates indicated that the virulence gene profiles of these strains most closely matched other cystitis isolates and not fecal-commensal E . coli ( P . Vigil and H . Mobley , in preparation ) . CFT073-specific microarrays were used to measure transcript levels from the eight E . coli isolates obtained from single-strain infections , both immediately from the urine of infected women and following static culture to mid-exponential phase in pooled human urine ex vivo . Under each condition , 20–30% of all genes measured ( 5379 ORFs ) were classified as expressed for each isolate ( Table S3 ) . It is important to note that genes classified as not expressed may be absent or divergent in these strains . In all strains , the most highly expressed genes in urine from women with UTI were those encoding ribosomal subunits . Indeed , ribosomal genes comprised 24–54% of the top 50 most highly expressed genes in each clinical isolate . It is well established that E . coli rRNA and ribosomal protein mRNA synthesis increases proportionally to growth rate [38] , [39] . Consistent with this , the most highly expressed non-ribosomal genes also suggest rapid bacterial growth in vivo ( Table 1 ) . Genes encoding transcription and translation machinery ( infC , yfiA , rpoA , rhoI , tufA , fusA , tufB , efp ) , F0F1 ATPase components ( atpE , atpF ) , fatty acid biosynthesis factors ( acpP , fabI ) , protein folding and secretion apparatus ( slyD , secG , prlA ) , and outer membrane components ( ompA ) were among the most highly expressed during human infection . Because variable host nucleic acid present in the in vivo samples prevented direct comparison between in vivo and in vitro conditions , a more conservative , relative comparison was derived . To identify expression differences between E . coli growth in urine during cystitis and growth in urine ex vivo , genes were ranked in order of microarray signal intensity , yielding an estimate of relative gene expression . For each isolate , these rank values were compared between in vivo and in vitro conditions . Genes for which the expression rank significantly changed between these conditions were considered differentially expressed . Although we noted that two cold-shock-associated genes , cspA and deaD , were upregulated in human urine samples ( Table 2 ) , expression of cspA is known to occur during non-cold shock stress conditions [40] , [41] . Moreover , both deaD and cspA were also upregulated by UPEC [4] and an ABU strain [35] following expulsion from the murine and human bladders , respectively . Nevertheless , to assess the effect of temperature downshift that may have occurred during sample processing , transcript levels of a representative virulence gene , fimA , were measured after a urine culture of strain CFT073 was moved from 37°C to room temperature ( Fig . S1 ) . fimA transcript levels did not significantly change after 10 min ( or up to 60 min ) at room temperature , suggesting that sample processing was not responsible for these results . Cell division factors ( ftsW , mraZ ) , tRNA processing and translation machinery ( rnpA , tgt , rplQ , rimP , truB , valS , gltX , yhdG , ybeA , ycfB , lepA , ychf ) , and protein secretion components ( secD , secF , yidC ) were among genes upregulated in vivo by the majority of strains , suggesting that E . coli may be replicating faster in the human urinary tract than in vitro ( Table 2 ) . Similarly , genes involved in nucleotide synthesis ( guaA , guaB , pyrG , pyrH ) , lipopolysaccharide assembly ( lptF ) , peptidoglycan recycling ( yjfG ) , and enterobacterial common antigen synthesis ( rfe ) were also upregulated . Taken together , these data indicate that E . coli was likely replicating very rapidly during symptomatic UTI in women . To confirm these microarray findings , qPCR was performed using in vivo- and in vitro-derived cDNA templates of two representative E . coli strains isolated in this study , AL151 and AL371 . Strains were selected based on availability of in vivo cDNA . The threshold cycle ( Ct ) values of the 13 genes measured ( gapA , hma , fyuA , fimA , papA_2 , fliC , sat , sisA , glnA , gdhA , ackA , acs , and rplD ) correlated ( P<0 . 001 or r<−0 . 8431 ) with the normalized microarray signal intensities of each gene ( Fig . 1A ) . Similarly , genes identified as differentially expressed between in vivo and in vitro conditions were consistently found to be up- or down-regulated in these two strains by qPCR measurement ( Fig . 1B ) . Finally , PCR using in vivo-derived cDNA generally confirmed expression of select virulence genes ( Fig . 1C ) . Genes involved in aerobic respiration tended to be highly expressed by most strains assessed immediately after expulsion from the human bladder . Encoding a component of cytochrome d complex , which is maximally expressed during microaerobiosis , cydA was among the top 50 most expressed non-ribosomal genes by four isolates ( Table 1 ) and was in the top 3% of genes expressed by all strains in vivo and was similarly expressed during urine culture in vitro . Expression of the cytochrome o oxidase genes cyoABCDE , which are expressed in oxygen-rich conditions , varied among patients . These genes were strongly expressed by 5 of 8 strains in vivo ( top 7% ) , more weakly expressed by 2 of 8 strains ( top 20–30% ) , and just below background in one strain . This is in contrast to a recent E . coli transcriptome study for an asymptomatic bacteriuria strain , which noted consistent downregulation of cyoABCD during intentional colonization of the human bladder [35] . For all strains , expression of cyoABCD and cydAB did not appear to differ between in vivo and in vitro samples or from expression by E . coli CFT073 during experimental infection of the murine urinary tract [4] , suggesting similar oxygenation in these conditions . Genes that encode a terminal electron acceptor pathway used during anaerobiosis were not uniformly expressed by all strains . For example , the genes encoding nitrate reductase I , narGHJIK , were only expressed in vivo by two strains , AL121 and AL151 . Formate dehydrogenase N ( fdnGHI ) , induced by nitrate [42] and anaerobic conditions [43] , was expressed by E . coli in half of UTI patients ( isolates AL121 , AL151 , AL291 , and AL361 ) . Nitrite-inducible genes nirBDC were also expressed in half of patients ( isolates AL121 , AL151 , AL291 , and AL371 ) . Interestingly , the three patient isolates that only weakly expressed cyoABCDE also expressed nitrate reductase , formate dehydrogenase N , and nitrite reductase , suggesting that these isolates likely experienced a more anaerobic and nitrate/nitrite-rich environment and may be using these products for anaerobic respiration . Together , these data indicate that oxygen and/or nitrate levels in voided urine vary by patient and suggest that pathogenic E . coli adapts to utilize either form of respiration . Previous transcriptomic analysis of E . coli indicated that the murine urinary tract is nitrogen-limiting for this pathogen , despite a high urea concentration in urine [4] . Glutamine synthetase ( glnA ) , which assimilates ammonia with high affinity in an energy-dependent manner and is transcriptionally induced by nitrogen-limited growth [44] , was among the most highly expressed genes in urine from 5 of 8 cystitis patients ( Table 1 ) . By qPCR , glnA was upregulated in vivo ( 2 . 9-fold ) in the two strains tested ( AL151 and AL371 ) , while the low affinity energy-independent glutamate dehydrogenase gdhA , which is transcriptionally repressed in low nitrogen conditions [45] , was downregulated 8 . 3-fold ( Fig . 1B ) . This indicates that E . coli similarly experiences nitrogen limitation during infection of the human urinary tract . High concentrations of nitrogen in urea are not available to the urease-negative E . coli . The gene encoding acetyl-CoA synthetase , acs , was one of the most strongly downregulated genes in all E . coli strains during growth in urine from women with UTI as compared to culture in urine ex vivo ( Table 3 ) . Involved in acetate assimilation by conversion to pyruvate , expression of this enzyme is activated by decreasing oxygen and increasing cAMP levels [46] . In contrast , genes involved in acetate excretion , shown to contribute to urovirulence in a murine model [47] , were strongly expressed in vivo . The phosphate acetyltransferase ( pta ) gene was among the top 100 genes upregulated in vivo in 6 of 8 isolates ( Table 2 ) and acetate kinase ( ackA ) was one of the most highly expressed non-ribosomal genes in 50% of the strains ( Table 1 ) . By qPCR , acs was downregulated nearly 600-fold in vivo , while ackA was relatively unchanged ( Fig . 1B ) . Acetate-induced genes [48] glcBGD and the glyoxylate shunt genes aceAB were also downregulated ( Table 3 ) . Furthermore , components of the pyruvate dehydrogenase complex , which functions upstream of AckA-Pta to convert pyruvate to acetyl-CoA , aceE and lpdA were among the most highly in vivo expressed genes in the majority of patient isolates ( Table 1 ) . These expression patterns imply that E . coli produces , but does not assimilate acetate during growth in the human bladder . In bacteria , the acetate switch occurs when cells transition from acetate dissimilation to assimilation . The expression of dissimilatory genes by E . coli during infection of the human urinary tract indicates not only that the bacteria have not yet reached stationary phase in vivo , but also that they may be utilizing acetogenic carbon sources . Acetogenesis occurs during mixed acid fermentation under anaerobic conditions or aerobically when rapid growth on excess carbon sources limits flux through the tricarboxylic acid ( TCA ) cycle by excessive NADH production ( reviewed in [49] ) . Because data presented here support rapid aerobic or microaerobic growth of E . coli during human UTI , this suggests that the observed acetogenesis is due to “overflow metabolism” and not mixed acid fermentation . This further implies that easily assimilable carbon sources are available to UPEC during symptomatic UTI . Peptides and amino acids have been previously implicated as primary carbon sources for UPEC during colonization of the murine urinary tract [26] . Di- and oligopeptide transport components dppA and oppA , shown previously to contribute to UPEC virulence [26] , were strongly expressed by all strains during UTI in women ( Fig . 2A ) . Additionally , a putative tripeptide transporter , ygdR ( tppB ) , was upregulated in vivo by all E . coli isolates ( Table 2 ) and was among the top 50 most highly expressed non-ribosomal genes in 5 of 8 strains ( Table 1 ) . Other amino acid or metabolite transporters , yifK , yhfC , and brnQ were also upregulated in vivo . These findings suggest that E . coli may use peptides as an energy source during human UTI , as well . When compared to culture in urine ex vivo , E . coli in voided urine from UTI patients downregulated a number of genes involved in metabolic functions ( Table 3 ) . The talA gene , which encodes one of two transaldolases in the E . coli pentose phosphate pathway and does not to contribute to UPEC fitness in a murine model [26] , was downregulated . Consistent with rapid growth and TCA cycle saturation , genes in this pathway ( sdhB , acnA , and fumA ) as well as α-ketoglutarate permease ( kgtP ) , which imports a TCA cycle intermediate , were also downregulated . Indeed , growth on excess glucose ( an easily assimilable carbon source ) has long been appreciated to inhibit expression of sdhAB [50] and the succinyl-CoA synthetase complex ( reviewed in [49] ) . Overall , dehydrogenase-type enzymes were frequently downregulated in vivo compared to growth in vitro , consistent with the hypothesis that rapid growth in vivo requires E . coli to adapt by modifying the TCA cycle to optimally regenerate NAD+ . Together , the finding that genes involved in acetogenic growth , peptide import , and the TCA cycle are expressed or modulated during human cystitis is consistent with the requirement of these pathways for UPEC virulence and support the current model of UPEC metabolism during UTI [26] , [47] . While a number of studies have assessed expression of UPEC virulence genes during experimental infection of animal models [3] , [4] , [51] , much less is known about expression patterns during human infection . These microarray data show most strikingly that the expression of genes involved in adherence to host tissues that are highly expressed during murine infection [4] , was not detected in the majority of patient isolates ( Fig . 2A ) . Genes encoding P , F1C , or Auf fimbriae were not expressed ( i . e . , below background ) in any E . coli strains after expulsion from the human urinary tract or gene absence/divergence otherwise prevented detection of these transcripts . Furthermore , the type 1 fimbrial adhesin fimH , which is required for virulence during murine infection [27] , [28] and invasion [29] , was only expressed at detectable levels by E . coli in 2 of 8 patients . These strains , AL231 and AL241 , also expressed the remaining genes in the fim locus , while the other six strains did not ( Fig . 2B ) . To validate these microarray findings , PCR was performed on in vivo-isolated cDNA from all strains except AL051 and AL121 ( for which no in vivo-derived cDNA sample remained ) . This confirmed the expression of the major structural subunit fimA only by strains AL231 and AL241 , as well as lack of P fimbrial ( papA_2 ) gene expression by strains AL151 , AL241 , AL361 , and AL371 ( Fig . 1C ) . Strains AL231 and AL241 , which were shown to express fimA in vivo by RT-PCR ( Fig . 1C ) , encoded genes that were 90% identical to fimACFT073 ( Fig . S4 ) . Because fimA transcripts were not detected by microarray for these strains ( Fig . 2 ) , we can conclude that our microarray hybridization conditions required greater than 90% sequence identity to yield signal significantly above background . PCR results for additional virulence genes were also generally consistent with their expression by microarray ( Fig . 1C , Fig . 2A ) . Few patient isolates appeared to express toxin-encoding genes with significant sequence identity to CFT073 toxins above background levels in vivo ( Fig . 2A ) . Two isolates expressed sat or genes encoding the non-ribosomal peptide/polyketide colibactin significantly above background and expression of both colibactin and tsh was only detected in a single isolate ( strain AL291 ) . Consistent with transcriptome data from murine UTI [4] , flagellin ( fliC ) gene expression was downregulated in vivo , as compared to in vitro culture ( Fig . 1B ) . As alluded to above , metabolic pathways have also been implicated in UPEC virulence . The central metabolic pathways of gluconeogenesis and the TCA cycle are required for UPEC fitness in the murine urinary tract [26] and sdhB , pckA and tpiA , encoding enzymes in these pathways , although downregulated compared to growth in vitro , were nonetheless expressed in all patient isolates ( Fig . 2A ) . Other metabolic genes implicated in urovirulence , including guaA and argC [52] were also expressed in vivo by all E . coli strains and guaA was upregulated relative to culture in urine ex vivo ( Table 2 ) . Expression of D-serine dehydratase ( dsdA ) , which processes the putative metabolite signal D-serine [53] , was detected in half of the bladder-expelled E . coli strains measured . Genes involved in siderophore production and iron acquisition were globally the most highly expressed fitness determinants across all eight isolates following infection of the human urinary tract . All isolates robustly expressed tonB , fepA , and fyuA , although genes for the synthesis of enterobactin and yersiniabactin siderophores were sporadically detected , possibly due to inherently lower transcript levels for these enzymes ( Fig . 2A ) . Nearly all ( 7 of 8 ) strains expressed the heme receptor-encoding chuA and in half of patients , this gene was among top 50 most highly expressed non-ribosomal genes ( Table 1 ) . In contrast , the heme receptor hma was expressed above background in just 4 of 8 patient isolates . In vivo expression of genes for the salmochelin receptor iroN and aerobactin receptor iutA was only observed in two isolates , while receptor genes ireA , fpbA1 and fbpA2 were not expressed by any strains , either in vivo or in vitro . Despite these differences , all strains were capable of growth in vitro under iron-limiting culture conditions in the presence of the chelator 2′2-dipyridyl ( 200 µM ) , although strains AL231 and AL241 could not replicate under more stringent iron-limitation ( 400 µM ) ( Fig . S2 ) . These data provide further support for the well-established model of the human urinary tract as an iron-limiting environment [4] , [25] , [35] . Interestingly , in vivo and in vitro expression of iron uptake systems did not always correlate and isolates occasionally expressed a specific system in only one condition . For example , patient isolates AL121 and AL241 expressed genes coding for yersiniabactin production only during in vitro culture , while strain AL231 expressed these genes strictly during in vivo conditions ( Fig . S3 ) . For strain AL231 , poor in vitro expression of these and other genes involved in iron acquisition ( including fep/ent and iro loci ) correlated with the strain's inability to replicate under stringent iron limitation ( Fig . S2 ) . This apparent differential expression of iron uptake genes implies that the extent of iron limitation ( and by implication , Fur regulation ) differs among individual patients , as well as between the human bladder and urine ex vivo . Furthermore , it suggests additional regulation of iron acquisition systems during E . coli infection; indeed oxygen tension has been shown to affect expression of iron uptake genes by other pathogens [54] . A subset of fitness genes expressed by E . coli following infection of the human urinary tract was specific to pathogenic E . coli . That is , these genes are absent from the fecal-commensal E . coli strain K12 , suggesting that they represent horizontally-acquired , putative fitness genes . Iron acquisition components ( fyuA , chuA , chuS , chuW , chuX ) , capsule synthesis genes ( kpsF , kpsD , kpsU , kpsC ) , an inflammatory suppressor ( sisA ) , a PAI-associated prophage integrase gene ( c2418 ) , and several hypothetical genes were expressed by the majority of UPEC patient isolates in vivo ( Table 4 ) . These data indicate that the patient isolates examined here express an array of pathogen-specific genes in vivo and further imply that they indeed represent pathogenic E . coli strains . To compare UPEC virulence gene expression in different mammalian hosts , relative expression levels of 46 fitness genes by CFT073 following experimental murine UTI ( derived from [4] ) were compared to the average relative expression levels ( average expression rank ) by E . coli patient isolates following human UTI . Overall , relative UPEC virulence gene expression in mice positively correlated with expression in a human host ( Spearman r = 0 . 5890; P<0 . 0001 ) . Genes involved in iron acquisition and metabolism correlated most strongly between the two data sets , with most having less than 10% difference in relative ( ranked ) expression ( Fig . 3 ) . fyuA was the exception , due to its poor expression by CFT073 [4] , likely attributed to mutations preventing yersiniabactin production [15] , [55] . Expression of toxin-encoding genes was moderately similar in expelled urine of the two hosts , differing by only 30–50% . In contrast , adhesin and fimbriae expression was quite different between human and murine urine , with most genes having more than 50% relative expression difference between the two hosts . For example , fimA was the fourth most highly expressed gene ( relative expression value: 5375 ) by E . coli CFT073 during murine infection , while it had an average rank of 3873 out of 5379 ORFs in women with UTI ( relative expression value: 1506 ) . Surprisingly , we found that most ( 6 of 8 ) E . coli isolates did not express type 1 fimbriae in the urine of patients with UTI . One possible explanation is that these strains do not encode intact type 1 fimbrial genes or that expression of these genes is defective . To distinguish among these possibilities , we examined expression of type 1 fimbriae in vitro by the 8 clinical isolates . Using a PCR-based assay ( Fig . 4A ) , we determined the orientation of the fim invertible element , the 314 bp region that contains the promoter for the major structural subunit fimA and is responsible for phase variation of type 1 fimbriae [33] . As expected , after two 48 h static passages to enrich for type 1 fimbriae expression , all strain populations consisted of bacteria in both the phase-on and phase-off orientations ( Fig . 4B ) . In contrast , when isolates were cultured with aeration for 4 h , only the phase-off orientation could be detected . Invertible element orientations correlated with detection of FimA by western blot ( Fig . 4C ) . Except for strain AL051 , from which neither fimA nor the invertible element could be PCR-amplified , all strains expressed an approximately14 kDa protein that reacted with antiserum raised against FimACFT073 . Band intensity may reflect differences in expression level among strains or , more likely , differences in antibody reactivity to diverse FimA antigens . Indeed , the nucleotide sequences of the fimA genes of the clinical isolates are 90–99% identical to fimACFT073 , with strain AL371 having the highest identity ( Fig . S4 ) . Finally , to assess the assembly of functional fimbriae , the ability of these isolates to agglutinate guinea pig erythrocytes in a mannose-sensitive manner was measured . With the exception again of AL051 , all strains exhibited some degree of mannose-sensitive hemagglutination , indicative of type 1 fimbrial production ( Fig . 4D ) . Strain AL371 consistently displayed weak , but detectable hemagglutination . Together , these data demonstrate that , although expression of type 1 fimbrial genes was generally not detected in the urines of cystitis patients , 7 of 8 clinical E . coli isolates obtained from women with UTI are capable of appropriately expressing functional type 1 fimbriae in vitro . The transcriptome analyses presented in this study identified genes differentially expressed in the urine of different mammalian hosts ( Fig . 3 ) . However , these data were obtained by comparing the expression of E . coli clinical isolates following collection from human UTI with E . coli strain CFT073 expression during murine experimental UTI . As a result , the expression incongruencies could be due to inherent strain-specific differences that exist between the model E . coli strain CFT073 and the clinical isolates collected from infected women . To address this , the isolates ( except for Fim− strain AL051 ) were tested in the murine model of ascending UTI . Unlike fecal strain EFC4 , which was shown previously to have low infectivity in mice [56] , all isolates except AL241 colonized the bladders of CBA/J mice ( Fig . 5A and data not shown ) . To directly compare the gene expression of a single strain after its colonization of the human and murine urinary tracts , urine was collected and pooled throughout the 48 h infection for strains AL151 and AL371 and transcripts isolated from these pooled samples were quantified by qPCR . In contrast to human infection , both clinical isolates strongly upregulated fimA in the urine of infected mice ( Fig . 5B ) . Relative to expression during human UTI , fimA was upregulated 660- and 640-fold in the murine bladder by strains AL151 and AL371 , respectively . These data , which reflect the change in relative expression rank between murine and human infection ( Fig . 3 ) , indicate not only that strains AL151 and AL371 are capable of expressing type 1 fimbriae in vitro , but that they robustly express fimA during murine UTI . Thus , it appears that these E . coli strains had downregulated type 1 fimbriae in the urine of women with cystitis .
Here we report , for the first time , the transcriptional profile for any pathogenic E . coli following a naturally-occurring human infection . This study represents the largest number of subjects for any human transcriptome study and includes the largest number of bacterial strains studied in such experiments . Our data suggest that E . coli had access to abundant carbon sources prior to expulsion from the human bladder and replicates rapidly , expressing genes involved in nitrogen assimilation , iron acquisition , and virulence , while variably expressing or downregulating at least one major adhesin as assessed in voided urine at the time of sample collection . Although the women sampled have a history of recurrent UTI , our data suggest that the E . coli strains measured in this study represent UPEC strains with full pathogenic potential . All strains were able to colonize a mouse model of ascending UTI , unlike fecal-commensal E . coli , and genotype analysis classified them with normal cystitis strains ( P . Vigil and H . Mobley , in preparation ) . All patients in the present study had pyuria , indicating the presence of a robust inflammatory response , which is generally absent in patients with asymptomatic bacteriuria [57] . Nonetheless , it is important to note that the data presented here indeed represent a limited sampling of patients and future studies should expand this analysis to a larger population . Iron acquisition systems were the most highly expressed virulence determinants across all eight patient isolates in vivo . In addition to bacteria isolated from urine , we and other groups have observed expression of outer membrane iron receptors by bladder cell-associated UPEC , both in vitro [58] and in vivo [51] . Iron acquisition is required for urinary tract colonization [25] and UPEC isolates produce siderophores that are not synthesized by most non-pathogenic fecal E . coli strains [59] . Consequently , outer membrane iron receptors have been examined as targets of a vaccine to protect against E . coli UTI [60] , [61] . Data presented here indicate that at least one protective antigen , Hma , is expressed by at least half of E . coli populations during expulsion from the human urinary tract . Of note , the antigen that generated the highest IgA titer , IreA , was not expressed by any patient isolate examined , while the non-protective antigen ChuA was among the most highly expressed genes in all strains . It is interesting to speculate that pressure from the host adaptive immune response may represent negative selection against these genes and/or their regulatory elements . Despite the iron- and nitrogen-limiting conditions within the human urinary tract , our data support a model of robust UPEC replication during infection . When rapid growth and excess reducing equivalents ( NADH and FADH2 ) impose a limitation on the oxidative-dependent TCA cycle , acetate can be excreted to maintain redox homeostasis and recycle coenzyme A [49] . Recently , Welch and colleagues demonstrated that UPEC is better adapted to acetogenic growth than E . coli K12 and showed that mutants defective in acetate dissimilation ( pta and ackA ) had reduced fitness during murine UTI , while a mutant defective in acetate assimilation ( acs ) did not [47] . Our data support these findings and suggest that UPEC is undergoing similar growth and metabolism during human cystitis . Furthermore , this implies that acetyl-phosphate , which accumulates during acetogenic growth and can act as an intracellular signal , could play a role in UPEC pathogenesis during UTI in women . While we indeed observed differential expression of a number of metabolic genes , flux through these pathways is often regulated posttranscriptionally by enzyme activity and allosteric mechanisms [62] , [63] , so it is not surprising that a complete metabolic profile cannot be detailed solely from transcriptional data . Our data suggest that growth of E . coli in the human urinary tract is similar to its replication in a chemostat culture . Urine has been described as a mixture of small peptides and amino acids [64] and urine production by the kidneys assures that this medium is continuously replenished . Thus , in contrast to culture in urine ex vivo , E . coli in the human urinary tract likely has constant access to easily assimilable carbon sources . These results also imply that , although nitrogen and iron are limiting in this environment , E . coli acquires adequate quantities of these elements for robust replication . Whether E . coli present in voided urine accurately represent the physiological state of the bacteria attached to and within the bladder mucosa is unclear [65] . Because the majority of UTI pathogenesis occurs on the bladder epithelium , the critical contribution of adherent bacteria is apparent . In contrast , the pathogenic contribution of luminal E . coli , which are diagnostic for UTI , is largely undefined . While several groups have measured global gene expression by various E . coli strains in urine from infected mice [3] , [4] and humans [35] , the transcriptome of bladder-associated UPEC has not yet been described and is obviously not feasible in human patients . However , genes identified as highly expressed or upregulated in the urine of infected mice , such as type 1 fimbriae and iron acquisition systems [4] , frequently have roles in colonization [25] , [28] , [66] , [67] , suggesting that these genes are expressed at some point during association with the murine bladder . Furthermore , qPCR analysis of laser-capture microdissected UPEC from within urothelial cells of infected mice showed increased expression of ferric iron acquisition genes , including the heme receptor chuA [51] . These data are consistent with our transcriptome studies of experimental [4] and natural UTI; both identified chuA as one of the most highly expressed genes in the urine of infected mice and cystitis patients . Taken together , these studies strongly suggest that , at least in mice , voided urine represents a reasonable estimate of virulence gene expression during cystitis . Surprisingly , none of the major adherence factors described for UPEC were appreciably expressed in bacteria in urine voided from the human bladder . Because surface structures like fimbriae are known to vary antigenically , sequence dissimilarities between the clinical isolates tested and the UPEC genome represented on the microarray likely contributed at least partially to the low fimbrial detection . Indeed , for type 1 fimbriae , sequence divergence appeared to account for the low detection of the major structural subunit gene fimA , as transcript could not be detected for any isolate by microarray , while strains AL231 and AL241 were shown to express this gene by qPCR . However , expression of the remaining fim locus was indeed detected for these two patient isolates ( AL231 and AL241 ) , indicating that these genes may be more conserved . Similarly , at least four patient isolates encode the major P fimbrial subunit papA_2 ( AL151 , AL241 , AL361 , AL371 ) , but neither microarray nor qPCR could detect papA_2 transcript in the corresponding in vivo samples . Although not critical for virulence in a murine model of infection [68] , P fimbriae have long been associated with pyelonephritis in humans [37] . Indeed , E . coli that react with anti-P fimbrial antibodies can be isolated from the urine of patients with UTI [69] , [70] , [71] . The fact that P fimbrial gene expression was not detected in our patient isolates may be due to temporal or localized expression of these genes , or a result of our focus on patients with cystitis , rather than pyelonephritis . Data from previous studies have implied expression of type 1 fimbriae by a small or variable subset of E . coli in human urine . Indirect immunofluorescence of bacteria present in urine of patients with acute UTI has yielded varied results with respect to type 1 fimbrial detection . Several studies identified type 1 fimbriate cells in less than 38% [70] or 45% [72] of urine samples , but nearly in 100% of the same isolates following in vitro culture , while another group observed type 1 fimbriate cells in 76% of urine specimens [69] . Experiments from our laboratory quantified the type 1 fimbrial invertible element switch orientation in a bacterial population collected directly from the urine of 11 women with E . coli UTI . In that study , the switch was primarily in the “off” position within this population; for all 11 cases , bacteria in patient urine averaged only 4% “on” [73] . While there appears to be some variation among patient populations , these findings are overall consistent with our observation that only 25% of E . coli isolates expressed type 1 fimbrial genes in urine collected from cystitis patients . Several models could account for our finding that the majority of E . coli are not transcriptionally active for type 1 fimbriae in urine collected from cystitis patients . First , it is possible that E . coli present in voided urine represent the nonadherent “losing” fraction of the population that is expelled from the bladder . However , the samples used in our microarray were not processed to remove exfoliated epithelial cells , so it is likely that both planktonic and adherent bacteria were present to some extent and that neither population significantly expressed type 1 fimbriae . Moreover , measurement of the UPEC transcriptome during murine UTI also relied on the collection of expelled urine from infected animals and those data identified fimA as the fourth most highly in vivo-expressed gene . Nonetheless , adhesin gene expression differences between adherent and planktonic E . coli during human infection likely contributed to our results and should be further examined in future studies . The duration of infection likely also contributed at least somewhat to our variable detection of fimbriae expression . Urine was collected from mice infected with strains AL151 and AL371 from 6–48 hpi ( Fig . 5 ) , while it is unknown how long the women in this study were colonized at the time of sample collection . Although it is well-established that type 1 fimbriae are critical for the establishment of infection in mice [28] , [74] , their role during bacterial persistence has not been characterized . Consequently , the human urine samples collected in this study may have represented later stages of UTI , during which type 1 fimbrial genes may not be expressed or after fimbriated cells had been cleared by the immune system . In a mouse model of UTI , the orientation of the fim invertible element varied throughout the course of infection and by strain , with cystitis strains generally maintaining their IEs in the phase-on position throughout the infection ( up to 96 hpi ) [75] . Similarly , expression of type 1 fimbriae was shown to be required for UPEC fitness subsequent to the initial attachment/invasion event in a murine model of intracellular replication [76] . Furthermore , transcriptome analysis of UPEC during murine UTI analyzed urine collected up to 10 days post-infection ( with a reinfection at day 6 ) and still observed a high level of fim expression [4] , so it is unclear whether infection duration alone can explain our results . Nevertheless , future studies should attempt to correlate UTI symptom duration with type 1 fimbrial gene expression of E . coli collected from human urine . Given the abundance of data from our and other laboratories demonstrating the importance of type 1 fimbriae for UTI [27] , [28] , [29] , [77] and positive selection for fimH among UTI isolates [16] , a likely explanation for our findings is that expression of these genes may be a transient or regulated event during human infection . Analogous to flagellin , which is tightly regulated and only maximally expressed during ascension to the kidneys [19] , fim expression might be temporally or spatially controlled . We may speculate that , while phase-on bacteria would be primed to adhere to and invade the bladder epithelium , perhaps switching to the phase-off orientation allows dispersal or immune avoidance . Thus , future delineation of the molecular basis for the apparent variable type 1 fimbriae expression detected in our samples , as well as distinction between global gene expression in planktonic versus adherent bacteria in urine voided during human UTI will be necessary . Differences in urinary tract environments among patients and between mammalian hosts are also expected to account for some of the variable expression patterns observed in this study . Diet , hydration , and genetic factors all influence urine composition , urinary tract physiology and , most likely , gene expression by colonizing bacteria . For example , amino acids , temperature [78] , sialic acid [79] , oxygenation [80] , pH and osmolarity [81] are known to affect the orientation of the invertible element region and thus , fimbrial expression . As the present study represents an initial investigation of UPEC gene expression during human UTI , further analysis of additional patient samples will be needed to more completely assess potential correlations between urine chemistry and UPEC gene expression in urine from patients with UTI . The data presented in this study provide the first insights into pathogenic E . coli gene expression within the human host . Our findings are generally consistent with data generated using murine models and support the current model of UPEC pathogenesis . In urine from women with cystitis , E . coli express metabolic genes consistent with rapid replication and acetate excretion , actively scavenge iron , express known virulence genes , and may modulate expression of genes involved in motility and adherence . Continued investigation of UPEC gene expression in the urine of UTI patients will contribute both to our understanding of UPEC pathogenesis and to the development of effective UTI therapies .
Non-pregnant women over the age of 18 years with symptoms indicative of a UTI were invited to participate in our study by A . L . L . or G . J . F . Written consent was obtained from all subjects prior to enrollment and patient samples were assigned arbitrary identification based on the order of enrollment in our study . All human subject protocols were approved by the Institutional Review Boards of the University of Michigan Medical School ( HUM00011155 ) . All animal procedures were conducted in accordance with the guidelines of the University Committee on Use and Care of Animals at the University of Michigan Medical School and following protocols approved by UCUCA . Clinical E . coli isolates were cultured from the urine of women with suspected UTIs using standard methods [82] . Antimicrobial susceptibility testing was performed on all clinical E . coli isolates cultured from the urine of the women participating in our study by the Clinical Microbiology Laboratory at the University of Michigan Health System using the VITEK 2 system ( bioMerieux , Durham , NC ) . Clinical isolates were serotyped by the E . coli Reference Center at the Pennsylvania State University using antisera against O1-O181 ( except O31 , O47 , O72 , O93 , O94 , and O121 , which were not designated ) and PCR-restriction fragment length polymorphism analysis of the fliC gene . E . coli CFT073 was isolated from the blood and urine of a patient with acute pyelonephritis and E . coli EFC4 was isolated from the feces of a healthy woman with no history of a UTI or antibiotic use in the previous six months [56] . E . coli K12 is the prototypical commensal strain , MG1655 [83] . CFT073 type 1 fimbriae phase locked-on ( L-ON ) and locked-off ( L-OFF ) mutants were constructed as previously described by our laboratory [84] . Strains were routinely cultured in Luria broth ( 10 g/L tryptone , 5 g/L yeast extract , 0 . 5 g/L NaCl ) at 37°C with aeration , unless otherwise noted . Fresh mid-stream urine was collected from consenting women with presumptive bacteriuria attending the University of Michigan Urology clinic . A diagnosis of presumptive bacteriuria was made based on symptoms of urgency and frequency and/or a history of previous UTI . Volumes collected ranged from 28 to 187 ml , with a median volume of 70 ml ( average = 78 . 8 ml ) . Urine was collected from 34 women in order to obtain 10 E . coli-positive samples that were suitable for our study . Of these , two samples contained multiple E . coli strains and were not analyzed further . For the eight patients from whom single strains of E . coli were isolated and studied in this report , no patient was catheterized . Seven of eight patients reported a previous UTI . Two patients were taking one antibiotic ( ciprofloxacin or nitrofurantoin ) ; however , each respective E . coli strain was resistant to that antibiotic . Collected urine was immediately tested by urinalysis and analyzed by wet-mount microscopy for the presence of bacteria . Specimens positive for leukocyte esterase and/or nitrites , and/or those containing visible bacteria by microscopy were immediately stabilized ( within 10 min of sample collection ) by the addition of 2 volumes of RNAprotect ( Qiagen ) and incubated at 25°C for at least 10 min . Stabilized urine specimens were collected by centrifugation ( 3000×g , 30 min , 25°C ) and stabilized bacterial pellets were stored at −80°C for up to four weeks . Upon receipt of the clinical culture and sensitivity results , the E . coli-positive samples were processed for RNA isolation using the RNeasy Mini system ( Qiagen ) according to the manufacturer's instructions . DNA was removed from the preparation using TURBO DNase ( Ambion ) and , where necessary , RNA samples were concentrated using MinElute columns ( Qiagen ) . It is important to note that samples likely also contained human RNA , which was not quantified or removed . Clinical E . coli isolates were cultured overnight in LB , washed twice in pooled , filter-sterilized human urine ( pooled from 5 healthy donors ) and adjusted to OD600 = 4 . 0 . Standardized bacterial suspension was inoculated 1∶100 into 25 ml human urine ( starting OD600 = 0 . 004 ) and cultured statically at 37°C until OD600 = 0 . 2±0 . 02 . Culture aliquots ( 5 ml ) were stabilized with 10 ml RNAprotect and total RNA was isolated using the RNeasy Mini procedure described above . All RNA and cDNA preparations were analyzed using the Agilent 2100 Bioanalyzer ( Agilent Technologies ) to verify sample quality and integrity . Each sample met the criteria A260/A280≥1 . 7 and A260/A230≥1 . 5 . Concentrations of total RNA and cDNA samples were determined using a NanoDrop ND-1000 spectrophotometer ( Thermo Scientific ) . cDNA was synthesized from total RNA isolated using the Superscript Double-Stranded cDNA Synthesis system ( Invitrogen ) according to the manufacturer's instructions . The only modifications to the protocol were an increase in random primer concentration ( 3 μg ) and extension of the reverse transcriptase reaction ( 42°C , 90–120 min ) . cDNA was labeled and hybridized by Roche NimbleGen ( Madison , WI ) according to their standard protocols . Briefly , cDNA was labeled with Cy3 using the One-Color DNA Labeling protocol ( Roche NimbleGen ) and 15 μg labeled cDNA was prepared for each sample ( 10 in vivo samples and 12 in vitro samples , each in triplicate microarrays ) . Following hybridization , microarrays were washed and scanned using a GenePix 4000B Scanner ( Axon Instruments ) . Data were extracted and analyzed using the Roche NimbleScan software , which normalizes expression data using quantile normalization [85] and generates gene calls using the Robust Multichip Average algorithm [86] . The E . coli CFT073 microarray ( Roche NimbleGen ) contains 14 60-mer perfect match probes ( no mismatch ) for each of the 5379 open reading frames in the annotated CFT073 genome [14] , as well as random probes with similar G+C content . Each array value for the 5379 genes was derived from the automated normalization of 5 replicates of probes printed on each slide . The expression value for each potential gene was obtained by hybridizing triplicate samples to the E . coli CFT073 microarray . Biological replicates were not performed on the in vivo specimens as patients commenced antibiotic therapy following consultation with G . J . F . and the opportunity to collect multiple urine specimens was not possible , therefore in vivo microarrays represent technical replicates . The median value of the three replicates was obtained for each ORF and was compared to the median value of the random probes on each chip . The median value of the random probes on each array was used as a correction factor for the remaining signal on the chip , normalizing the hybridization and RNA quality of each preparation . The resulting absolute intensity values were transformed into log2 values and subsequent analysis utilized these log transformed normalized values to determine the potential expression for any given gene represented on the array . The gene was considered to be expressed if the intensity was four-fold above the value of the randomized control values . It is important to note that , as these isolates are uncharacterized , a lack of hybridization does not always indicate that a gene is not expressed , only that under the conditions examined there was not sufficient signal to indicate expression . Gene absence or genetic divergence between gene sequences of the isolates and gene sequences of the reference strain , E . coli CFT073 may also explain poor hybridization . Gene expression heat maps were generated using TreeView 1 . 60 . Microarray data have been deposited in NCBI Gene Expression Omnibus [87] and are accessible through GEO Series accession number GSE24478 ( http://www . ncbi . nlm . nih . gov/geo/ ) . The orientation of the fim invertible element was determined as described [73] using the primers listed in Table 5 . Clinical isolates and strain CFT073 were inoculated into 5 ml LB , incubated statically at 37°C for 48 h , passaged 1∶100 into fresh medium , and incubated at 37°C for an additional 48 h . Aerated cultures were similarly inoculated and incubated at 37°C for ∼4 h . All cultures were adjusted to OD600 = 1 . 0 and 500 µl was centrifuged ( 10 , 000×g , 1 min ) for western blotting ( see below ) . Standardized culture ( 50 µl ) was added to 50 µl water and boiled 10 min . PCR was performed to amplify the invertible element and 2 µl of crude lysate as template . PCR product was digested with SnaBI and separated on a 2% agarose gel . The fimA gene was cloned into the commercially-available expression vector pBAD-myc-HisA ( Invitrogen ) in-frame with a C-terminal His6 tag . Expression was induced by addition of L-arabinose to 100 µM and recombinant protein was isolated on nickel-nitriloacetic acid-agarose columns ( Qiagen ) . Antibodies were raised in rabbits against recombinant FimA-His6 excised from SDS-PAGE gels by Rockland Immunochemicals , Inc . ( Gilbertsville , PA ) . Bacterial pellets collected as described above were resuspended 100 µl acidified water ( pH 1 . 8 ) and boiled for 10 min . Lysate was mixed with 20 µl 6× SDS-PAGE loading buffer , neutralized with 1 N NaOH , and separated on 15% SDS-PAGE gels . Proteins were transferred to PVDF membrane and blocked with 5% milk in TBS +0 . 01% Tween-20 . FimA was detected with rabbit anti-FimA ( 1∶2000 ) , followed by anti-rabbit-HRP ( 1∶25 , 000 ) and ECL Plus detection reagents ( GE Healthcare ) . Strains were passaged statically or cultured with aeration as described above and adjusted to OD600 = 0 . 8 . One ml of culture was pelleted ( 2500×g , 2 min , 25°C ) and resuspended in 200 µl PBS and serial dilutions of this bacterial suspension ( 25 µl ) were added to the wells of a microtiter plate . A 3% suspension of PBS-washed guinea pig erythrocytes ( v/v ) was prepared in PBS on ice and 25 µl added to each well . For testing mannose sensitivity , erythrocyte suspension mixed with α-methyl mannoside ( Sigma ) at 1 mg/ml was added to wells containing undiluted bacterial suspension . Plates were gently rocked and incubated at 25°C for 30–45 min . Hemagglutination titer was defined as the highest dilution of bacterial suspension that yielded a positive reaction . Six- to eight-week female CBA/J mice were transurethrally inoculated as previously described [34] . Clinical isolates were cultured overnight in LB , collected by centrifugation ( 3000×g , 30 min , 25°C ) and resuspended in PBS to 2×109 CFU/ml . Bacterial suspension ( 50 µl/mouse ) was delivered directly into the bladders of anesthetized mice via a sterile 0 . 28 mm inner diameter polyethylene catheter connected to an infusion pump ( Harvard Apparatus ) , with a total inoculum of 1×108 CFU/mouse . At two-hour intervals beginning at 5 h post-inoculation , urine was collected and pooled from each cage of animals ( 5 mice ) . Immediately after collection , cold 5% phenol-ethanol stop solution was added , samples centrifuged ( 10 , 000×g , 1 min , 4°C ) , and pellets stored at −80°C for RNA isolation . For CFU determination , mice were sacrificed at 48 hpi and urinary tract organs homogenized with a GLH homogenizer ( Omni International ) in 3 ml PBS . Homogenate was plated on LB agar using an Autoplate 4000 spiral plater ( Spiral Biotech ) and colonies enumerated with a QCount plate reader ( Spiral Biotech ) . Significance was determined using the two-tailed Mann-Whitney test . For microarray validation , real-time qPCR was performed with cDNA isolated above for microarray hybridization . Reactions were conducted in a Mx300P instrument ( Stratagene ) , using 30 ng cDNA template , 0 . 1 µM primers ( Table 1 ) , and Brilliant SYBR Green reagents ( Stratagene ) . Data were normalized to gapA and analyzed with MxPro 4 . 0 software ( Stratagene ) . For fimA measurement at room temperature , CFT073 wildtype and L-ON were cultured in 70 ml pooled , filter-sterilized human urine for 6 h at 37°C . Cultures were decanted into 120 ml urine collection cups with lids and incubated at room temperature for 60 min . At intervals , 1 ml culture aliquots were mixed with 125 µl cold 5% phenol-ethanol stop solution , centrifuged ( 10 , 000×g , 1 min , 4°C ) , and stabilized pellets stored at −80°C . Thawed pellets were resuspended in 100 µl 1 mg/ml lysozyme in TE and RNA isolated using the RNeasy protocol as described above . cDNA was synthesized using SuperScriptII First-Strand Synthesis reagents according to the manufacturer's instructions and qPCR was performed as described .
|
Animal models of infection have been used extensively to study how bacteria and other pathogens cause disease . These models provide valuable information and have led to the development of numerous vaccines and antimicrobial therapies . However , it is important to recognize how these animal models compare to human infection and to understand how bacteria cause disease in humans . This study measured gene expression in E . coli , a major cause of urinary tract infection , immediately after collection from the urine of women with bladder infection symptoms . The data showed that E . coli gene expression in the urine from women with urinary tract infection was very often similar to what had been observed in a mouse model , but these studies also identified several potentially important differences , including a bacterial surface structure that is necessary for infection in mice but not detected in most E . coli in human urine . Although more precise measurements are still needed , these findings contribute to our understanding of bacterial infection in humans and will help in the development of vaccines and treatments for urinary tract infection .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"infectious",
"diseases/bacterial",
"infections",
"infectious",
"diseases/urological",
"infections",
"microbiology/cellular",
"microbiology",
"and",
"pathogenesis"
] |
2010
|
Escherichia coli Global Gene Expression in Urine from Women with Urinary Tract Infection
|
Leishmania amastigotes manipulate the activity of macrophages to favor their own success . However , very little is known about the role of innate recognition and signaling triggered by amastigotes in this host-parasite interaction . In this work we developed a new infection model in adult Drosophila to take advantage of its superior genetic resources to identify novel host factors limiting Leishmania amazonensis infection . The model is based on the capacity of macrophage-like cells , plasmatocytes , to phagocytose and control the proliferation of parasites injected into adult flies . Using this model , we screened a collection of RNAi-expressing flies for anti-Leishmania defense factors . Notably , we found three CD36-like scavenger receptors that were important for defending against Leishmania infection . Mechanistic studies in mouse macrophages showed that CD36 accumulates specifically at sites where the parasite contacts the parasitophorous vacuole membrane . Furthermore , CD36-deficient macrophages were defective in the formation of the large parasitophorous vacuole typical of L . amazonensis infection , a phenotype caused by inefficient fusion with late endosomes and/or lysosomes . These data identify an unprecedented role for CD36 in the biogenesis of the parasitophorous vacuole and further highlight the utility of Drosophila as a model system for dissecting innate immune responses to infection .
Leishmaniasis affects 12 million people in over 98 tropical and subtropical countries or territories [1 , 2] . The disease is caused by protozoan parasites of the genus Leishmania , which are transmitted by sandflies . In humans , an infection starts with the interaction between promastigote forms of the parasite , delivered by a sandfly bite , and host phagocytes . In the case of L . major infection , neutrophils in particular are recruited to the bite site and serve as the first host for these parasites , where they differentiate into amastigote forms ( 3 ) . However , this relationship is temporary because the infection induces apoptotic death of these neutrophils , which stimulates their phagocytosis by dendritic cells and macrophages . The delivery of amastigotes from apoptotic neutrophils to macrophages and dendritic cells as well as the capacity of neutrophils to reduce the recruitment of dendritic cells to the sandfly bite site have been shown to be an important step for the establishment of L . major infection in the vertebrate host [3–5] . In all types of leishmaniasis , infection is perpetuated in macrophages and the symptoms associated with leishmaniasis are promoted through sequential cycles of intracellular amastigote replication , lysis , and infection of naïve cells . Host cell receptors engaged during Leishmania infection modulate the innate and acquired immune responses . Multiple macrophage receptors are implicated in Leishmania recognition , making the study of individual components challenging . The Fcγ receptor was the first of these receptors to be studied in depth; Fcγ receptors bind to antibody-opsonized amastigotes to promote their internalization . This interaction favors infection by inducing the secretion of the anti-inflammatory cytokine IL-10 [6–10] . Similar to Fcγ receptors , complement receptor 3 increases the efficiency of infection and reduces the cellular response against promastigote forms [11 , 12] . Parasite recognition by the phosphatidylserine receptor and DC-SIGN are also implicated in down modulating cellular responses while fibronectin and mannose-fucose receptors are examples of receptors that bind to parasites and favor phagocytosis [13–17] . On the other hand , it has been shown that Toll-like receptors ( TLR ) are required to induce a protective cellular response observed in resistant mouse strains [18–21] . However , the inventory of host factors that are engaged during Leishmania infection is still incomplete , limiting our knowledge about host cell signaling pathways essential for leishmaniasis . Following receptor mediated phagocytosis , the Leishmania-containing phagosome rapidly matures into a phagolysosome-like organelle known as a parasitophorous vacuole ( PV ) . The PV size varies among Leishmania species: very large PVs that can harbor multiple amastigotes are typical with Leishmania mexicana complex ( L . amazonensis , L . mexicana , L . pifanoi , L . venezuelensis ) infections . On the other hand , small and tight fitting PVs , which accommodate single amastigotes and split when the parasite replicate , are observed with other Leishmania species , such as L . major . Yet the mechanisms involved in the maturation and maintenance of PVs are largely unknown . To date , studies have shown that the coordinated fusion of vesicles from both endocytic and secretory pathways , similar to classical phagolysosome maturation , is required for formation of these large PVs . In particular , the PV gradually acidifies and acquires markers of early endosomes ( Rab5 ) , late endosomes and lysosomes ( Rab7a , MHC class II , LAMP1 , LAMP2 , M6PR and hydrolases ) , as well as endoplasmic reticulum markers such as calnexin [22–28] . However , unlike the degradative pathway of classical phagosomes , where the content is degraded and the organelle is recycled , L . amazonensis PVs quickly enlarge and maintain this enlarged size throughout the life of the infected cell [29] . PV enlargement has been shown to be associated with the high fusogenicity with secondary lysosomes and , to a lesser extent , with the endoplasmic reticulum , however the relative contribution of these organelles for PV formation and the mechanisms that control their fusion to PVs remain unclear [28 , 30 , 31] . The customization of the PV by Leishmania is essential for the acquisition of nutrients and to avoid immune responses . In a series of experiments studying the role of the protein LYST/Beige , Wilson et al . ( 2008 ) demonstrated that large PVs are essential for L . amazonensis replication . Cells from mice mutant in LYST/Beige have oversized L . amazonensis PVs with higher parasite replication , while overexpression of LYST/Beige inhibited the expansion of PVs and blocked parasite proliferation . Furthermore , large PVs were shown to dilute the intravacuolar concentration of nitric oxide , lowering it to concentrations tolerated by the parasite . The association of PV size with parasite survival has also been related to the fusion of PVs with the endoplasmic reticulum , where interference with SNARE protein functions caused a discrete but significant decrease in PV size and parasite proliferation [28] . These studies highlight the importance of proper enlargement of the PV for L . amazonensis proliferation . Here we developed an experimental Drosophila model of Leishmania infection and used a forward genetic screen to identify novel factors that modulate parasite infection . With this approach , 6 scavenger receptors required for resistance to infection were identified , three of which shared homology with mammalian CD36 . We further analyzed the role of CD36 in mammalian models of L . amazonensis infection and found that CD36 is concentrated in the PV membrane juxtaposed to the posterior end of amastigotes . Moreover , the PV in CD36-/- mouse macrophages was reduced in size , as a consequence of reduced fusion of late endosome and/or lysosomes with the PV , and did not support amastigote replication . Collectively , these studies identify an essential role for CD36 in the maturation of the PV and Leishmania survival , and demonstrate that alternative models of infection , such as Drosophila , are advantageous for discovery of new host factors involved in the Leishmania-host interaction .
Given the important role of amastigotes in perpetuating Leishmania infection and promoting disease , we focused on analyzing the innate interactions between this parasite form and the immune system of the host . As Drosophila has proven to be an excellent model system to study such interactions [32] , we developed a Drosophila model of L . amazonensis infection , in which opsonin-free amastigotes were isolated from mouse bone marrow-derived macrophage ( BMDM ) cultures and used to infect adult flies by microinjection . This infection caused only mild lethality , with the death of approximately ~25% of flies between days 3 and 6 post-infection ( Fig 1 , compare black and blue curves ) . The robust survival of ~75% of the Leishmania-infected flies suggests the presence of defense mechanisms in Drosophila to control these parasites . To identify key defense pathways for host protection , we next infected mutant Drosophila that were deficient in known immune defense mechanisms , including humoral immune responses , melanization , or phagocytosis . The Drosophila humoral immune response plays a crucial role in the defense against bacterial and fungal infections through the production of a diverse set of antimicrobial peptides and other factors circulating in the hemolymph . Two NF-κB dependent innate immune pathways control this response , the Toll and Imd ( immune deficiency ) pathways . We infected flies mutant for either Dif or the receptors PGRP-LC/LE , which are essential components of the Toll or Imd pathways , respectively . These mutants had similar survival rates compared to WT flies ( Fig 1A and 1B ) suggesting that humoral immune signaling , and its ensuing antimicrobial peptide response , does not participate in the defense against Leishmania parasites . Melanization is classically described as a defense mechanism used by arthropods to encapsulate and kill some pathogens such as eggs of parasitoid wasps and the ookinetes of the malarial parasite Plasmodium berghei [33 , 34] . Melanization is also linked to the effective response against certain bacterial infections [35 , 36] . We examined the potential role of the melanization cascade in controlling Leishmania infection . Flies deficient in serine protease 7 ( CG3066 ) , which are unable to cleave and activate prophenoloxidase and fail to trigger the melanization cascade [37] , displayed normal resistance against Leishmania infection indicating that this pathway is also not critical for defense against Leishmania ( Fig 1C ) . In flies , approximately 95% of hemocytes are plasmatocytes , professional phagocytes responsible for detecting and clearing invading microbes as well as unwanted cells [38] . To evaluate whether plasmatocytes participate in Leishmania defense , we ablated the phagocytic ability of plasmatocytes by injecting polystyrene beads into the hemocoel [39] . Polystyrene beads were co-injected with L . amazonensis amastigotes and fly viability was observed for ten days ( Fig 1D ) . Bead-injected flies were significantly ( p<0 . 0001 ) more susceptible to Leishmania infection , with 72% lethality over the course of the 10-day study . This was confirmed using a second approach in which all hemocytes were genetically ablated with the targeted expression of the proapototic protein Bax in plasmatocytes and other blood cells , generating the “phagoless” strain [40] . Similar to the bead-injected animals , this fly strain also showed dramatically reduced survival ( p<0 . 0001 ) following amastigote infection ( Fig 1E ) . These results suggest that plasmatocytes and phagocytosis play a critical role in the control of L . amazonensis infection in Drosophila . We hypothesized that plasmatocytes control infection by killing parasites following their phagocytosis . The phagocytosis of parasites was confirmed by microscopic studies of GFP-expressing hemocytes from flies infected with dsRed-expressing L . amazonensis amastigotes . As expected , flies co-injected with polystyrene beads and amastigotes had plasmatocytes saturated with polystyrene beads but few intracellular parasites at 24 h ( Fig 2A ) ; at this same time point , flies injected with amastigotes but not beads had plasmatocytes loaded with numerous parasites ( Fig 2B ) . After 72 h of infection , plasmatocytes contained numerous round amastigotes as well as occasional elongated promastigote forms ( Fig 2C ) . In mammalian macrophages , L . amazonensis amastigotes proliferate within enlarged PVs , with multiple parasites per vacuole as they replicate . On the other hand , amastigotes in Drosophila plasmatocytes remain enclosed in tight fitting phagosomes which rarely contain more than one parasite ( Fig 2B and 2C ) . This suggests that amastigotes cannot effectively manipulate the Drosophila phagocytic machinery to create their preferred niche , and instead these hemocytes control the infection through phagocytosis and parasite killing . We next evaluated whether the high mortality of flies correlated with increased parasite proliferation . The amount of parasites surviving within the host was estimated by limiting dilution of individual flies . Bead-treated flies contained significantly more parasites than untreated flies ( Fig 2D ) . Additionally , while the control flies showed a trend of reduced parasite load over the course of 12 days of infection , the flies defective in phagocytosis had a parasite load that increased over three days post-infection and remained elevated until day 12 ( Fig 2D ) . These data are consistent with the high death rate observed between the 3rd and 6th day of infection of phagocytosis-deficient flies ( Fig 1D and 1E ) . In summary , these data demonstrate that phagocytic activity , and plasmatocytes in general , are critical for controlling parasite proliferation and protecting the fly from death . Given the importance of phagocytosis in controlling Leishmania infection , the Drosophila model was then used to screen for hemocytes genes required for anti-parasite defense . We analyzed a collection of flies carrying RNAi constructs targeting a curated set of potential phagocytic genes . Targeted factors included the core phagocytic machinery and hemocyte-specifying factors , such as Rac2 or FTZ-F1 , as well as a collection of 25 receptors linked to phagocytosis , including members of the Scavenger Receptors Class B ( i . e . , CD36-like ) , Class C ( an insect-specific group ) and Class F ( rich in EGF repeats such as Nimrods , Eater , Draper and Slow down ( Table 1 ) ) . The knockdown of these genes was targeted specifically to hemocytes using the Hml ( Δ ) -Gal4 driver . Consistent with the role of phagocytosis in the Drosophila defense against Leishmania infection , this approach identified several essential phagocytic components , including Rac2 , Coat Protein ( coatomer ) ß’ and the ftz transcription factor 1 ( Fig 3A–3C , Table 1 ) . In addition , multiple scavenger receptors were found to be important for defense against L . amazonensis infection: SR-CIII and SR-CIV , Nimrod C3 , and three CD36-like receptors ( or SR-Bs ) , namely CG10345 , CG31741 , and Croquemort ( Fig 3D–3J and Table 1 ) . Because CD36-deficient flies were more susceptible to Leishmania infection ( Fig 3 ) , we next investigated if this susceptibility was linked to an increase in parasite burden and defects in phagocytosis . While the parasite load of control flies did not increase during infection , a sharp increase in parasites was observed in flies expressing RNAi for CD36-like receptors ( Fig 4 ) . Next , to investigate the role of CD36 scavenger receptors in phagocytosis , the number of parasites associated with hemocytes was scored 1 h post-infection . Notably , hemocytes from RNA flies for Crq , CG31741 , and CG10345 contained significantly less intracellular parasites , compared to WT hemocytes ( Fig 4B ) . These data support our hypothesis that the expression of each of these three CD36-like scavenger receptors in Drosophila hemocytes is critical for control of Leishmania infection by phagocytosis . Next we evaluated which parasite stage is proliferating in the susceptible flies . At day one of infection all parasites are in the amastigote form , in all genotypes . However , in control flies by day three 15% of parasites are promastigotes and this increases to 20% by day eight . With knockdown of CG10345 or crq , promastigote levels significantly increased to 40% or 60% , respectively ( Fig 4C ) . However , total parasite load did not correlate with the degree of promastigote differentiation/replication . Given the results with knockdown of CD36 homologs in the Drosophila model , we investigated the role of this scavenger receptor in the interaction between L . amazonensis and mammalian cells . The localization of mCerulean3-tagged CD36 during amastigote infection of human 293T cells was analyzed . By one hour post-infection , CD36 was widely distributed on the cell membrane and co-localized with mCherry-Rab7a at numerous cytoplasmic vesicles and at the PV membrane ( Fig 5A ) . Notably , at 6 h post-infection , when the PV is expanding , CD36 concentrated at the PV membrane juxtaposed to the parasite point of contact ( Fig 5B ) . This suggests that localization of CD36 on the PV membrane is regulated in response to the amastigote . To further demonstrate the close association of CD36-positive PV membranes and parasites , we studied the association of CD36 with amastigotes harvested from mCherry-CD36 expressing 293T cells infected with GFP-expressing parasites . The intracellular parasites were isolated free of host cells 24 h after infection by mechanical disruption and centrifugation . These amastigotes retained a strong mCherry-CD36 signal at their posterior end ( Fig 5C ) , suggesting a persistent CD36-parasite interaction in infected cells . A time-lapse confocal micrograph of amastigote-infected mCherry-CD36 293T cells ( S1 Movie ) showed the dynamic fusion of the late endosomes and lysosome in cells infected for 6 h and analyzed after a 15 min pulse of pHrodo-Dextran . When endocytosed , pHrodo serves as a marker of late endosomes and lysosomes because it becomes highly fluorescent in the acidic environment of these organelles . PV regions juxtaposed to parasites and rich in CD36 were associated with numerous acidic ( pHrodo positive ) , mCerulean3-CD36 positive organelles . Although it was not possible to differentiate between vesicles fusing with PV from those simply moving out of the focus plane , the gradual decrease in the number of endocytic vesicles along with the increase of pHrodo signal into the PV is a clear indication of fusion . The intriguing recruitment and localization of CD36 on the PV of 293T cells , led us to examine the phenotype of amastigote infected CD36-/- mouse bone marrow derived macrophages ( BMDMs ) , a more relevant cell type for Leishmania studies . L . amazonensis parasites , as well as other members of the L . mexicana subgenus , invade macrophages by receptor-mediated phagocytosis and induce the formation of an enlarged PV in WT cells ( Fig 6 ) . We infected BMDMs derived from WT ( C57BL/6 ) or CD36-/- mice with a multiplicity of infection ( MOI ) of three parasites per macrophage and observed that the size of the PVs was similar between CD36-/- and WT at 8 h post-infection but by 24 h the PVs of WT macrophages were significantly larger ( Fig 6A and 6B ) . The difference peaked at 48 h when the area of PVs from WT macrophages reached 62 μm2 while the area of PVs from CD36-/- macrophages was only 16 μm2 . From 48 h to 72 h the size of PVs did not change significantly ( Fig 6B ) . At an MOI = 3 , PV expansion occurs slowly and this may contribute to the delay in the appearance of clear differences between WT and CD36-deficient BMDMs . Therefore , BMDMs were infected at an MOI = 10 , which induces a quicker expansion of the PV . Using this condition , WT cells had enlarged PVs by 3 h post infection , while the PVs in the CD36-deficient macrophages remained small ( Fig 6C and 6D ) , suggesting that CD36 is directly involved in the expansion and maintenance of L . amazonensis PVs . We next evaluated if parasites were able to establish a replicative niche in the small PV of CD36-/- macrophages . The percentage of infected cells and the average number of parasites per infected cell were similar in WT and CD36-/- macrophages at 4 h post-infection , indicating that CD36 is not essential for parasite phagocytosis ( Fig 7 ) . However , while the replication rate of the parasite was similar in CD36-/- and WT macrophages during the first 48 h of infection , by 72 h parasite proliferation in CD36-/- macrophages was significantly reduced compared to WT suggesting a key role for CD36 in amastigote proliferation ( Fig 7A ) . Wilson et al . ( 2008 ) reported that the large PV of L . amazonensis-infected cells dilutes the concentration and toxicity of nitric oxide ( NO ) inside the PV , thereby allowing parasite replication [41] . To evaluate whether the impaired proliferation of parasites in the small PV of CD36-/- macrophages was related to the NO toxicity , macrophages were treated with N-nitro-L-arginine methyl ester ( L-NAME ) , an inhibitor of NO synthase , prior to amastigote infection . Notably , the inhibitor rescued the number of parasites in CD36-/- macrophages at 72 h post-infection ( Fig 7A ) , confirming the role of NO in restraining parasite proliferation in the small PVs of CD36-/- macrophages . On the other hand , L-NAME had no effect on PV size ( Fig 7B ) and significantly reduced the NO levels of infected macrophages stimulated with IFN-γ ( Fig 7C ) . However without IFN-γ stimulation , the levels of NO remained low and the effect of L-NAME on this low level of NO was not significant ( Fig 7C ) . Therefore , L-NAME treatment likely protects parasites from the local NO production that is thought to occur at the PV surface [42] . Intriguingly , while local NO impaired parasite replication in the small PV of CD36-deficient macrophages , GFP-expressing parasites were observed for at least 4 days in these cells , suggesting that they could not mount a sterilizing anti-parasitic response . The production of reactive nitrogen species in IFN-γ/LPS-stimulated macrophages is important to the clearance of intracellular parasites [43] . Uninfected CD36-/- macrophages produced WT levels of NO following IFN-γ and LPS ( Fig 7C ) . Consistent with this finding , IFN-γ/LPS treatment 24 h post-infection caused a nearly complete clearance of intracellular parasites in both WT and CD36-/- macrophages by 96 h ( Fig 7D ) . Together , these data confirm that CD36-/- macrophages maintain IFN-γ/LPS-inducible anti-parasitic activity and argue that the large PV of L . amazonensis protects amastigotes from the local production of NO , but not against the high concentrations generated by fully activated macrophages . CD36 is involved in the transport of long chain fatty acids and cholesterol [44 , 45] . Therefore , we investigated if CD36-/- macrophages exhibited any defects in the transport of these lipids to the PV or to the parasites that might relate to the formation of PVs . The incorporation of BODIPY FL C12 and CholEsteryl BODIPY was measured by flow cytometry at 2 h of continuous presence of the probes in the culture . The incorporation for both probes was similar in uninfected WT and CD36-/- macrophages , and both probes were also similarly incorporated into L . amazonensis amastigotes within the PV of WT and CD36-/- macrophages ( S1 Fig ) , indicating that these lipids are trafficking normally in the absence of CD36 . In a previous study , Beige was shown to regulate the L . amazonensis PV size [41] . In particular , these authors concluded that the expression of Beige is triggered by Leishmania infection , as a defense mechanism that reduces the PV size and increases the intravacuolar NO concentration for more effective parasite killing . Beige mRNA levels were quantified by qPCR in WT and CD36-deficient macrophages after infection with L . amazonensis amastigotes . If Beige was related to the undersized PV in CD36-/- macrophages we would expect higher expression of this gene , however we did not detect significant differences compared to WT ( S2A Fig ) . CD36 has also been implicated as a co-receptor for TLR2/6 and TLR4/6 heterodimers , which are involved in triggering the inflammatory response following stimulation with Staphylococcus aureus [46] or oxidized low-density lipoprotein [47 , 48] . To determine if the small PVs in CD36-deficient macrophages were caused by an inefficient CD36-TLR signaling axis , the PV size was measured in immortalized macrophages ( iMO ) from mutant mice lacking the adaptor protein MyD88 or both MyD88 and TRIF . As expected , CD36-deficient iMOs had smaller PVs , but MyD88 single or MyD88/TRIF double knockout iMOs contained PVs with areas comparable to WT iMOs ( S2B Fig ) , demonstrating that the undersized PVs of CD36-deficient macrophages is not related to TLR signaling . To test whether overexpression of CD36 could rescue the undersized PV phenotype of CD36-/- macrophages infected with L . amazonensis , CD36-deficient iMOs were engineered to express mCherry-tagged CD36 and infected with L . amazonensis amastigotes . As shown in Fig 8A , CD36-/- iMOs exhibited small PVs , consistent with our observations in primary macrophages . The expression of mCherry-CD36 , via a stable transduction of a lentiviral expression vector , restored the PV size of amastigote-infected CD36-/- iMO to WT levels . While infection of 293T cells induced the aggregation of CD36 in the PV within a few hours of infection , mCherry-CD36 expressing iMOs exhibited much more rapid CD36 clustering . Immediately following amastigote contact with the iMO , CD36 was observed clustering on the plasma membrane at the amastigote contact site , as part of the phagocytic cup ( Fig 8B ) . These CD36 clusters were then incorporated , along with some of the plasma membrane , into the developing PV during amastigote entry ( Fig 8B ) . During the first 6 h of infection , the CD36 clusters were not associated with a specific pole of the parasite , but were always localized at the point of contact . Later , with the enlargement of the PV , the posterior pole of most parasites aligns to contact the PV membrane , reorienting the CD36 clusters to this region of the parasite ( Fig 8C ) . The attachment and polarization of amastigotes inside PV is typical for L . amazonensis [23 , 26 , 29] . We next asked whether the amastigote actively induces the recruitment of CD36 to the cell membrane surface by secretion of virulence factors or by passive receptor-ligand interaction . Formaldehyde-fixed amastigotes also induced CD36 clustering , demonstrating that molecules on the surface of the parasite are responsible for CD36 recruitment ( Fig 8D ) . However , the formaldehyde-fixed amastigotes were degraded by macrophages within 6 h of infection and the CD36 aggregates were dispersed . Promastigotes are the initial infective form of the parasite and are also internalized by macrophages . To test whether promastigotes induce CD36 recruitment , we infected iMOs expressing mCherry-CD36 . Notably , unlike amastigotes , promastigote cell contact and internalization did not recruit CD36 within the first 6 h of infection ( Fig 9A ) . However , starting at 6 h , PV containing promastigotes ( or differentiating amastigotes ) gradually acquired CD36 , which increased up to 24 h ( Fig 9 ) . Note , the promastigote PVs were still small and tight fitting , indicating a delay in PV enlargement compared to infections initiated with amastigotes . To further investigate the role of CD36 clustering in Leishmania infection , we studied CD36 dynamics in response to L . major , which proliferate in small single parasite PVs . Amastigotes were harvested from mouse footpad lesions and used immediately to infect iMOs expressing mCherry-CD36 . Confocal microscopy demonstrated that CD36 was not enriched in the small PVs of L . major at either 6 h or 24 h post-infection ( S4A and S4B Fig ) . The absence of CD36 clustering in L . major tight fitting PVs suggests that the receptor is not involved in this infection . However , to further evaluate the participation of CD36 in L . major proliferation , the parasite loads of WT and CD36-/- macrophages were monitored during a time course of infection . The number of parasites recovered in WT and CD36-/- macrophage cultures was similar throughout the infection ( S4C and S4D Fig ) . The initial parasite load at 3 h of infection was relatively high because both infective and non-infective parasites were recovered . The following 3 days , both WT and CD36-/- macrophages had low parasite levels because only a small proportion of infective promastigotes survived . Parasite proliferation was detected by 96 h post-infection in both genotypes , indicating that lack of CD36 does not impair L . major proliferation . CD36 has been reported to induce cell membrane fusion through binding to phosphatidylserine [49] . In this model , the interaction of CD36 from one cell with phosphatidylserine on the surface of another , promotes the fusion of macrophages and generates giant cells similar to those observed in granulomas . These results suggest that the small PV phenotype observed with L . amazonensis could be caused by CD36-mediated phosphatidylserine recruitment . If true , we would expect the enrichment of phosphatidylserine on the PV in a CD36-dependent manner . However , PS was not observed associated with the PV of WT or CD36-/- macrophages , as monitored by Annexin V staining ( S5 Fig ) . These results do not support the model of CD36-mediated recruitment of PS to the PV membrane . To study the maturation of the PV , which is an altered phagosome , we next characterized the acidification of PV in WT and CD36-/- macrophages . We monitored the acidification of PV during the first 2 h post-infection using live L . amazonensis amastigotes that were uniformly immunolabeled with antibodies conjugated to Oregon Green 488 ( pH-sensitive ) or Alexafluor 647 ( pH-stable ) . The fluorescence intensity of the infected macrophages was determined by flow cytometry . As shown in Fig 10A , CD36-/- and WT BMDMs similarly acidify the PV , which reaches a pH of 4 . 5 within 15 min post-infection and gradually stabilizes by 1 hour post-infection to an approximate pH of 4 . 0 . Note , the opsonized parasites probably engaged the macrophage Fc receptors during internalization , however , this interaction did not interfere with the small PV phenotype of CD36-/- macrophages ( S3 Fig ) . As PV acidification appeared normal in CD36-deficient cells , we also characterized endosome to PV fusion in Leishmania-infected BMDMs . Twenty four hours post-infection , macrophages were loaded with pHrodo Dextran and the amount of dye that accumulated in the PV was measured 30 min later . Notably , the total pHrodo fluorescence intensity per PV was higher in WT than in CD36-deficient macrophages , indicating that CD36 contributes to the fusion of PV with endocytic vesicles ( Fig 10B ) . This assay , however , does not discriminate which endocytic organelle delivers pHrodo Dextran to PVs . In particular , lysosomes are well known as an important source of material for PV enlargement [22 , 24 , 30 , 31 , 41] . Therefore , we hypothesized that the underdevelopment of PVs in CD36-/- macrophages could be caused by a dysfunction of lysosome biogenesis and/or fusion to the PV . To assess this , we measured the accumulation of the lysosomal markers LAMP1 and LAMP2 in the PV membrane of infected macrophages by immunostaining . Fluorescence microscopy imaging of WT macrophages showed that ~40% of PVs stained strongly for LAMP1/2 at 8 h post-infection , and this increased to ~80% by 72 h ( Fig 10C and 10D ) . In CD36-/- macrophages , the percentage of LAMP positive PVs was similar to WT at earlier time points ( 8 h and 24 h ) , whereas there was significantly lower association of LAMP proteins with PV by 48 h and 72 h post-infection ( Fig 10C and 10D ) . The lower concentration of LAMP1 and LAMP2 in the CD36-/- PVs could be caused by lower quantity and/or reduced fusogenicity of lysosomes . To investigate this possibility further , two approaches were used to quantify the lysosomal content . The quantification of total fluorescence intensity of LAMP1 and LAMP2 in the macrophages , and the measurement of the activity of the lysosomal protease cathepsin B in total lysates . On average , the total fluorescence from LAMP1 and LAMP2 staining was similar in CD36-/- and WT macrophages ( Fig 10E ) . Also , the cathepsin B activity was similar in lysates of uninfected CD36-/- and WT macrophages ( Fig 10F ) . Together , these assays indicate that lysosomal biogenesis was normal in CD36-deficient macrophages , and therefore the lower rate of lysosomal fusion is the most likely explanation for reduced LAMP staining and small PV size . In order to reinforce these findings , WT and CD36-/- BMDMs were stained with the lysotropic dye Lysotracker prior to and 24 h after infection . Cells were imaged by confocal microscopy and highly fluorescent organelles were scored through a complete Z-stack ( Fig 10G ) . As expected , the number of Lysotracker-positive organelles in WT macrophages decreased 24 h post-infection , indicative of lysosomal fusion to PVs . However , the number of these organelles did not change in infected CD36-/- macrophages ( Fig 10H ) indicating a defect in lysosomal-PV fusion in the absence of CD36 . While the CD36-deficient cells also showed a reduced overall level of lysotracker-positive vesicles , and this may contribute to the defect in PV enlargement , these vesicles were still detectable yet did not reduce after infection , as observed in WT macrophages . This strongly suggests that lower fusogenicity of late endolysosomal vesicles is the main cause of PV maturation dysfunction in the absence of CD36 . In fact , this reduced fusogenicity may contribute to the overall reduction in large endolysosomal compartments observed prior to infection .
In this work , we developed an in vivo L . amazonensis infection model using Drosophila melanogaster to exploit the genetic tools available in this system . Characterization of this Drosophila infection model revealed that phagocytosis plays a pivotal role in parasite resistance . The central role of phagocytic plasmatocytes in Leishmania defense enabled an in vivo screening approach , in which RNAi was expressed specifically in these macrophage-like cells to evaluate the role of different genes in cellular anti-parasitic defense . Using the Drosophila infection model in a small scale screen , we identified six scavenger receptors , from three different families , that are involved in the defense of flies against Leishmania , suggesting that mammalian scavenger receptors could be more important to Leishmania infection than previously realized . Importantly , three of the scavenger receptors identified in our Drosophila screen were CD36-like ( also known as the SR-B family ) . These Drosophila CD36s are critical for efficient phagocytosis of parasites and to control the infection . One of these CD36-like receptors , Crq was first identified as a plasmatocyte phagocytic receptor for apoptotic cells [50 , 51] , and was later linked to S . aureus recognition , which led to the discovery that mammalian CD36 is the co-receptor for the presentation of bacterial lipopeptides to TLR 2/6 [46] . The other two CD36-like receptors identified in our screen are uncharacterized . Our most striking finding was that in mammalian systems CD36 plays a key role in the innate immune response to L . amazonensis infection by promoting PV expansion and parasite proliferation . Interestingly , CD36 is intensely recruited to the macrophage membrane upon amastigote contact , and then internalized within the phagocytic cup . Within a few hours after entry , CD36 is clustered at the anchoring site of the parasite , suggesting that the recruitment and fusion of endosomal vesicles is induced by parasites at this site . In the absence of CD36 , the enlarged PV , typical of L . amazonensis infection , does not form . Although macrophages do not generate massive NO production upon amastigote infection [52 , 53] the local NO environment was enough to arrest parasite proliferation in the small PVs of CD36-/- macrophages , in agreement with previous work [41] . This implies that the toxicity of the local NO environment is diluted in large PVs , but high concentrations of NO produced by IFN-γ/LPS-activated macrophages is sufficient to kill parasites . The formation of similar clusters of CD36 upon contact with heat killed or fixed amastigotes indicates that CD36 aggregates upon binding to ligands found on the parasite surface , rather than through the action of some secreted virulence factors . Therefore , promastigotes probably do not recruit CD36 because they do not express CD36 ligands on their surface . Indeed promastigotes and amastigotes of Leishmania parasites have very different cell surface compositions . Promastigotes have a thick glycocalyx composed of lipophosphoglycan ( LPG ) , proteophosphoglycan , gp63 and glycophosphatidylinositol lipids . In contrast , the amastigote glycocalyx has a low abundance of LPG and is rich in glycoinositolphospholipids ( reviewed in [54] ) . In agreement with the hypothesis of differential CD36 ligand exposure of promastigotes and amastigotes of L . amazonensis , the accumulation of CD36 following promastigote infection started to appear around 12 h post-infection , when amastigotes begin to form inside the PV . In this situation , the delay in CD36 accrual may be caused by expression of LPG and contribute to the delayed PV enlargement often observed in infections started with promastigotes [55 , 56] . Collectively , the observation of the focal aggregation of CD36 in the large PV of L . amazonensis amastigotes , and not in the small PV of L . amazonensis promastigotes or L . major amastigotes is consistent with the finding that CD36 is essential for PV enlargement and maturation . Data presented here show that CD36 is critical for fusion of late endolysosomes with growing PVs . These data are in agreement with previous reports highlighting the importance of lysosomes for the enlargement of PVs observed in L . mexicana group infections [22 , 24 , 30 , 31 , 41] . While analysis of LAMP levels and cathepsin B activity were comparable between WT and CD36-/- macrophages , the total level of large lysotracker-positive organelles was decreased in CD36-/- macrophages . This apparent discrepancy might be associated with the methodology , which considered only a fraction of total lysosomes , the ones with a strong and large Lysotracker signal; and therefore did not account for the numerous smaller and dimmer lysosomes [29] . This decrease in larger lysosomal vesicles suggests a fundamental defect in vesicle fusion in the absence of CD36 . Other members of the scavenger receptor B class have been linked to the maturation of phagolysosomes . The best studied example is the mammalian LIMP-2 , which binds β-glucocerebrosidase at the endoplasmic reticulum and transports this enzyme into lysosomes [57 , 58] . In addition , two other Drosophila Class B-related scavenger receptors , Crq and Debris buster ( Dsb ) , were recently shown to be involved in the late stages of phagosome maturation necessary to degrade dendrite fragments phagocytosed by epithelial cells [59] . Thus , given the high degree of structural similarity amongst members of this class , the role of CD36 in maturation of PV is not unprecedented [60] . The underlying molecular mechanisms involved in the active fusion of PVs with endolysosomal vesicles and the link to CD36 requires further investigation . One possibility is that the CD36 aggregation observed on the L . amazonensis PV triggers CD36-signal transduction via receptor multimerization [61–64] or via interaction with other factors localized to the parasite anchoring site , such as MHC Class II , LAMP1 and LAMP2 [23 , 26 , 65] . Alternatively , CD36 may function as a channel to exchange lipids to and/or from the PV , including virulence factors that could increase fusogenicity of the PV . Such a channel has been demonstrated in the crystal structure solution of LIMP-2 , and modeled in other members of the CD36 family [60] . For SR-BI this channel has been related to the bidirectional transport of cholesterol ( esters ) [66–68] . Another hypothesis is that CD36 on the PV membrane mediates the interaction with anionic lipids present in the incoming vesicle and promotes the fusion of these two organelles . Although we did not observe enrichment of phosphatidylserine in the PV , as suggested for the formation of giant granuloma cells [49] , further studies will investigate if other lipids are involved in the CD36-dependent PV expansion . It is important to emphasize that our work is focused on the study of amastigote forms and to further investigate the role of CD36 in the infection initiated by promastigotes , future studies should consider the infection of neutrophils , which are the primary cell type infected by Leishmania promastigotes during natural infection by sandfly bites [3 , 69] . The neutrophilic delivery of amastigotes to macrophages was shown to be essential to establish the infection of L . major in the ear pinna of mice following a sand fly bite . Although the role of neutrophils in in vivo infection with parasites of the L . mexicana group is not completely understood , one interesting connection is that macrophage CD36 , a known receptor for apoptotic cells , could be used to phagocytose apoptotic infected neutrophils , enabling a Trojan horse infection . In conclusion , here we described the development of a novel Drosophila infection model of Leishmania which can be used to efficiently explore the innate immune pathways involved in interaction between phagocytes and Leishmania parasites . Studies in this model demonstrated that CD36-like receptors are essential for the fly to defend against Leishmania infection . In mammalian macrophages , we showed that L . amazonensis parasites exploit host CD36 function to favor their intracellular survival: amastigotes expose CD36 ligands to stimulate fusion with endolysosomal vesicles and promote PV enlargement , thereby reducing NO levels in the local environment and enhancing replication . On the other hand , promastigotes do not expose CD36 ligands and PV enlargement is delayed until these parasites differentiate into the intracellular adapted form , the amastigote . These findings open a new avenue of investigation to study the CD36 signaling pathways and cell biology involved in vesicular trafficking , phagosome maturation and host defense in leishmaniasis as well as in other diseases .
All experiments were conducted according to the guidelines of the American Association for Laboratory Animal Science and approved by the Institutional Animal Care and Use Committee at the University of Massachusetts Medical School ( Docket#: A-2056-13 ) . CD36 mutant mice were generated by targeted gene disruption in embryonic stem cells [70] . C57BL/6 and Balb/c mice were obtained from The Jackson Laboratory . All animals used in this work were 7 to 12-weeks-old and were maintained under pathogen-free conditions at the University of Massachusetts Medical School animal facilities . Femurs and tibia were dissected from mice and bone marrow was flushed with PBS using a 30G needle connected to a 10 mL syringe . The cells were cultivated in RPMI supplemented with 30% L929 cell-conditioned medium and 20% FBS and maintained in bacteriological petri dishes at 37°C and 5% CO2 [71] . The cultures were fed on day 3 and used on day 7 . Immortalized macrophages were generated using J2 recombinant retrovirus carrying v-myc and v-raf oncogenes as described in Halle [72] . MyD88-/- and TRIF-/- immortalized macrophages were kindly provided by Dr Egil Lien ( University of Massachusetts ) and were generated from mutant mice BMDMs [73 , 74] . GFP-transfected L . amazonensis ( MHOM/BR/1973/M2269 ) , and dsRed-transfected ( RAT/BA/LV78 ) [75] were generously donated by Dr . Silvia R . Uliana ( ICB-USP , Brazil ) and Dr . Kwang-Poo Chang ( Rosalind Franklin University of Medicine and Science ) , respectively . Leishmania major was of the MHOM/IL8/Friedlin strain . Promastigotes were cultivated in vitro in M199 medium supplemented with 10% FBS and 30 μg/mL Hygromycin B ( GFP transfected ) or 5 μg/mL of Tunicamycin ( dsRed transfected ) at 26°C . Promastigote forms were differentiated axenically to amastigote forms by transferring the parasites to 199 media supplemented with 0 . 25% glucose , 0 . 5% trypticase , 40 mM sodium succinate ( pH 5 . 4 ) and 20% FBS , at 1x10ˆ7 cells/mL at 32°C for 3 days . Axenic amastigotes were then used to infect Balb/c BMDM cultivated in 175 cm2 T-flasks ( 1 . 5x10ˆ7 cells/flask ) at a multiplicity of infection of 10 parasites/BMDM . One week after infection cells were harvested from T-flasks and BMDMs were disrupted in PBS at 4°C using a glass dounce tissue grinder with teflon rod followed by two centrifugations at 210g for 8 min to remove intact BMDMs and cell debris , and one at 675g for 12 min to harvest the amastigotes . The full length mouse CD36 cDNA ( NM_007643 . 4 ) and mCherry were inserted in the retroviral transfer plasmid pCX4 Puro . 293T cells were transfected with the pCX4 mCherry-CD36 , MLV gag-pol , and VSVG using GeneJuice transfection reagent ( EMD Millipore ) following the manufacturer’s recommendations . For the viral production , supernatants were refreshed 24 h and collected 48 h after transfection , filtered through a 0 . 45 μm pore filter and stored at -80°C until use . Immortalized macrophages were cultured in the presence of virus particles and 8 μg/ml Polybrene for 24 h , and then the transduced cells were selected with 3 μg/ml of puromycin for 4 days . Fly lines used in this work are listed in the S1 Table . Hml ( Δ ) GAL4 was used as a hemocyte-specific driver , expressed from the 1st instar larvae [40 , 76] . Phagoless flies were generated by crossing virgin hml ( Δ ) -GAL4 , UAS-eGFP female flies [31] with UAS-bax/CyO-actin-eGFP males . The progeny were maintained at 29°C after eclosion to optimize the function of the UAS/Gal4 system . Seven to 10 day old male flies were used in all experiments . A Nanoject ( Drumond ) equipped with a capillary needle was used for microinjections of 32 ƞL of a suspension of parasites and/or polystyrene beads in the abdomen . Polystyrene beads ( 0 . 2 μm diameter , FluoSpheres , Invitrogen F8805 ) were washed with PBS three times followed by centrifugation and suspended in PBS at concentration of 2% for injections . Forty thousand BMDM-derived amastigotes resuspended in PBS were injected per fly immediately after purification . We used at least 60 flies per sample for survival experiments that were monitored daily for at least 10 days . The survival curves of experimental flies were compared to flies injected with PBS and to control flies carrying the driver alone injected with the same preparation . The statistics of the survival curves were analyzed using Log-rank ( Mantel-Cox ) test . Susceptible flies were defined as those that reproducibly showed statistically significant decreases in survival with parasite but not with PBS injection . The relative amount of parasites in adult flies was determined by limiting dilution . Infected flies were quickly washed in ethanol and individually ground with a tissue glass-teflon dounce homogenizer in 5 mL of Schneider media supplemented with 10% FBS , and 50 U/mL of a penicillin and streptomycin solution . The homogenates were filtered through a 70 μm mesh cell strainer to remove debris , and 100 μL of homogenate was serially diluted 24 times by a factor of 2 in triplicates . The cultures were analyzed for the presence of live promastigotes in the wells after 7 days of culture at 27°C , a technique modified from the limiting dilution method [77] . Alternatively , the flies were gently ground in 40 μL of Schneider media using a plastic pestle and the cell suspension loaded in an improved Neubauer chamber to count the dsRed parasites using CellProfiler cell image analysis software [78] . The same sample was used to determine the percentage of promastigotes in the flies by visual inspection . hml ( Δ ) Gal4-GFP adult flies expressing GFP in hemocytes were injected with parasites and/or beads and dissected in 20 μL of Schneider media on a glass slide on ice to drain the hemolymph . Samples from 5 flies were pooled in a coverglass bottom petri dish and centrifuged for 3 min at 200g to settle down the hemocytes in the bottom of the plate . The cells were visualized using a Leica SP8 AOBS laser-scanning microscope equipped with a 63X objective . The parasite load in hemocytes was determined in RNAi fly lines by counting the number of dsRed parasites in each GFP-expressing hemocyte using a fluorescence microscope . Each sample was a pool of 3 flies and 4 samples were scored per strain at 1 h post-infection . Bone marrow derived macrophages were seeded ( 1x10ˆ5 cells/well ) in 24 well plates containing round 12 mm glass coverslips and allowed to adhere overnight at 37°C and 5% CO2 in RPMI 1640 medium supplemented with 10% FBS and 5% L929 cell conditioned medium . Cultures were infected with amastigotes for 2 h and the coverslips were washed 3 times with RPMI media and transferred to a new plate . At the indicated times infected BMDMs were fixed with 4% paraformaldehyde for 15 min and mounted over a glass slide for microscopic imaging in a fluorescence microscope . The parasite load was determined by counting the intracellular GFP or dsRed-expressing amastigotes in each cell by microsocopy . The borders of PVs that contained GFP expressing parasites were delineated manually using ImageJ software to calculate the PV area . 293T cells were cultured in coverglass bottom 35mm dishes ( Mattek Corporation ) , and transfected with mCerulean3-CD36-C-10 , mCherry-CD36-C-10 and/or mCherry-Rab7A , gift from Michael Davidson , Addgene plasmid # 55405 [79] , #55011 , #55127 using GeneJuice ( EMD Chemicals ) according to the manufacturer’s recommendations . Cells were infected with amastigotes between 24 h and 48 h after transfection . For time-lapse live cell imaging , cells were infected for 24 h and incubated with pHrodo Red Dextran , 10 , 000 MW ( Molecular Probes ) for 15 min , washed and images were taken at intervals of 3 s at 34°C using Leica SP8 AOBS laser-scanning microscope equipped with a 63X oil immersion objective . The imaging conditions were optimized to use the minimum laser power to reduce phototoxicity . To determine the parasite load of L . major in macrophages , the cultures were grown in 96 well plates , infected with metacyclic promastigotes for 3 h , then extensively washed to remove free parasites and cultured at 34°C . At indicated times post infection the media was removed and switched to Schneider media containing 10% FBS and cultured for 6 days at 25°C . These conditions induce macrophage death and release of parasites that proliferate in promastigote forms . At the end of 6 days in culture the promastigotes were counted using a hemacytometer to estimate the relative parasite load in each sample . Cells were cultivated in coverslips and nitric oxide synthase was inhibited by addition of N-nitro-L-arginine methyl ester ( L-NAME ) at 100 μM in the culture media for 1 h and washed prior to infection . For parasite killing assays , bone marrow derived macrophages were infected for 24 h and activated with 150 U/mL of IFNγ ( eBioscience ) and 50 ng/ml of ultra pure LPS ( Invivogen ) in the culture media . Infection was measured by counting the intracellular parasites and the percentage of infected cells . The fusion of endocytic vesicles with PV in infected macrophages was measured using a fluorescence-labeled dextran dye ( pHrodo red dextran , MW 10000 , Molecular Probes ) . This marker enters cells through endocytosis and increases fluorescence in low pH compartments like late endosomes and endosomes . Twenty four hours after infection , BMDM cultures were incubated with 50 μg/mL of pHrodo dye for 15 min , washed with PBS and incubated for 15 min in 20 mM Hepes buffer [pH = 7 . 4] containing 140 mM NaCl , 2 . 5 mM KCl , 1 . 8 mM CaCl2 , and 1 mM MgCl2 . The cultures were washed and the cells were imaged in a confocal microscope at the level of the parasite center for the next 20 min . The area of PV of the images was delineated manually and the intensity of fluorescence was measured using ImageJ . The pH of PVs was determined by dual fluorescence flow cytometry [80] . For the dual fluorescence staining , amastigotes were incubated on ice for 30 min with Balb/c infected mouse serum ( 1:1000 ) , washed once and immunostained with the secondary antibodies conjugated to Alexafluor 647 or Oregon green 488 ( Invitrogen ) for another 30 min on ice and then washed once before using for infections . BMDMs were seeded in non-tissue culture treated 24 well plates one day before infection . The cultures were incubated on ice for 10 min to inhibit phagocytosis , and then the labeled parasites were added at an MOI = 4 and centrifuged 300g for 3 min to increase interactions between the BMDM and parasites . The plates were immediately incubated at 37°C in a water bath for 15 , 30 , 60 or 120 min . To detach the cells from the plate , the media was replaced by calcium and magnesium free PBS , kept on ice for 10 min and pipetted up and down until most of the cells were in suspension . Cell fluorescence was measured by flow cytometry and the pH was determined by the ratio of fluorescence of Oregon Green 488 to Alexafluor 647 . Standard curves for each sample were prepared by equilibrating the cells in solutions with defined pH , 80mM potassium chloride , 30mM sodium chloride , 30 mM potassium phosphate , 5 . 5 mM glucose , 0 . 8 mM magnesium sulfate , 1 . 3 mM calcium chloride , and 20 μM Nigericin to equilibrate the pH of the phagosomes to the pH of the media [81] . Infected BMDMs were prepared as described above , and after fixation the cells were permeabilized with 0 . 05% Triton X-100 in PBS for 10 min , blocked with PBS 1% bovine serum albumin and stained with the monoclonal antibodies 1D4B ( LAMP-1 ) and ABL-93 ( LAMP2 ) from developmental studies hybridoma Bank ( Iowa City , IA ) overnight at 4°C followed by secondary antibody conjugated to Alexafluor 488 ( Invitrogen ) for 1 h at 37°C . The coverslips were washed and mounted with 50% glycerol on microscopic glass slides and imaged in a fluorescence microscope . For quantification assays we set the images to a high threshold and counted only PV that presented more than half of extension of the membrane enriched with LAMP1/2 . Uninfected cells or cells infected for 24 h were stained with 100ηM Lysotracker for 2 h at 34°C . The images were taken in the continuous presence of Lysotraker using a Leica SP8 AOBS laser-scanning microscope equipped with a 63X objective , in z-stacks of 0 . 5 μm to cover the entire cells . The number of late endocytic compartments was determined using the 3D image counter plugin in ImageJ , and the counter was adjusted to consider only objects with intense fluorescence to avoid counting false objects from the background signal , and areas less than 8 μm3 to avoid counting PVs . Cathepsin B activity assays were performed using the chromogenic substrate Z-RR-pNA ( Enzo Life Sciences ) . 200 μM of substrate was diluted in 50 mM sodium acetate ( pH = 5 ) , 2 . 5 mM EDTA , and 0 . 1% Triton X-100 . Cells were lysed in the assay buffer plus 1μM pepstatin A , 0 . 75μM aprotinin and 1mM PMSF ( 2x10ˆ7 cells/ml ) , and 10 μL of lysate was used per reaction in triplicate . The cleavage of substrate was monitored at 410 nm for 45 min at 15 min intervals and the amount of pNA released by Cathepsin B activity was determined using a pNA standard curve . Total RNA from BMDMs was isolated using the TRIzol reagent ( Invitrogen ) following the manufacturer’s recommendations . The RNA was then treated with DNase and re-extracted by phenol:chloroform method . cDNA was synthesized using iScript cDNA synthesis kit ( BioRad ) and quantitative PCR analysis was performed using SYBR Green ( BioRad ) . The specificity of amplification was assessed for each sample by melting curve analysis and relative quantification was performed using a standard curve with dilutions of a standard . Oligonucleotide primers 5’-AGCAGAAGGTGATAGACCAGAA-3’ and 5’CCCACACTTGGATCATCAATGC-3’ were used to amplify the Beige/LYST and 5’-TCAGTCAACGGGGGACATAAA-3’ and 5’-GGGGCTGTACTGCTTAACCAG-3’ to amplify the loading control standard cDNA HPRT1 [41] . The statistical analyses were performed using GraphPad Prism version 6 . 00 for Mac OS X , GraphPad Software , La Jolla California USA , www . graphpad . com . The tests and the criteria used for each comparison are reported in the Figure legends . To quantify the incorporation of lipids , C57BL/6 and CD36-/- macrophages were cultured for 2 h in the presence of the fluorescent fatty acid and cholesterol analogs BODIPY FL C12 and CholEsteryl BODIPY 542/563 C11 ( Molecular Probes ) ( 5 μM final concentration ) . The cells were harvested from the culture dishes and the incorporation of the probes was measured by flow cytometry . The localization of the lipids was analyzed by confocal microscopy in paraformaldehyde macrophages infected for 4 h and incubated for 2 h with the probes . Infected BMDMs were fixed with 4% formaldehyde in PBS , permeabilized with 0 . 05% Triton X-100 in PBS for 10 min , blocked with PBS 1% bovine serum albumin and stained with Annexin V FITC ( Apoptosis detection kit , eBiosciences ) . The coverslips were washed and mounted with 50% glycerol on microscopic glass slides and imaged in a confocal microscope .
|
Leishmaniasis is caused by Leishmania parasites and transmitted to humans by sandflies . After the establishment of infection , the intracellular parasite form , known as an amastigote , preferentially infects and replicates in macrophages , cells otherwise specialized for killing microbes . To overcome macrophage lethality , Leishmania possesses a sophisticated evasion strategy that sabotages macrophage defenses . The cell biology of Leishmania-macrophage interactions is not completely understood , because of the complexity of the host-parasite relationship and limited technical resources available in the classical mouse infection model . In this study we created a model of leishmaniasis in fruit flies , which have advantages of genetic tractability , low cost , and high conservation . By screening a collection of genetically modified flies , CD36-like receptors were identified as factors involved in the Leishmania-phagocyte interaction . Further testing in CD36-deficient mouse macrophages showed that they did not support parasite proliferation due to the inability of parasites to enlarge the parasitophorous vacuole , a strategy used to avoid toxicity of reactive nitrogen species . The participation of CD36 in the control of the parasitophorous vacuole , which is an altered phagosome , has further implications for diseases such as Alzheimer’s disease , atherosclerosis , and certain bacterial infections where CD36 is a known phagocytic receptor .
|
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"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
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"blood",
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"and",
"health",
"sciences",
"immune",
"cells",
"immunology",
"microbiology",
"parasitic",
"diseases",
"protozoan",
"life",
"cycles",
"parasitic",
"protozoans",
"developmental",
"biology",
"protozoans",
"leishmania",
"membrane",
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"promastigotes",
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"amastigotes",
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] |
2016
|
Leishmania amazonensis Engages CD36 to Drive Parasitophorous Vacuole Maturation
|
Despite mounting evidence that epigenetic abnormalities play a key role in cancer biology , their contributions to the malignant phenotype remain poorly understood . Here we studied genome-wide DNA methylation in normal B-cell populations and subtypes of B-cell non-Hodgkin lymphoma: follicular lymphoma and diffuse large B-cell lymphomas . These lymphomas display striking and progressive intra-tumor heterogeneity and also inter-patient heterogeneity in their cytosine methylation patterns . Epigenetic heterogeneity is initiated in normal germinal center B-cells , increases markedly with disease aggressiveness , and is associated with unfavorable clinical outcome . Moreover , patterns of abnormal methylation vary depending upon chromosomal regions , gene density and the status of neighboring genes . DNA methylation abnormalities arise via two distinct processes: i ) lymphomagenic transcriptional regulators perturb promoter DNA methylation in a target gene-specific manner , and ii ) aberrant epigenetic states tend to spread to neighboring promoters in the absence of CTCF insulator binding sites .
Follicular lymphomas ( FLs ) and diffuse large B-cell lymphomas ( DLBCLs ) are the most common non-Hodgkin lymphomas [1] . Follicular lymphomas represent a spectrum from low- to high-grade tumors and , while predominantly diagnosed as indolent tumors , progress to more aggressive lymphomas like DLBCL over the course of several years [2] . DLBCLs are high-grade tumors that are sub-classified based on gene expression profiling into a typically chemo-responsive germinal center B-like ( GCB ) subtype and a more refractory activated B-like ( ABC ) subtype ( Figure 1A ) [3] . Although FL and DLBCL have markedly distinct clinical phenotypes , they both originate from mature B-cells transiting the germinal center ( GC ) reaction . When resting naïve B-cells are activated by exposure to T-cell dependent antigens , they migrate within lymphoid follicles and initiate massive clonal expansion while simultaneously undergoing somatic hypermutation and class switch recombination . Genetic defects arising as a byproduct of this immunoglobulin affinity maturation process are believed to give rise to FLs and DLBCLs [4] . Consistent with this hypothesis , genomic resequencing studies identified a large number of mutations occurring in FL and DLBCL . While it is known that FLs accumulate new mutations as they progress , the underlying cause of the different phenotype of de novo FL and DLBCL , which share many of the same mutant alleles , remains unclear . Emerging data suggest that epigenetic gene regulation through cytosine methylation is perturbed in FLs and DLBCLs , yet very little is known about how aberrant DNA methylation contributes to the disease phenotype , the genomic features of epigenetic defects in these tumor types , and mechanisms through which these defects occur . Recently we demonstrated that DNA methylation patterning plays a key role in hematopoietic development [5] and that DNA methylation and expression signatures define molecular subtypes of diffuse large B-cell lymphomas [6] . Here , we hypothesized that direct comparison of DNA methylation patterning in normal B-cells , FLs and DLBCLs would provide clues about gene deregulation during lymphomagenesis and explain the nature of the different clinical behavior of these lymphoma subtypes .
We examined the DNA methylation profiles of normal naïve B-cells ( NBC , 8 samples ) , normal germinal center B-cells ( NGC , 10 samples ) , follicular lymphomas ( FL , 8 samples ) , germinal center B-like DLBCLs ( GCB , 39 samples ) , and activated B-like DLBCLs ( ABC , 18 samples ) ( Figure 1A , Methods and Text S1 , Module 1; ) using the HELP assay [7] and custom-designed NimbleGen microarrays with probesets representing >50 , 000 CpGs corresponding to regulatory regions of roughly 14 , 000 human genes . In the HELP assay , the normalized array signal intensity corresponds to the degree of methylation associated with each probeset ( Methods , [6] , [8] ) . For any given probeset , a positive or negative normalized signal intensity indicates that the respective CpGs are either unmethylated or methylated ( Figure S4 ) . In contrast , intermediate probeset signal intensity indicates that a fraction of cells within the sample are unmethylated while others are methylated , thus reflecting the heterogeneity of methylation . We performed technical validation for the HELP array and validated DNA methylation profiles of six DLBCL samples using orthogonal base-pair resolution quantitative bisulfite sequencing based assays: ERRBS and MassARRAY assays ( Text S1 , Module 1 and Figures S5 , S6 , S7 , S8 ) . Overall mapping of probesets according to their positions along the human chromosomes indicated that sites of hypo- and hyper-methylation were distributed across all chromosomes in both normal and lymphoma samples ( Text S1 , Module 1 , and Figure S9 ) . However , we noted a higher abundance of intermediate methylation states in lymphomas and hypothesized that epigenetic heterogeneity might contribute to the clinical features of the disease . In order to address this question we derived two parameters: Using these indicators for normal cell types , we observed a strong bimodal distribution of probeset intensities , indicating that the vast majority of gene promoter CpGs were predominantly either unmethylated or methylated within the cells of a sample , as represented by the two modes at positive or negative M-scores , respectively ( Figure 1B ) . This observation is consistent with previous studies noting the bimodality of DNA methylation distributions in normal tissues [7] , [9] . In contrast , the distribution of DNA methylation in lymphoma samples was significantly different from those of normal cells ( Figure 1B; Kolmogorov-Smirnov test between pairs of normal and lymphoma samples; FDR corrected p-value<2 . 2×10−16 ) . All lymphoma subtypes showed a significantly greater proportion of probes with an intermediate M-score , indicating increased intra-sample variation , and most notably , such variation increased progressively from FL to GCB to ABC DLBCLs . This intra-sample variation was not due to sample purity , which was high for both lymphoma and normal ( NBC , NGC; >90% purity ) samples as confirmed by flow cytometry , and was not accounted for by differences in cellularity among the samples [10] ( Text S1 , Module 1 ) . In order to prove that intra-sample variation is an inherent feature of neoplastic transformation , rather than a technical artifact or a result of a confounding biological factors , we performed analysis controlling for ( i ) copy number variations using SNP data ( Figure S1 ) , ( ii ) sample purity using % purity data ( Figure S3 ) , ( iii ) exclusion of low signal-to-noise ratio probes from the analysis ( Figure S4 ) , ( iv ) differences in mitotic rate using cell line data with known doubling times ( Figure S11; Table S2 ) , and ( v ) potential age differences between controls and DLBCL patients ( Figure S12 ) . Finally , we validated the observation of increasing intra-sample heterogeneity in DLBCLs using the MassARRAY and ERRBS orthogonal assays , which supported our findings ( Text S1 , Module 1 , and Figures S5 , S6 , S7 ) . Likewise , we then found that the IQR values , which represent inter-sample variation , were small in normal B-cell controls , but again progressively increased in FL and the GCB and ABC subtypes of DLBCL ( Figure 1C; Mann-Whitney test between pairs of normal and lymphoma tissues; FDR corrected p-value<2 . 2×10−16 ) . We also obtained consistent results using alternative approaches to profile methylation changes as well as an alternative definition of inter-sample variation ( Text S1 Module 1 , Figure S10 ) . Since higher-grade lymphomas are known to display genomic instability , we verified that the observed differences in methylation in lymphomas were not due to gain or loss of genomic material by controlling for copy number alterations using SNP data from the same patients ( Text S1 , Module 1 , and Figure S1 ) . Variability was also independent of whether the probes were localized in CpG islands or not ( Text S1 , Module 2; Figure S13 ) . We found that the promoter regions with high CpG density usually were more hypo-methylated than others , as observed using both HELP and ERRBS assays , but that the CpG density did not affect patterns of inter-sample variation ( Text S1 , Module 2; Figures S14 , S15 S16 ) . Notably , the probes with high intra-sample variation ( i . e . , M-scores near zero ) were also likely to have high inter-sample variation ( i . e . , high IQR ) in normal and lymphoma samples ( Figure 1D ) ; this finding is consistent with the identification of variable CpGs in solid tumors [11] . In summary , since FLs are diagnosed most often as indolent tumors while GCB and ABC DLBCLs have progressively worse prognosis , our findings suggest that the extent of intra-and inter-sample variation in DNA methylation increases with disease aggressiveness . Based on our cell line data ( Text S1 , Module 1 , and Figure S11 ) it is unlikely that the greater epigenetic heterogeneity in more aggressive tumors is a reflection of higher proliferative rates that lead to stochastic variation in the DNA methylation distribution . Alternatively , heterogeneity could be related to loss of function of specific epigenetic regulators that normally tightly control DNA methylation patterns . Either way , epigenetic diversity could foster the survival of subpopulations of lymphoma cells after exposure to cytotoxic drugs , thus contributing to the greater risk of relapse in ABC DLBCLs . We found that DNA methylation diversity initiates within NGC , which are more heterogeneous than NBCs ( Figure 1B–1D ) , which is consistent with recent findings [10] . All three lymphoma subtypes originate via different molecular and likely epigenetic mechanisms from a common precursor – germinal center B-cells . Each subtype is characterized by a different extent of epigenetic heterogeneity , which likely reflects different mechanisms of lymphomagenesis . Epigenetic diversity might then cooperate with somatic mutations in predisposing NGC towards malignant transformation . It is not known whether alterations in DNA methylation patterning are associated with clinical outcome in lymphomas . Using a phylogenetic clustering approach [12] , which arranges samples according to their distance in methylation patterning from that of undifferentiated cells , we found that genome-wide DNA methylation undergoes progressive changes from bone marrow CD34+ hematopoietic progenitor cells to NBC and NGC , FL and then DLBCL ( Figure 2A ) . This finding reflects the ontogeny of normal B-cell development , the origin of B-cell lymphomas in NGC , and the increased aggressiveness of DLBCL subtypes . We then performed a Kaplan-Meier analysis using a methylation heterogeneity score derived from the distances of the methylation pattern of each tumor to that of the methylation pattern of NGC ( Text S1 , Modules 1 and 3; Figures S18 , S19; Tables S3 , S4 ) . Cox models incorporating the International Prognostic Index ( IPI ) [13] and methylation heterogeneity score as covariates were then utilized for stratification of patients into high- and low-risk groups , depending on whether their estimated risk scores were above or below the cohort median . Analyzing the GCB and ABC samples together , we found that the methylation heterogeneity score improved the concordance [14] of the predictions of the IPI from 0 . 64 to 0 . 7 ( ΔC 0 . 06; 95% CI −0 . 08–0 . 20; Text S1 , Module 3 ) and yielded a significant risk stratification ( HR = 3 . 85 , p<0 . 03; Figure 2B ) . Thus , we found that the extent of aberrant methylation , as measured by the distance of a patient sample in terms of its methylation patterning from that of normal B-cells , is a significant predictor for survival: disease types with high intra-sample methylation variation have a poor prognosis and short survival while disease types with low intra-sample methylation variation have a good prognosis and long survival . Increased epigenetic heterogeneity may reflect the presence of diverse tumor cell populations in the patient , which in turn increases the risk of resistance and of the emergence of more aggressive clones , thus leading to poor prognosis . In a complementary analysis , we grouped the FL samples according to grade and found that DNA methylation patterning becomes increasingly heterogeneous with an increase in disease severity in FL ( Text S1 , Module 3; Figure S20 ) . Taken together , our results demonstrate that the landscape of epigenetic DNA modifications is associated with the degree of neoplastic transformation and aggressiveness of a tumor . To determine whether genomic features direct the aberrant cytosine methylation distribution in lymphomas , we examined DNA methylation diversity at the chromosomal regional level . In order to facilitate the visualization of intra-sample ( M-scores ) and inter-sample ( IQR ) heterogeneity in DNA methylation , we transformed the histograms shown in Figure 1B and 1C into a “violin” plot format ( Figure 3A ) . Chromosomes were separated into telomeric , centromeric , and intermediate regions . We observed that centromeric regions were hyper-methylated in normal cells but exhibited a gradual loss of methylation in lymphomas ( Figure 3B ) . Intermediate chromosomal regions displayed increasing intra-sample variation with disease severity , i . e . NBC<NGC<FL<GCB<ABC ( p-value for NBC-FL , NBC-GCB and NBC-ABC pairs<2 . 2×10−16; Kolmogorov-Smirnov test ) , suggesting that much of the heterogeneity observed in the initial analysis is localized in these regions . All three regions displayed an overall tendency towards greater inter-sample variation in lymphoma cells compared to normal cells throughout all three chromosomal regions . These results were also validated using the ERRBS assay ( Text S1 , Module 2; Figure S17 ) . To investigate whether disruption of cytosine methylation is associated with gene density , we divided the genome into non-overlapping windows classified as gene-rich , intermediate , or gene-poor ( Methods section ) . We found that in normal B-cells , gene-rich regions displayed a bimodal methylation pattern , while gene-poor regions were mostly hyper-methylated . This distribution was perturbed in lymphomas , which exhibited increased intra-sample variation in gene-rich regions , while gene-poor regions displayed progressive hypo-methylation compared to normal B-cells ( Figure 3C ) . Inter-sample variation was low in normal cells in both gene-poor and gene-dense regions , but significantly increased in the lymphoma subtypes for both categories ( FL: p-value<1×10−3 , GCB and ABC: p-value<1×10−10; Mann Whitney test ) . Our findings were robust even after excluding centromeric and telomeric regions ( Text S1 , Module 4; Figure S21 ) . Taken together , our results show that abnormal methylation patterns in lymphoma samples depend on chromosomal regions and local gene density . This differential aberration in gene-poor versus gene-rich areas suggests that these changes are not random , but are directed by genomic or epigenomic modifiers . We next focused at the level of specific genes and their impact on DNA methylation of neighboring genes ( Figure 4A ) . We found that 3 , 414 and 2 , 044 probesets were significantly hyper- and hypo-methylated in ABC vs . NGC specimens , respectively ( FDR-corrected p-value<5 . 0×10−3 , Text S1 , Module 5 ) . For each of these hypo- and hyper-methylated promoters ( denoted as “i” in Figure 4A and 4B ) , we investigated their neighboring promoter probesets ( “i+1” , “i−1” , “i+2” , “i−2” up to “i−5” and “i+5” ) . For both hyper- and hypo-methylated promoter probesets , we found that their neighboring promoter probesets also displayed a change in methylation in the same direction ( Figure 4B ) , and that this effect weakened with increasing distance , i . e . decayed from i+1 ( or i−1 ) to i+5 ( or i−5 ) . Therefore , when a promoter displayed aberrant hypo- or hyper-methylation in lymphoma samples , their neighboring promoters were also likely to follow a similar trend . For instance , when the i-th promoter probeset was aberrantly hypo-methylated ( ΔM-score>0 ) , then the i±1 ( i . e . i+1 and i−1 ) promoter probesets were also significantly aberrantly hypo-methylated ( ΔM-score>0; p-value: 4 . 56×10−5 ) ; and when the i-th promoter probeset was aberrantly hyper-methylated ( ΔM-score<0 ) , then the i±1 positions were also significantly aberrantly hyper-methylated ( ΔM-score>0; p-value: 3 . 11×10−3 ) . This effect was stronger for hypo-methylated loci . For instance , when the i-th probeset was aberrantly hypo-methylated ( ΔM-score>0 ) , then the s±5 ( i . e . i+5 and i−5 ) positions were also significantly aberrantly hypo-methylated ( ΔM-score>0; p-value: 3 . 01×10−3 ) , but the effect was not significant in the case of aberrant hyper-methylation ( ΔM-score<0; p-value>0 . 05 at i±5 positions ) . We then found that the aberrantly hypo-methylated promoters , but not the hyper-methylated ones , generally displayed a greater extent of inter-sample variation among ABC lymphomas ( Figure 4C ) . Our results were similar for the other lymphoma subtypes ( Text S1 Module 5; Figures S22 and S23 ) , and at par with published reports that local DNA methylation and histone modification ( H3K9me3 ) patterns spreads to neighboring regions . [15]–[17] Thus it is likely that abnormal promoter methylation , especially hypo-methylation , tends to spread to neighboring promoters along the chromosomes; however , at this stage we cannot rule out other possibilities . The transcriptional repressor CTCF contributes to the organization of chromatin domains and the spatial delimitation of epigenetic marks [18] . Hence , we investigated whether CTCF was associated with the DNA methylation status of genes in normal and lymphoma cells . Overlaying published genome-wide CTCF ChIP-seq data [18] , [19] , we found that promoters in CTCF-binding site ( BS ) -poor regions were usually hyper-methylated in normal B-cells , but hypo-methylated in lymphomas ( FL , GCB and ABC ) ( Figure 5A–5B ) . There was little inter-sample variation in normal cells regardless of the density of CTCF binding , whereas in the lymphoma subtypes , CTCF-BS-poor regions displayed significantly greater inter-sample variation than CTCF-BS-dense regions ( p-value for NBC: 1 . 040×10−6; NGC: 6 . 656×10−7; FL: 2 . 9×10−13; GCB: 1 . 367×10−11; ABC: <2 . 2×10−16 , Mann Whitney test ) . Our findings were robust even after excluding centromeric and telomeric regions ( Text S1 , Module 6; Figure S24 ) . These data suggest that CTCF-BS-poor regions are more susceptible to epigenetic deregulation . Since CTCF can establish boundaries between genomic regions , we tested whether it might affect DNA methylation spreading between loci and whether this function was perturbed in lymphomas . We divided the probesets into two groups: those in which neighboring promoter probesets were separated by at least one CTCF-BS , and those in which neighboring promoter probesets were not separated by any CTCF-BS . First , focusing on the promoters hypo-methylated in ABC versus NGC , we found that promoter pairs not containing intervening CTCF–BS displayed greater spreading of aberrant hypo-methylation from one promoter ( probe set i ) to the neighboring promoters ( probe sets i+1 and i−1 ) compared to those that had one or more intervening CTCF-BS ( Figure 5B , comparing probe sets i+1 , i−1 between the two groups , p-value = 2 . 2×10−8 , Mann Whitney test ) . In contrast , we did not observe any impact of CTCF on genes with hyper-methylation in DLBCL ( Figure 5B , p-value>0 . 05 at probesets i±1 , Mann Whitney test ) . We obtained similar results for the FL and GCB samples and using the ERRBS assay instead of the HELP assay data ( Text S1 , Module 6; Figures S25 , S26 ) . Thus , CTCF is suspected to play a gatekeeper role in the spreading of aberrant hypo-methylation among genes in DLBCL ( Figure 5C ) , although more work is necessary to rule out other possibilities . We then investigated potential factors associated with the abnormal DNA methylome in lymphomas . We first investigated whether in general , there was an association between promoter methylation status and the expression of the same gene and found a positive correlation ( Text S1 , Module 7 ) , which suggests that genes with a loss of promoter methylation were likely to experience increased expression . We then obtained genomic localization data , detected by genome-wide CHIP-chip or CHIP-seq studies , of four master regulators of lymphoid differentiation and lymphomagenesis: BCL6 [20] , EZH2 [21] , MYC ( newly reported herein ) , and AICDA [22] , and overlaid promoter methylation information ( Text S1 , Module 7; Figures S27 , S28 , S29 ) . BCL6 is a transcriptional repressor that is expressed in NGCs and also in most DLBCLs and FLs [23] , and its constitutive expression is known to drive malignant transformation of NGCs [24]; EZH2 is a Polycomb repressor protein also expressed in NGC [25] that is highly expressed in most DLBCLs [26] and is sometimes targeted by gain of function mutations [27]; and the MYC oncogene , is aberrantly expressed in DLBCLs , often through chromosomal translocations [20] , [28] . AICDA is a cytosine deaminase that mediates single- and double-strand DNA breaks during somatic hypermutation and class switch recombination [29] . We first investigated the extent of change in the DNA methylation status of the BCL6 and MYC loci , including the surrounding genes , in lymphoma samples compared to that in the NBC samples , and found that both BCL6 and MYC loci experienced loss of promoter methylation in lymphoma samples compared to normal samples ( Text S1 , Module 7; Figure S29 ) . Furthermore , we found that the target gene promoters of MYC , BCL6 and EZH2 were hypo-methylated in normal B-cells and became increasingly hyper-methylated in lymphoma samples ( Figure 6A–6C; p-value<1×10−4 for all three cases; Mann-Whitney test ) . Gain of methylation at the promoters of the target genes of MYC , BCL6 , and EZH2 in lymphomas was significantly higher than that at the promoters of other genes ( Mann Whitney test , p-value<1×10−5 in each case ) . Since BCL6 and EZH2 are transcriptional repressors , accumulation of DNA methylation may reflect their constitutive activity at their targets in lymphoma cells . Notably , a previous report showed that EZH2 and H3K27 are mostly mutually exclusive with DNA methylation in NGC B-cells , but that this opposing relation is disrupted in DLBCL [21] . The reason for MYC targets acquiring hypermethylation is not as clear , but it is notable that the MYC and BCL6 ChIP-on chip binding patterns are highly overlapping ( data not shown ) . In contrast , target genes of AICDA , such as BRCA2 , GATA1 and LMO1 [22] , displayed a loss of bimodality ( Figure 6D ) . However , hypomethylation of promoters of AICDA target genes was not immediate apparent , perhaps indicating disruption or variability in AICDA binding to the genome in malignant cells . Nevertheless , AICDA expression was associated with a loss of DNA methylation at a genome-wide scale , as discussed in the following section in detail , which is consistent with the role of AICDA in demethylation [6] , [30] , [31] . AICDA plays a role in gene demethylation downstream of TET family protein-mediated hydroxylation of methylcytosine [30] . Moreover , we recently reported that genes that are hypo-methylated in NGC B-cells tend to be known direct targets of AICDA [10] . Collectively , aberrant DNA methylation in lymphomas is related in part to the action of constitutively expressed lymphomagenic regulatory factors during lymphomagenesis ( Figure 6E ) . We next used an independent approach , integrating DNA methylation and gene expression profiling data in a subset of our cases , to identify factors driving or associated with the aberrant lymphoma methylome . First , we focused on a set of genes: DNMT3A , DNMT3B , DNMT3L , MYC , BCL6 , AICDA , MBD4 , MBD6 , CD79A , CD79B and MECP2 – which include DNA methyltransferases , methyl-CpG binding domain proteins , as well as signaling and transcription factors involved in lymphoid differentiation and lymphomagenesis . We investigated whether the expression levels of these genes correlated with genome-wide aberrant DNA methylation patterns in DLBCL samples ( see Text S1 , Module 7 for details of the method ) . We found the following trends ( Figure 7A ) : ( i ) the BCL6 expression level was significantly correlated with aberrant hyper-methylation at a genome-wide scale ( p-value<0 . 05 ) , which is consistent with a transcriptional repressor role of this gene , and ( ii ) expression levels of AICDA and CD79A were significantly correlated with aberrant hypo-methylation at a genome-wide scale ( p-value<0 . 05 in both cases ) . This finding was significant given the role of AICDA in demethylation , as noted above [6] , [30] , [31] . The association was not significant for other genes in the list for our dataset . Larger patient cohorts will be necessary to test those cases systematically . Some the above factors , such as DNMT3B , are associated with the maintenance of methylation in simple repeat sequences [32] . Indeed , overlaying DNA repeat sequence information , we found that both low-complexity repeats and simple repeats exhibited hypo-methylation in normal cells , but displayed an increasing extent of hyper-methylation in lymphoma samples ( Text S1 , Module 8; Figure S30 ) . Note that the effect size of expression levels of each of the 11 genes , including DNA methyltransferases , is relatively modest; this may be at least partly due to the fact that these modifier genes influence the epigenetic state of their target genes by the recruitment of other enzymes or cofactors , and that the lymphoma samples show a high level of intra-sample variation . A completely unbiased genome-wide analysis exploring whether other genes on the expression array showed significant associations with the aberrant methylation pattern in lymphomas yielded a list of candidates provided in Figure 7B ( see Text S1 , Module 7 for details ) . Interestingly , some of the top genes are known epigenetic modifiers . For instance , the top candidate of the list , WHSC1L1 , is a known histone methyltransferase and plays a key role in chromatin integrity [33]–[35] . Other top hits are important for the genomic and epigenomic integrity of the cell , such as NAP1L2 , which promotes histone acetylation [36] , and SMC6 , which regulates chromosomal stability [37] , [38] . Many of these genes are known epigenetic modifiers , downstream targets or co-factors of the shortlisted genes described in the paragraph above , while others may be novel factors associated with perturbation of DNA methylation patterning in DLBCLs . Systematic characterization of these candidates will be pursued further in forthcoming work . Through integrative analysis of DNA methylation , copy number variation , genomic sequence , gene expression and genomic localization data , our study provides insights into the architecture and biology of aberrant DNA methylation patterning in a human malignancy . Based on these analyses , we report three key findings: ( a ) DNA methylation exhibits considerable heterogeneity , both within individual lymphoma samples and between patients , and the degree of heterogeneity and departure from the DNA methylation pattern of normal B-cells correlates with disease severity and patient survival , ( b ) these abnormal methylation patterns are not randomly distributed but instead associate with chromosomal regions , local gene density , and the methylation status of neighboring genes , and ( c ) the pattern of DNA methylation abnormalities is at par with the effects of at least two distinct processes: i ) lymphomagenic transcriptional regulators , such as BCL6 and EZH2 , perturb DNA methylation in a target gene-specific manner; and ii ) aberrant methylcytosine marks , especially promoter hypo-methylation , tend to spread to neighboring promoters in the absence of insulator elements such as CTCF . We propose that focal aberrant hyper- and hypo-methylation via target-specific recruitment of master regulators and non-specific spreading of aberrant methylation drives the generation of epigenetic abnormalities in follicular lymphoma and DLBCL . While our results themselves cannot pinpoint causality , they are consistent with emerging reports that highlight the roles of lymphomagenic transcriptional regulators [23] , [24] , [26] and that DNA methylation patterns tend to spread in a genomic neighborhood [15]–[17] . Recently , Lai et al . showed that BCL6 expression is maintained during lymphomagenesis in part through DNA methylation that prevents CTCF-mediated silencing [39] , and our results confirm their conclusions . The fact that epigenetic diversity is first observed in NGC B-cells further suggests that epigenetic heterogeneity may originate in these rapidly dividing cells and potentially contributes to malignant transformation . Since epigenetic abnormalities increase with disease aggressiveness and are a predictor of patient survival , clonal epigenetic diversity and evolution may increase the survival advantage of lymphoma cells , leading to more aggressive and chemo-resistant tumors . Heterogeneity in DLBCL DNA methylation patterning does not preclude the co-existence of subtype-specific DNA methylation profiles , in which specific genes are differentially methylated . Indeed , we previously observed that ABC and GCB DLBCLs feature distinct and specific DNA methylation signatures , involving genes of potential functional significance , most notably including hypermethylation of a TNFα gene network in ABC DLBCLs [6] . These signatures represent a core of stably affected loci within the larger context of more variable DNA methylation disruption as reported herein . While previous studies including ours aimed at identifying key genes and pathways dysregulated in lymphomas , here we add another dimension to these studies by highlighting the implications of epigenetic heterogeneity at a genome-wide scale , and also the complex interaction between master regulators and insulator elements that contribute to establishing an abnormal methylome during lymphomagenesis . Our approach can be used to analyze other tumor types and delineate the contribution of aberrant methylation patterning to the development of human cancers .
Samples used in the study included naïve B-cells ( NBC; 8 samples ) , normal germinal center B-cells ( NGC; 10 samples ) , follicular lymphoma ( FL: grades 1 and 2 representing lower grade lymphoma than Diffuse Large B-cell Lymphoma ( DLBCL ) ; 8 samples ) , germinal center B-cell-like DLBCL ( GCB: DLBCL with better prognosis; 39 samples ) , and activated B-cell-like DLBCL ( ABC: DLBCL with worse prognosis; 18 samples ) . All FL and DLBCL samples used in the study were selected based on their high content of neoplastic cells from primary diagnostic material preceding treatment and were obtained by the Vancouver Cancer Center in British Columbia , Canada . The FL and DLBCL samples represent soft tissue biopsy material . The percent of neoplastic cells in the biopsy was determined based on pathologic evaluation using morphologic criteria and immunohistochemical characteristics of the neoplastic cells ( expression of CD79B , CD20 , BCL2 , CD10 , CD43 , BCL6 antigens ) . The use of human tissue was in agreement with IRB of the Vancouver Cancer Center and Weill Cornell Medical Center . Primary NBC and NGC B-cells were purified from reactive human tonsillar specimens . Tonsils were minced on ice and mononuclear cells were isolated using Histopaque density centrifugation . All washes were performed in PBS/2% Bovine Serum Albumine/2% EDTA . All antibodies were used at 1∶100 dilution in cold PBS and staining was done for 10 min on ice , followed by 3 washes . The B-cell populations were separated using the AutoMACS system ( Milteny Biotec , Auburn , CA ) using the “posselD” program . In brief , NBC cells were separated using depletion of GC cells , T-cells , plasma and memory cells ( CD10 , CD3 , CD27 ) , followed by enrichment for IgD+ B-cells; GCB cells were separated by positive selection with CD77 ( anti-CD10: BD Biosciences cat# 555373 Lot 59624 , anti-CD3: BD Biosciences cat# 555332 Lot 59347 , anti-CD27: BD Biosciences cat# 555439 Lot 71274 , anti-CD77: Serotec cat# MCA579 Batch 180510 , anti-IgD: BD Biosciences cat# 555778 Lot 58641 ) . While the tissue environment of the collected normal and lymphoma cells ( e . g . cytokine exposure level ) differ , this is unlikely to bias our analyses . All NBC and NGC samples yielded a purity of >90% . For patient characteristics ( Table S1 ) see Shaknovich et al . [6] and GEO number GSE23967 . We assayed genome-wide patterns of promoter methylation using HELP assays and custom-designed oligonucleotide arrays . HELP assays were performed based on the standard protocol [7] . One µg of high molecular weight genomic DNA was digested with HpaII and MspI ( NEB , Ipswich , MA ) , digestion products were extracted with phenol-chloroform and resuspended in 10 mM TRIS-HCl pH8 , after which they were subjected to ligation of HpaII adapter using T4 DNA Ligase . This approach was followed by PCR amplification and labeling of HpaII and MspI digestion products and co-hybridization to custom NimbleGen HELP microarrays ( NimbleGen , Inc . Madison , WI ) . The microarray design was previously published and represents >50 , 000 CpGs corresponding to 14 , 000 promoters [6] , [7] . Data processing was performed using the published HELP pipeline [40] . Intra- and inter-array normalization was performed by subtracting mean random probe intensity separately within HpaII and MspI channels , after which quantile normalization was performed within each channel independently . Quantile normalized log2 ( HpaII/MspI ) values , denoted as M-scores , were subsequently used for all further analysis . The probes whose intensity of Msp1 channel was less than 2 . 5 mean absolute standard deviations from the mean of log2 ( Hpa2/Msp1 ) of random probes were considered failed and removed from the analysis . Since the Msp1 channel served as an internal control , it allowed us to remove the probes that had low intensities due to genomic deletions , thus avoiding false positives for hypermethylation . We also calculated the inter-quartile range ( IQR ) of the M-scores between the samples within the same normal or disease group at a given locus to reflect inter-sample methylation diversity . The analysis was based on the Human Reference Genome version hg19 [41] and the list of human protein-coding genes was obtained from Ensembl v59 [42] . HELP arrays for DLBCLs can be found in GEO number GSE23967 and for CD34+ cells in GEO number GSE18700 . NBCs , NGCs and FLs data is pending GEO accession number . HELP methylation data for CD34+ hematopoietic progenitor cells was obtained from the NCBI Geo database ( GSE18700 ) . HELP was performed in the same manner as the normal ( NBC and NGC ) and lymphoma ( FL , GCB and ABC ) samples , hybridized on the same methylation microarray and normalized using the same protocol together with the normal and lymphoma samples . Only promoter methylation probes without missing values were considered for further analysis . M-scores were averaged for each group ( CD34+ , NBC , NGC , FL , GCB and ABC ) on each probe . Pairwise distances between groups were then calculated with the Pearson correlation distance on the group-averaged M-scores . This approach was repeated 1000 times , with bootstrapping of promoter methylation probesets and samples . Then , phylogenetic trees were constructed using the FastME method , implemented in the ape R package , on these 1000 distance matrices . A consensus tree was calculated in Dendroscope . The code is available as R package at https://github . com/lima1/maphylogeny . We obtained both genome-wide promoter methylation and gene expression data for 4 NBC and 45 DLBCL samples ( 13 ABC and 32 GCB samples ) . Expression data for NBC were obtained from GSE15271 , generated using Affymetrix HG133_Plus2_microarray and mas5 normalized together with the expression data for the DLBCL samples ( GSE23501 ) . The processing of RNA , hybridization , and image scanning were performed as per Affymetrix protocols . The trimmed mean target intensity of each array was set to 500 . Expression-based classification labels GCB and ABC were assigned as published in Shaknovich et al . [6] . The gene expression data for all DLBCLs was deposited to GEO number GSE23501 . MYC ChIP-on-chip analysis was performed using Ramos cells . First , Ramos cells were fixed in 1% formaldehyde for 10 min , quenched with glycine and washed three times with PBS . Cells were then resuspended in lysis buffer and sonicated 6×30 sec ( amplitude 55% ) in an Ultrasonic Dismembrator Model 500 ( Fisher ) to shear the chromatin to an average length of 500 bp . Supernatants were precleared using protein-A agarose beads ( Roche ) and 10% input was collected . Immunoprecipitation was performed in 107 cells using antibodies against MYC ( Santa Cruz ) . DNA-protein complexes were pulled down using protein-A agarose beads and washed . DNA was recovered by overnight incubation at 65°C to reverse cross-links and purified using QIAquick PCR purification columns ( Qiagen ) . ChIP products and their respective input genomic fragments were amplified by ligation-mediated PCR [43] . Q-ChIP was repeated after amplification to verify that the enrichment ratios were retained . The genomic products of three biological ChIP replicates were labeled with Cy5 ( for ChIP products ) and Cy3 ( for input ) and co-hybridized on a NimbleGen human promoter array representing 1 . 5 kb of promoter sequence from >24 , 000 genes ( human genome version 35 , May 2004 ) according to manufacturer's protocol ( Roche NimbleGen , Inc . , Madison , WI ) . The enrichment for each promoter was calculated by computing the log ratio between the probe intensities of the ChIP product and input chromatin , which were co-hybridized on the same array . Thereafter , for each of the >24 , 000 promoter regions , the maximum average log ratio of three neighboring probes in a sliding window was calculated and compared with random permutation of the log ratios of all probes across the entire array . The MYC ChIP-on-chip data is available on GEO ( accession number GSE31110 ) . The Chip-chip data for BCL6 and EZH2 were previously published [20] , [21] . AICDA ChIP-seq data was obtained from the recently published study in mouse activated B-cells [22] ( GEO accession number GSE24178 ) . Short reads were aligned to the mm9 genome and ChIP-seq peaks were called using the ChIPSeeqer program ( http://icb . med . cornell . edu/wiki/index . php/Elementolab/ChIPseeqer_use ) . Peaks within RefSeq gene promoters , defined as 4 kb windows centered on transcription start sites , were then extracted . Human and mouse unambiguous orthologs were then determined using the reciprocal best BLAST strategy with protein sequences obtained from RefSeq ( and matched with RefSeq transcripts ) . Human genes whose mouse orthologs were associated with 1 or more AICDA peaks in mouse activated B-cells were then determined . We obtained CTCF binding site data from InsulatorDB ( http://insulatordb . uthsc . edu; downloaded Jan , 2011 ) , where CTCF binding sites ( CTCF-BS ) were determined using ChIP-on-chip and computational approaches [18] , [19] , [44] . We performed our analysis using experimentally determined CTCF-BS from this database , and obtained similar results using computationally predicted CTCF-BS from this database . All statistical analyses were performed in R .
|
Follicular lymphomas and diffuse large B-cell lymphomas are the most common non-Hodgkin lymphomas . Although these diseases share many mutant alleles , the underlying cause of the different phenotypes remains unclear . We show that direct comparison of DNA methylation patterning provides insights about gene deregulation during lymphomagenesis and explains the nature of the different clinical behavior .
|
[
"Abstract",
"Introduction",
"Results/Discussion",
"Methods"
] |
[
"oncology",
"medicine"
] |
2013
|
Aberration in DNA Methylation in B-Cell Lymphomas Has a Complex Origin and Increases with Disease Severity
|
Bacterial porins are water-filled β-barrel channels that allow translocation of solutes across the outer membrane . They feature a constriction zone , contributed by the plunging of extracellular loop 3 ( L3 ) into the channel lumen . Porins are generally in the open state , but undergo gating in response to external voltages . To date the underlying mechanism is unclear . Here we report results from molecular dynamics simulations on the two porins of Providenica stuartii , Omp-Pst1 and Omp-Pst2 , which display distinct voltage sensitivities . Voltage gating was observed in Omp-Pst2 , where the binding of cations in-between L3 and the barrel wall results in exposing a conserved aromatic residue in the channel lumen , thereby halting ion permeation . Comparison of Omp-Pst1 and Omp-Pst2 structures and trajectories suggests that their sensitivity to voltage is encoded in the hydrogen-bonding network anchoring L3 onto the barrel wall , as we observed that it is the strength of this network that governs the probability of cations binding behind L3 . That Omp-Pst2 gating is observed only when ions flow against the electrostatic potential gradient of the channel furthermore suggests a possible role for this porin in the regulation of charge distribution across the outer membrane and bacterial homeostasis .
The outer membrane of Gram-negative bacteria is sprinkled of homotrimeric channels comprised of three 16-stranded β-barrels . They are the principal conduits for the passive penetration of hydrophilic molecules into the periplasm , and are often referred to as the general diffusion porins . Classical examples include Escherichia coli OmpF [1] , OmpC [2] and PhoE [1] . The general diffusion porins display a conserved β-barrel architecture with eight periplasmic turns and eight extracellular loops ( L1–L8 ) . Also conserved is the presence of a constriction zone ( CZ ) , at mid height of the channel . The CZ is contributed by the plunging of extracellular loop L3 into the channel lumen , where it adopts a helix-turn-loop fold and interacts with the barrel wall through hydrogen bonding and Van der Waals ( VDW ) interactions . The amino-acid distributions in L3 and on the barrel wall opposite to it ( “anti-L3 region” ) determine the sieving properties of the porins , i . e . their ion specificity and size exclusion limit [3 , 4] . The L3 and anti-L3 region generally display opposed charge distribution , with L3 being negatively charged and anti-L3 positively charged . The general diffusion porins can switch from their open state to gated states when a transmembrane potential is applied [5–10] , a phenomenon termed as voltage gating ( VG ) . VG is characterized by step-wise , long-lasting closed states that persist until the transmembrane potential is suppressed . The critical voltage required for voltage gating ( Vc ) varies among porins , but is generally in the order of hundreds of mV . Vc can be influenced by a variety of environmental cues , including pH and salt concentration [11 , 12] , membrane constitution [8 , 13] , polarity of the transmembrane potential [9] , or the presence of effectors such as oligosaccharides [13] and polyamines [14] . Accumulated evidences have suggested that voltage sensing in general diffusion porins occurs at the CZ [15–21] . It was shown that replacement of L3 charged residues by uncharged ones invariably results in alteration of voltage sensitivity , channel conductance and/or ion selectivity [15 , 21 , 22] . In particular , replacement of negatively charged residues from L3 leads to an increase of Vc in cation-selective E . coli OmpF , while that of the positively charged residues in anti-L3 regions causes the decrease of Vc in anion-selective E . coli PhoE [15] . In addition , mutagenesis studies on OmpF showed that destabilization of L3 by deletion of residues at its tip leads to increased voltage sensitivity ( lower Vc ) and reduced conductance [18 , 19] , whereas mutations of residues involved in L3 stabilization result in reduced voltage sensitivity ( higher Vc ) [19 , 20] . More than a decade ago , Tieleman et al reported the first molecular dynamics ( MD ) simulation of E . coli OmpF embedded in explicit lipids . Their results revealed instability in L3 due to a breakdown of the hydrogen-bonding network ( HBN ) anchoring L3 to the barrel wall [23] . Suspecting that the fluctuation in the CZ may have been due to the protonation setting , Im et al and Varma et al respectively implemented MD simulations using different ionization states to the charged residues on L3 [24–26] . The invariable observation was that L3 is prone to large fluctuations , suggesting that this loop could intervene in translocation across porin and possibly also in voltage-gating . Owing to constant improvements in MD simulation algorithms [27 , 28] and the successful implementation of artificial transmembrane potentials [29] , it has become possible to simulate ion mobilizing within the channel and thus to study the channel transport properties at a molecular level . For example , Pezeshki et al showed that mutation of one charged residue within CZ leads to visible effects on ion permeation and selectivity in OmpF [4]; Faraudo et al found that removal of negative charges in CZ influences the distribution of cations along OmpF channel [30] . However , owing both to limited computational resources and to the low voltage sensitivity ( high Vc ) of the hitherto simulated porin ( OmpF ) , the molecular mechanism by which the general diffusion porin gating occurs has not yet been reported . In the following , we report on extensive comparison of sub-microsecond scale MD simulations that provide insights into the molecular basis of voltage gating in general diffusion porins . Simulations were conducted on Omp-Pst1 and Omp-Pst2 , two general diffusion porins from Providencia stuartii [31] . The sequence identities with OmpF of Omp-Pst1 and Omp-Pst2 are 50% and 46 . 1% respectively , while the RMS deviation of their Cα atoms ( as measured from their respective X-ray structures ) are 0 . 94 Å and 0 . 89 Å respectively . Omp-Pst1 and Omp-Pst2 show a high level of structural similarity , but owing to a completely different pattern of charge distribution along their channel wall , the two porins display opposite ion selectivities . Furthermore , whereas Omp-Pst1 gates at voltages above 199 mV , Omp-Pst2 undergoes the typical three-step gating at voltages as low as ~20 mV , making it the most voltage sensitive bacterial porin studied to date . Taking advantage of this striking contrast , we constructed parallel simulations between Omp-Pst1 and Omp-Pst2 at positive , negative and none transmembrane potentials ( VTM; extracellular to intracellular ) . In Omp-Pst2 , we observed gating at VTM < 0 V , but not at VTM > 0 V , consistent with asymmetrical gating observed experimentally . At VTM < 0 V , the gating stems from stable binding of cations in acidic niches behind L3 , which in turn disrupts the HBN anchoring L3 to the barrel wall and thereby allows a local conformational change in conserved W111 at the tip of L3 . The repositioning of W111 aromatic side chain in the middle of the CZ effectively halts ionic permeation across the Omp-Pst2 channel . At VTM > 0 V , the HBN between L3 and the barrel wall strengthens . The stabilized L3 impedes cation binding , and thus thwarts gating . In Omp-Pst1 , gating was not observed , regardless of the VTM applied . Additional hydrogen bonds in HBN contribute to a more resilient L3 . Altogether , our results suggest that the voltage sensitivity of Omp-Pst1 and Omp-Pst2 is encoded in the HBN that anchors L3 onto the channel wall , and that conformational changes in the side chain of conserved W111 at tip of L3 leads to channel closing .
Reconstitution of a single Omp-Pst1 / Omp-Pst2 channel in a planar lipid bilayer showed a single trimer conductance of 2 . 7 ± 0 . 1 / 3 . 7 ± 0 . 2 nS respectively at 1M KCl , pH 7 . In the ion selectivity measurements performed as described in [32] , Omp-Pst2 shows strong cation selectivity whereas Omp-Pst1 shows indistinctive cation selectivity ( Table 1 ) . For Omp-Pst1 , the critical voltage ( Vc ) for observing the typical three-step gating was ≥199 mV in all measurements ( n = 8 ) . For Omp-Pst2 , pore-to-pore variation was observed , and Vc measurements varied between 20 and 90 mV , with a median at 50 mV ( n = 20 ) . The crystallographic structures of Omp-Pst1 and Omp-Pst2 trimers were used as starting model for the simulations . Briefly , each porin was inserted in a lipid bilayer , solvated at pH 7 , and the ionic concentration was adjusted to 1 M KCl . After equilibration , the two systems were subjected to either a negative ( VTM direction pointing from extracellular to intracellular ) or a positive transmembrane potential ( VTM direction pointing from intracellular to extracellular ) and each simulation was ran for > 500 ns . Additional simulations without a transmembrane potential were also carried out for both porins and each lasted for 100 ns . The β-barrel remains rigid in all voltage conditions , showing RMS fluctuation lower than 1 Å ( S1 Fig ) . Larger fluctuation is observed in extracellular loops , especially in L1 , L4 , L5 and L6 . L1 lies on the interface of the trimer and interacts with the positively charged residues at anti-L3 regions from another monomer . L4 , L5 and L6 form secondary structure elements that associate to cover the extracellular entrance of the channels . Applying transmembrane potentials does increase the amplitude of loop fluctuation . However , according to the principal component analysis , their movements are constrained in a radius of ~5 Å , as imposed by a strong network of polar interactions between adjacent loops ( S2 Fig ) . In simulations with VTM ≠ 0 , anions and cations separate into two pathways at the CZ: cations ( K+ ) trail along negatively charged L3 ( residues Y98 to D123 in Omp-Pst1 and Y95 to D120 in Omp-Pst2 , respectively ) , while anions ( Cl- ) duct on the positively charged anti-L3 region ( Fig 1A and 1B ) . In Omp-Pst1 , K+ mainly shuffle between the acidic side chains of L3 residues D109 and D117 , while Cl- interact with the basic side chains of anti-L3 residues K16 , R20 , R41 , R59 , K65 , R78 , K163 and K170 . In Omp-Pst2 , these residues are D106 , D114 and D117 , on the one hand , and R20 , R38 , R56 , R75 , K160 and K168 , on the other . Steady ionic currents were developed in the voltage-applied systems except for Omp-Pst2 at VTM < 0 mV . As we show below , gating occurred in this simulation , thus impacting ion current across the channels ( Figs 2A and S3 ) . In the other simulations , ion currents across the channel reached excellent agreement with the experimental measurements after fluctuating for ~200 ns . The current curve suggests that long equilibration time ( ~100–200 ns ) is required when simulating porins at high ionic concentration ( 1M in our case ) . That this requirement was not fulfilled in earlier simulations on porins at high ionic concentrations may explain the discrepancy between their simulated and experimentally measured currents [4 , 33] . From the stable K+ and Cl- permeation after 200 ns or before gating in the case of Omp-Pst2 at VTM < 0 mV , we derived the simulated conductance via least-square linear regression of the I/V curve ( VTM = [–1 , 0 , 1] V ) . The raw conductance for Omp-Pst1 and Omp-Pst2 are 3 . 64 nS and 4 . 01 nS respectively , while the corrected values based on ion diffusion coefficients ( See Methods ) are 3 . 95 nS and 4 . 47 nS respectively . The deviation from experimental observables might result from poor data samples in regression fitting . Simulations indicate a strong cation-selectivity for Omp-Pst2 and a mild anion-selectivity for Omp-Pst1 ( Fig 2C ) , in line with experimental electrophysiology data and also with the analysis of their structures . The net charge of Omp-Pst1 is indeed +1 e at the constriction zone ( within 5 Å radius of channel’s narrowest point ) and +5 e along the pore , while that of Omp-Pst2 is -4 e at the constriction zone and -3 e along the pore . In the case of Omp-Pst2 , we observed that translocation of cations from the intracellular to the extracellular side is more efficient than the other way around ( and conversely for Cl- ions ) , as grounded by its two times more pronounced cation-selectivity at VTM > 0 than at VTM < 0 ( Fig 2C ) . This asymmetry in ion selectivity correlates with the asymmetry in charge distribution ( and thus with the gradient of electrostatic potential ) along Omp-Pst2 channel , which features more acidic residues on the extracellular side than on the intracellular side . In Omp-Pst1 , where the distribution of charged residues is even along the pore ( Fig 1C ) , ion selectivity and translocation rate are less affected by the direction of the transmembrane potential ( Fig 2C ) . Our simulations thus suggest that the ion selectivity of porins is not only determined by the charge distribution at their constriction zone [30 , 34 , 35] , but also by the profile of charge distribution along the channel . After 100 ns , a decline in ion fluxes was observed in the simulation of Omp-Pst2 at VTM < 0 . Examination of ion fluxes on a monomer basis reveals that the decline mainly stems from one monomer in the trimer ( monomer B ) undergoing gating . At VTM < 0 , K+ ions are forced to translocate from the extracellular to the intracellular side , and inversely for Cl- ions . This direction of transit is unfavourable for cations , as suggested by the electrostatic potential developed along Omp-Pst2 channel and established by the two times slower uptake of cations in comparison to the other direction . As this unfavourable flux gets heavier across the channel of monomer B , it effects in disorganizing the HBN within L3 ( S4 Fig ) . A main-chain flip consequently occurs after 84 ns in 112-GA-113 , resulting in the opening a highly acidic niche ( niche I ) between L3 and the barrel wall ( Fig 3A ) . The acidic nature of the niche mainly results from the side chain oxygens of E258 , a barrel-wall residue hitherto shielded from the bulk , but also from the side-chain oxygens of N102 , T105 , T115 and N276 . A K+ ion rapidly ( within 0 . 5 ns ) lodges into niche I ( Fig 3F ) , dragging along the side chain of D106 . This results in the latter adopting a barrel-facing conformation , thereby reinforcing of the acidic nature of the niche . The change in orientation of D106 propagates to D114 , whose side chain draws towards the channel lumen , leading to yet another main chain rearrangement in L3 ( ~114 ns ) ( Fig 3C ) . Originating in 112-GA-113 , these changes rapidly transduce to 111-WGAD-114 ( ~131 ns ) and result in W111 side chain wandering away from the barrel wall ( Fig 3D and 3E ) . W111 movement effects in uncovering D312 , a highly conserved barrel wall residue whose side chain oxygens hydrogen bond to the tip of L3 ( main chain nitrogens of L110 and W111 ) in the crystallographic structure of Omp-Pst2 . Exposure of D312 side chain generates a second acidic niche ( niche II ) in which another K+ ion binds ~10 ns latter ( Fig 3F ) . Occupation of this niche effects in unleashing the tip of L3 , and most notably , the side chain of W111 ( Fig 3B ) . Originally constrained by Y20 , K314 and V334 , W111 first positions itself in the middle of L3 ( ~187 ns ) , but finally ends up in the channel lumen ( ~260 ns ) . There , it imposes steric and hydrophobic hindrance to ions translocation , notably through the formation of a hydrophobic belt by Y95 , Y99 and A103 side chains ( Fig 3G ) . In the last 100 ns , translocation of K+ ions is diminished by ~3/4 in monomer B , while that of Cl- ions by ~1/4 ( S5 Fig ) . In the two other monomers of the Omp-Pst2 trimer simulated at VTM < 0 , a comparable sequence of events is observed , albeit incomplete and spanning a longer time scale . In monomer C , a potassium ion binds in niche I after ~110 ns , following a main chain flip in 112-GA-113 ( ~80 ns ) . Similar to that in monomer B , binding of this first K+ ion induces a conformational change in 111-WGAD-114 ( ~240 ns ) that results in the detachment of W111 from the barrel wall ( 243 ns ) and the concomitant opening of niche II . However , binding of a K+ ion in this niche occurs at more than 200 ns latter ( ~ 453 ns ) . Thus , D312 side chain remains in its native conformation and migration of W111 side chain toward the channel lumen does not complete ( S6 Fig ) . Accordingly , only a slight reduction of K+ flux is observed , while Cl- flux remains unaffected . In monomer A , binding of a K+ ion in niche I occurs after 327 ns , and the resulting conformational change in 112-GA-113 after 350 ns . By the end of the simulation time , however , no K+ ion lodges into niche II . W111 consequently stays in place , and ionic fluxes remain steady ( S7 Fig ) . It is interesting to recall that in electrophysiology experiments , gating of the three monomers in a trimer also occurs sequentially . Thus monomers A , C and B could represent different Omp-Pst2 intermediates in the process of gating . No voltage gating was observed in the simulation of Omp-Pst2 at VTM > 0 . This observation is consistent with experimentally observed asymmetrical gating behaviour [10 , 36 , 37] . Trajectory analysis reveals that it is the stronger stabilization of L3 at VTM > 0 that impeaches gating . In short , a main chain rearrangement in 112-GA-113 leads to exposure of E258 and concomitant binding of K+ ions in niche I after ~340 , 20 and 95 ns in monomers A , B and C respectively . In the two latter , main chain rearrangements in W111 ( after 210 and 340 ns in monomers B and C , respectively ) precede binding of K+ ions in niche II ( after 245 and 351 ns in monomers B and C , respectively ) . Nevertheless , K+ binding to niche II is less stable at VTM > 0 and gating is hence not observed ( S8–S10 Figs ) . Two factors are responsible for the reduced gating sensitivity of Omp-Pst2 at VTM > 0 . First and most importantly , the HBN anchoring L3 to the barrel wall is more robust at VTM > 0 than at VTM < 0 ( Fig 4A ) , owing to a more favourable orientation of acidic side chains . At VTM > 0 , the acidic residues on Omp-Pst2 L3 are indeed facing toward the barrel wall , facilitating interactions with non-L3 residues . Major stabilizing interactions include: i/ a salt-bridge between E119 and R126; ii/ alternating hydrogen bonds between D117 and either Y99 or N102; and iii/ an hydrogen bond between D114 and Y294 before the main chain rearrangements in 112-AG-113 occurs ( Fig 4B ) . In strong contrast , L3 acidic residues adopt outward conformations in the simulation at VTM < 0 , which prevents them from forming hydrogen bonds or salt-bridges with neighbouring residues . Secondly , that the transit of K+ ions is energy favoured at VTM > 0 and that these ions thus transit faster across the CZ furthermore diminishes their likelihood of forming stable interaction with D312 . Their intermittent binding to niche II does not trigger the repositioning of W111 side chain . From a structural point of view , Omp-Pst1 shares the critical features of two acidic niches under L3 , with E266 and D321 ( equivalent to Omp-Pst2 E258 and D312 ) being the main contributors to niche I and II , respectively . Likewise , an aromatic residue is found at its L3 tip , viz . W114 ( equivalent to W111 in Omp-Pst2 ) . Yet , gating was not observed in Omp-Pst1 on the time-scale of our simulations , regardless of the directionality of the transmembrane potential . Accordingly , the HBN holding L3 attached to the barrel wall , W114-D321 , remained unaffected throughout all simulations ( Fig 5A ) . These observations are in faithful agreement with experimental data , which established a higher Vc for Omp-Pst1 than Omp-Pst2 ( Table 1 and S11 Fig ) . At VTM < 0 , binding of a K+ ion in niche I ( ~200 ns ) occurs in monomer A , where it results in a conformational jump of L3 residues 115-AG-116 ( equivalent to 112-GA-113 in Omp-Pst2 ) toward the extracellular side . In monomers B and C , however , neither binding of a K+ ion in niche I nor a conformational change in L3 is observed . Niche II meanwhile remains unoccupied in all three monomers ( S12–S14 Figs ) . At VTM > 0 , binding of a K+ ion in niche I is observed in monomers A and B ( after ~196 and ~254 ns , respectively ) , which precedes a conformational change in 115-AG-116 ( after ~300 and ~500 ns , respectively ) . But again , niche II remains unoccupied in all three monomers ( S15–S17 Figs ) . Thus at both VTM , and in all three Omp-Pst1 monomers , D321 remains covered by the side chains of L113 and W114 . The tip of L3 is therefore kept fastened to the barrel wall , and W114 maintains its native , open-state conformation . The discrepancy in the voltage sensitivity can be rationalized by a comparative analysis of Omp-Pst1 and Omp-Pst2 structures . In Omp-Pst2 , the tip of L3 is attached to the barrel wall solely through hydrogen bonding of L110 and W110 main chain nitrogens to D312 , whereas the presence of Q270 , S280 and Y309 in Omp-Pst1 contributes three additional hydrogen bonds that manifestly increase the energy barrier of exposing the acidic niches ( Fig 5B ) . The lower sensitivity to voltage of Omp-Pst1 ( S18 Fig ) is thus encoded in the HBN holding L3 attached to the barrel wall . Of note , we also observed asymmetry in HBN resilience in simulations of Omp-Pst1 . At VTM > 0 , and not at VTM < 0 , the hydrogen bond between Y309 and V110 is disrupted at the end of the simulation . Therefore , Omp-Pst1 could gate faster at this VTM ( Fig 5C ) . Opposed asymmetries in voltage sensitivity were reported earlier for E . coli OmpF and PhoE [10] .
This study reports on four 0 . 5 μs time-scale simulations conducted on the two porins from P . stuartii , viz . Omp-Pst1 and Omp-Pst2 , at different transmembrane potentials . Under application of a negative transmembrane potential , we observed partial gating in one monomer of Omp-Pst2 ( monomer B ) . Trajectory analysis revealed that a sequential changes on L3 , including loop rearrangement and exposure of the hitherto hidden acidic niches , leads to protrusion of W11 side chain in the channel lumen . The presence of this large indol ring in the middle of the CZ hinders ion transit and results in the conductance of the monomer being decreased by ~50% . Based on curve fitting ( I ( t ) = a × e-b×t ) to the calculated currents , we estimate that the time required for full closure is about tens of microseconds . Direct comparison from experiments under similar transmembrane potentials ( VTM = ±1 V ) is not available , as lipid membranes do not withstand application of high voltages in experiments . However , we note that it was experimentally demonstrated that the time required for a bacterial porin to gate fully decreases exponentially as the applied voltage increases [6 , 38] . Thus the estimated closing constant of Omp-Pst2 could be plausible . In the rest of our voltage-applied systems , stronger attachment of L3 loop onto the barrel wall was observed and the porins of study failed to undergo gating . We thus propose that the strength of this hydrogen bond network determines voltage sensitivity in the two porins from P . stuartii . Based on our simulations , the most critical residues for porins ( Omp-Pst2/1 ) gating are the acidic niches residues E258/E266 and D312/D321 , on the one hand , and W111/W114 on the other hand . Sequence alignment with E . coli porins of known structures shows good conservation of these residues . First , all E . coli porins feature an aspartic acid at the position equivalent to D312 in Omp-Pst2 ( niche II; viz . D312 , D315 and D302 in OmpF , OmpC and PhoE , respectively ) . While E258 is not strictly conserved in terms of sequence position , negatively charged residues emanating from the barrel wall are found at other locations behind the L3 loop in E . coli porins , where they could contribute to an equivalent of niche I ( E296 in OmpF; E260 and D299 in OmpC; E248 and D287 in PhoE ) ( S19B and S19C Fig ) . Also , an aromatic residue is found at the L3 tip of all E . coli porins ( F118 , F110 and F111 in OmpF , OmpC and PhoE , respectively ) ( S19A Fig ) . That residues critical for the voltage-gating of Omp-Pst2 are highly conserved in E . coli porins suggests that our gating model could apply to them as well . Furthermore , the lower sensitivity to transmembrane potential of OmpF , OmpC and PhoE ( ~155 , ~185 and ~135 mV , respectively ) correlates with the higher stabilization of their L3 loop onto the barrel wall . E . coli porins indeed all feature an additional glutamic acid at the L3 tip ( E117 , E109 and E110 in OmpF , OmpC and PhoE , respectively ) , which reinforces the attachment of their L3 tip to the barrel wall by one ( Y22 in PhoE ) or two hydrogen bonds ( Y22/Y22 and Y310/Y313 in OmpF/OmpC ) . Thus , our proposal that it is the strength of the HBN attaching the tip of L3 onto the barrel wall that determines the likelihood of gating—and thus the sensitivity to voltage—is supported by examination of E . coli porins structures and by their Vc . Further support to our proposed model for gating comes from mutagenesis data gathered on OmpF , which , similar to Omp-Pst2 , is cation-selective . Replacement of acidic residues L3 ( E117C ) and niches residues ( D312N , E296L , E296A and E296Q ) by neutral ones , or stabilization of L3 by disulphide-bridge tethering onto the barrel wall ( between E117C and A333C ) [19 , 22] both leads to increased Vc in OmpF . Reversely , replacement of basic residues in the anti L3 region ( K16A , K16D , R42C , R82C , R132A , and R132D ) [17 , 18] leads to decreased Vc , as expected from a heavier trafficking of cations at the CZ—and thus an easier binding behind L3 . That disulphide-tethering of L3 onto the barrel-wall does not suppress gating but merely increases the required voltage [20] is in accordance with our observation that gating does not require major conformation changes in L3 , but a mere protrusion of an aromatic side chain in the channel lumen ( here , W111 from Omp-Pst2 ) . It needs to be acknowledged that fitting into our gating model mutagenesis data from anion-selective PhoE is less straightforward . While the critical residues are conserved ( S19D Fig ) , mutagenesis data are pointing to the opposite direction , as discussed earlier [10 , 15] . Mutation of residues involved in the attachment of the L3 tip to the barrel wall ( E110C ) or in the constitution of the acidic niche under L3 ( E302C ) indeed induce a decrease in Vc , while mutations of anti-L3 residues ( R37C , R75C , R37C/R75C , K18C ) all provoke an increase in Vc [15] . We believe that similar calculations need to be conducted on PhoE to provide an atomic level understanding of its possibly peculiar gating behaviour . A tormenting question is whether voltage gating is a mere experimental artefact or underlies a functionally relevant regulatory mechanism . In early days , it was proposed that voltage gating could be a means by which porins that are mistakenly inserted into the inner membrane keep in the closed state [39 , 40] , because the Donnan potential of the outer membrane is ≤ -30 mV [39] , while the inner membrane displays a transmembrane potential of about 160–200 mV ( i . e . a value close to the Vc of most porins ) . Yet , the observation that Omp-Pst2 displays a strong propensity to gate ( low Vc ) suggests that this porin could , in the physiological context , rest in the gated state . That the gating propensity is asymmetric , and that this asymmetry correlates with the transportation of cations in an unfavourable direction—i . e . against the gradient of transmembrane potential—furthermore raises the question as to whether voltage gating conceals a role , for this porin , in the regulation of the cationic content of the periplasm . In humans , the primary habitat of P . stuartii , is the urinary tract , where the ammonium ( NH4+ ) concentration is high . As P . stuartii features a urease activity that degrades urea into ammonia and carbonate , the higher propensity of Omp-Pst2 to transport cations from the intracellular to the extracellular side could participate in cleansing the bacterial periplasm of abounding cations . That Omp-Pst2 display a strong propensity to gate when the cationic flux occurs from the extracellular to the intracellular side furthermore suggests an active participation in the regulation of cationic fluxes across the outer-membrane . As new contributions of porins to bacterial development are discovered [41 , 42] , the functional meaning of voltage gating requires to be re-visited . In this context , molecular dynamics simulations hold the promise of allowing functional insights at the atomic level of resolution , as shown by the present work .
The crystal structure of Omp-Pst1 and Omp-Pst2 were solved at 3 . 3 Å and 2 . 2 Å respectively ( PDB code 4D64 and 4D65 respectively ) . The crystal packing revealed an organization as dimers of trimers , viz . two symmetric , face-to-face trimmers . Chain A , B , and C of the crystal structures were taken out and used as starting models for the simulations . To create the lipid bilayer whereto embed our porins during the simulations , we used dimyristoylphosphatidylcholine ( DMPC ) , as this lipid is of adequate length to model the thickness of a bacterial outer-membrane and has been used in a number of simulations studies on other bacterial porins [25 , 43] . The bacterial outer-membrane is asymmetric , featuring lipopolysaccharides ( LPS ) on its extracellular leaflet . In our simulations , we did not use LPS , given that both experimental data collected on Omp-Pst1 and Omp-Pst2 reconstituted in LPS-containing bilayers and studies on other porins [44] established that the translocation properties of porins are not influenced by the presence of LPS . The trimeric structure of Omp-Pst1 and Omp-Pst2 were thus inserted into a pre-equilibrated lipid bilayer consisting of 512 DMPC by using the Perl script InflateGRO [45] . After the insertion , 58 DMPC were deleted , on the basis of steric clashes with the protein . In the final configuration , the area per lipid ( APL ) reached 60 Å2 . Lipid-embedded Omp-Pst1 and Omp-Pst2 were then wrapped with 51 , 943 and 51 , 976 water molecules , respectively , in a cubic simulation box of 13 . 7 × 13 . 7 × 13 . 7 nm3 . Potassium and chloride ions were added to reach a salt concentration of 1 M , and adjusted to provide neutral simulation systems . The standard protonation state at neutral pH was used for charged residues . Molecular dynamics simulations were performed using GROMACS 4 . 5 package [46] , and all-atom CHARMM force field for proteins [47] and lipids [48] . The TIP3P model [49] was used for water molecules . In all simulations , periodic boundary conditions were used in x , y and z directions . Electrostatic interactions were computed using the particle mesh Ewald ( PME ) method [50] . A Fourier spacing of 0 . 11 nm was used to avoid spurious drifts in the center of mass of the system [28 , 30] . The LINCS method [51] was used to restrain bond lengths , allowing integration steps of 2 fs and updating of the neighbor list every 5 fs ( cut-off distance of 1 . 2 nm ) . Lennard-Jones and Coulomb cut-off distances were set to 1 . 4 and 1 . 2 nm , respectively . To prepare the simulation systems , we used the following procedure . First , the initial configurations of the lipid-embedded Omp-Pst1 and Omp-Pst2 were optimized by four steps of energy minimization , during which positional restrains were imposed on i/ all none-hydrogen atoms , ii/ main-chain atoms , iii/ Cα atoms and iv/ no atoms . Thereby , maximum forces lesser than 100 kJ . mol-1 . nm-1 was attained . The two systems were then thermalized to 310 K in six steps of NPT ensemble , each lasting 500 ps . In the NPT ensembles , the pressure was kept constant at 1 bar independently on the x-y plane ( containing the lipid bilayer ) and the z-axis direction ( normal to the lipid bilayer ) by semi-isotropic coupling to a Parrinello-Rahman barostat with τP = 1 . 0 ps and a compressibility of 4 . 6x10-5 bar [52] , while the temperature was maintained at the target temperatures ( 50 K , 100 K , 150 K , 200 K , 250 K , and 310 K ) by weakly ( τT = 0 . 1 ps ) coupling lipids , protein and solvent separately to a V-rescale thermostat [53] . Each system was then subjected to another 1 ns NVT ensemble at 310 K . After equilibration , the two systems were coupled to a homogenous electrostatic field E aligned with the z-axis , allowing for the simulations with an artificial transmembrane potential ( E = VTM/LZ ) of either +1 or -1 V ( extracellular to intracellular ) . As for controls , we also subjected the Omp-Pst1 and Omp-Pst2 simulation systems to NVT ensembles in the absence of an electrostatic field ( VTM = 0 V ) . Simulations at VTM = 0 V were carried out for 100 ns while those at VTM ≠ 0 for 500 ns each . A recapitulation of the simulations is given in S1 Table . Net ion permeation events and residency times within the channels were calculated using g_flux tool [54] . Ions that bound in niche I and II and the occupancy of the two niches were counted using the GROMCAS tool of g_select . Ions were required to move from one side of the lipid to the other to count as a complete permeation event . The selectivity and conductance of Omp-Pst1 and Omp-Pst2 were determined by calculating the current-voltage relationship under different applied electric fields . In each simulation , the current contributed either by K+ or Cl- ions was determined through linear regression of the net ion crossing at every time interval of Δt = 50 ns . At each voltage , the total current was the sum of K+ and Cl- currents , and conductances were calculated as the slope of the current-voltage curves [55] . Voltage-specific ion selectivity was calculated by using the ratio of K+ and Cl- current at each voltage . Simulated ion diffusion coefficients were calculated from our 100 ns simulations at VTM = 0 . Diffusion coefficients obtained for K+ and Cl- were 1 . 83 cm2/s and 1 . 86 cm2/s in the simulation of Omp-Pst1 , and 1 . 72 cm2/s and 1 . 95 cm2/s in the simulation of Omp-Pst2 . The ratio of the experimentally determined diffusion coefficients ( 1 . 96 cm2/s for K+ and 2 . 02 cm2/s for Cl- [56] ) to the calculated ones was used to get the corrected conductance . In practice , the K+ and Cl- currents were scaled by the ratio before calculation of the slope of the I-V curves . Ion densities within the channels ( S4 Fig ) were calculated using the density calculation module in MDAnalysis tool [57] . Hydrogen-bonding network analysis was performed using VMD ( Figs 4A and 5A ) . Sequence alignments were performed using CLUSTALW . PhoE residue numbering was adjusted to fit the nomenclature used in previous papers [10 , 15 , 18] . Planar lipid bilayers were formed according to the monolayer technique of Montal and Mueller [58] . The bilayer was formed across a hole that was about 50 μm in diameter in a 25 μm thick polytetrafluoroethylene ( PTFE ) film . A lipid bilayer was prepared by spreading 1 μL of a 5 mg/mL solution of 1 , 2-diphytanoyl-sn-glycero-3-phosphocholine in a solvent mixture of n-pentane in the aperture . Ag/AgCl electrodes were used to detect the ionic currents . The electrode on the cis side of the cell was grounded , whereas the other one on the trans side was connected to the headstage of an Axopatch 200B amplifier . Purified detergent-solubilized porins ( 1 ng/mL ) were added to the cis side of the chamber in presence of 1M KCl , 20mM PO4 pH 4 and inserted into the bilayer membrane by applying a 150–200 mV voltage . The recordings were made after diluting the same chamber with 1M KCl , 10mM HEPES pH 7 .
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Porins are the main conduits for hydrophilic nutrients and ions uptake into the periplasm of Gram-negative bacteria . Their translocation permeability is determined by the amino-acid distribution on their extracellular loop L3 . Bacterial porin channels have long been known to undergo step-wise gating , under the application of a transmembrane potential . Yet the exact molecular mechanism by which gating is achieved and the exact relevance of this evolved characteristic remain elusive . In the present study , we report on electrophysiology experiments and molecular dynamics simulations on the two general-diffusion porins of Providencia stuartii , Omp-Pst1 and Omp-Pst2 . Our results show that gating in Omp-Pst2 occurs as the result of L3 displacement , which follows from the binding of cations in acidic niches between L3 and the barrel wall and effects in exposing the side chain of a highly conserved aromatic residue at the tip of L3 in the channel lumen . That Omp-Pst2 displays asymmetric voltage sensitivity and that the likelihood of gating is increased when cations transit from the extracellular to the intracellular side suggests voltage-gating underlies a regulatory role in bacterial homeostasis . Rational antibiotic-design strategies based on the maximization of antibiotic penetration and accumulation at their target sites , should take this role into account .
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[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[] |
2015
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Understanding Voltage Gating of Providencia stuartii Porins at Atomic Level
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Centromere is a specialized chromatin domain that plays a vital role in chromosome segregation . In most eukaryotes , centromere is surrounded by the epigenetically distinct heterochromatin domain . Heterochromatin has been shown to contribute to centromere function , but the precise role of heterochromatin in centromere specification remains elusive . Centromeres in most eukaryotes , including fission yeast ( Schizosaccharomyces pombe ) , are defined epigenetically by the histone H3 ( H3 ) variant CENP-A . In contrast , the budding yeast Saccharomyces cerevisiae has genetically-defined point centromeres . The transition between regional centromeres and point centromeres is considered as one of the most dramatic evolutionary events in centromere evolution . Here we demonstrated that Cse4 , the budding yeast CENP-A homolog , can localize to centromeres in fission yeast and partially substitute fission yeast CENP-ACnp1 . But overexpression of Cse4 results in its localization to heterochromatic regions . Cse4 is subject to efficient ubiquitin-dependent degradation in S . pombe , and its N-terminal domain dictates its centromere distribution via ubiquitination . Notably , without heterochromatin and RNA interference ( RNAi ) , Cse4 fails to associate with centromeres . We showed that RNAi-dependent heterochromatin mediates centromeric localization of Cse4 by protecting Cse4 from ubiquitin-dependent degradation . Heterochromatin also contributes to the association of native CENP-ACnp1 with centromeres via the same mechanism . These findings suggest that protection of CENP-A from degradation by heterochromatin is a general mechanism used for centromere assembly , and also provide novel insights into centromere evolution .
Chromatin is organized into different chromatin domains . Centromere is a specialized chromatin domain that plays a vital role in chromosome segregation [1–3] . In most eukaryotes , centromere is surrounded by the epigenetically distinct heterochromatin domains . Peri-centromeric heterochromatin is usually composed of tandem DNA repeats that are organized into condensed , transcriptionally silenced structures . The region contains the conserved hallmark of heterochromatin , histone H3 lysine 9 ( H3K9 ) methylation [1] . Heterochromatin has been shown to contribute to centromere function [4–9] . But its exact role in centromere assembly remains unknown . The vast majority of eukaryotes have “regional centromeres” , which contain large regions of DNA , hosting multiple microtubule nucleation sites . DNA sequence in regional centromeres is highly variable among species . Sequence-independent epigenetic mechanisms are crucial for specification of “regional” centromeres . A conserved histone 3 ( H3 ) variant protein , CENP-A , serves as the epigenetic mark for centromeres [1–3] . The histone variant partially replaces the canonical histone H3 in centromeres , and is assembled into unique CENP-A nucleosomes that promote kinetochore assembly . CENP-A proteins across species share a conserved C-terminal histone fold domain containing the CENP-A targeting domain ( CATD ) , while the N terminus tails are vastly divergent in sequence and length [10–12] . It has been proposed that CENP-A evolves adaptively in concert with the centromeric sequence [8 , 13] . Unlike other eukaryotes , the budding yeast Saccharomyces cerevisiae contain genetically defined “point centromeres” . Point centromeres are only ~125 base pairs in length , and this short DNA sequence is necessary and sufficient for centromere formation . The centromeric DNA is recognized by specific DNA-binding proteins that recruit the CENP-A homolog , Cse4 , to drive kinetochore assembly [1 , 14 , 15] . Noticeably , point centromeres lack peri-centromeric heterochromatin . The transition between regional centromeres and point centromeres is considered as one of the most dramatic evolutionary events in centromere evolution [8] . It has been shown that major heterochromatin proteins and RNAi machinery were lost in budding yeast [16 , 17] . The evolution event appears coupled with the emergence of point centromeres . This raised an important question of how epigenetically-defined regional centromere that requires heterochromatin machinery evolved to a genetically defined point centromere that relinquishes the requirement for these proteins [8] . Mistargeting of CENP-A to non-centromeric regions has been reported to produce ectopic kinetochores , and trigger chromosome instability and aneuploidy in multiple organisms . Overexpression of CID , the Drosophila CENP-A , promotes formation of ectopic centromere , leading to chromosome missegregation and growth defects [18] . Interestingly , ectopic centromeres prefer to assemble at regions near heterochromatin [19] . In budding yeast S . cerevisiae , mislocalization of Cse4 also results in defects in chromosome segregation [20] . Overexpression and mispositioning of CENP-A have been found in many cancer cells and contribute to carcinogenesis [21–25] . One of the conserved mechanisms used to prevent CENP-A mispositioning is ubiquitin-dependent proteolysis . Proteolysis of CENP-A in both budding yeast and Drosophila ensures the exclusive restriction of CENP-A at centromeres [26–28] . Both the N-terminus and the CATD domain in the conserved C-terminus in the budding yeast Cse4 are important for proteolysis of the protein [29 , 30] . A recent study showed that the FACT ( facilitates chromatin transcription/transactions ) complex mediates the degradation of Cse4 by destabilizing the ectopic Cse4 nucleosomes and facilitating the interaction of the E3 ubiquitin ligase , Psh1 , with Cse4 [31] . On the other hand , the kinetochore has been reported to protect Cse4 at point centromeres from ubiquitin-mediated proteolysis [26 , 32] . The association of Scm3/HJURP , a CENP-A specific chaperone , was also suggested to play a role in guarding Cse4 at centromeres [27] . In addition , the kinetochore is implicated in protecting of CENP-A degradation in regional centromeres in Candida albicans [33] . However , how CENP-A is protected from degradation especially at regional centromeres remains poorly understood . In contrast to budding yeast , evolutionarily divergent fission yeast ( Schizosaccharomyces pombe ) contains regional centromeres defined by the CENP-A homolog , Cnp1 [1 , 34] . Centromeres in fission yeast are also flanked by heterochromatin . Overexpression of Cnp1 can cause chromosome missegregation during both mitosis and meiosis [35–37] . We have found that the N terminus of Cnp1 is important for its ubiquitin-mediated degradation [37] . In addition , centromeres and peri-centromeric heterochromatin are localized near the nuclear membrane periphery , where proteasome subunits , such as Rpt3 and Mts2 , and a proteasome anchor protein Cut8 are enriched [38–40] . The pericentromeric heterochromatin in fission yeast is marked by H3K9 methylation . Clr4 , a homolog of the mammalian histone methyltransferase SUV39H1 , mediates H3K9 methylation , which is bound by the HP1 homolog Swi6 . RNA interference ( RNAi ) plays an important role in H3K9 methylation and heterochromatin silencing [1 , 41 , 42] . It has been reported that RNAi-mediated heterochromatin is required for Cnp1 assembly at neocentromeres but is dispensable for inheritance of Cnp1 chromatin [6] . Deletion of centromere sequences in fission yeast can result in formation of ectopic centromere in heterochromatin region [5] . We and others have also shown that overexpressed Cnp1 forms ectopic loci that are often associated with heterochromatic regions [35 , 37] . But the mechanism underlying the role of heterochromatin in centromere assembly remains unknown . To study the role of peri-centromeric heterochromatin in centromere specification , we expressed the budding yeast Cse4 in S . pombe . We demonstrated that Cse4 can target to fission yeast centromeres , but overexpression of Cse4 leads to its preferential localization in heterochromatin . We showed that Cse4 in S . pombe is subject to efficient ubiquitination and degradation , resulting in its expression at low level . Using Cse4 domain-deletion mutants and also domain-swapped chimeras by swapping the N- and C-terminal domains of Cse4 and Cnp1 , we showed that the N-terminal domain of these proteins mediates their proper centromere distribution by dictating the protein level via ubiquitination . Importantly , we found that heterochromatin and RNAi promote targeting of Cse4 to centromeres by protecting Cse4 from ubiquitin-dependent degradation . Peri-centromeric heterochromatin also protects native Cnp1 at centromeres . In heterochromatin mutants , proteasomes are accumulated in centromeres , resulting in high ubiquitination of Cnp1 and its unstable association with centromeres . To our knowledge , our findings provide the first mechanistic insight into the role of heterochromatin in the regulation of centromere specification . The study advances our understanding of how different chromatin domains functionally interact , and also sheds light on centromere evolution .
To investigate how Cse4 behaves in fission yeast , Cse4 was constructed under the strong inducible promoter nmt1 in the fission yeast expression vector , pREP1 . The promoter was induced when thiamine is depleted from the media . We found that no florescent signal in wild-type ( WT ) cells carrying Cse4-GFP was detected in repressed condition . However , ~90% of cells exhibited a single GFP focus 24 hours after thiamine withdrawal ( Fig 1A , left panel ) . In S . pombe , centromeres are clustered near the spindle pole body ( SPB ) during interphase . We found that the single Cse4-GFP focus colocalizes with the CFP-tagged SPB protein Sad1 ( Fig 1B ) , indicating that Cse4-GFP is associated with centromeres . Chromatin immunoprecipitation ( ChIP ) analysis also confirmed that Cse4-GFP is enriched in the centromeric sequences ( Fig 1E ) . To test whether Cse4 can functionally substitute Cnp1 in fission yeast , Cse4 was expressed in the temperature sensitive ( ts ) mutant , cnp1-1 . When cultured at 36°C , cnp1-1 cells are non-viable , but the presence of Cse4 largely complements the growth defects , demonstrating that Cse4 is at least partially substitute Cnp1 to establish functional centromeres in fission yeast ( Fig 1C ) . We noticed that with extended overexpression , Cse4-GFP was found further enriched in centromeric regions ( Fig 1E ) , which is similar to overexpressed Cnp-GFP ( S1 Fig ) . In addition , we found more Cse4-GFP foci with extended overexpression . 79% of cells at 28-hour induction display multiple foci , commonly 2–6 , that tend to be associated with the nuclear envelope ( Fig 1A , right panel ) . Heterochromatin in fission yeast interphase cells is organized into 2–6 clusters next to the nuclear envelope; cells expressing mCherry-tagged Swi6 thus also exhibit 2–6 foci [37 , 43] . We found that these Cse4-GFP foci largely colocalize with mCherry-Swi6 ( Fig 1D ) , suggesting that overexpressed Cse4-GFP preferentially binds to heterochromatic regions . This is reminiscent of Cnp1’s enrichment to heterochromatic domains when overexpressed [35 , 37] . We also performed ChIP qPCR analysis to quantitate the relative enrichment of Cse4-GFP to heterochromatin regions . Consistent with the microscopic observations , Cse4-GFP is absent in pericentromeric and sub-telomeric heterochromatin regions with short overexpression ( Fig 1E ) . But when the induction of Cse4-GFP is extended for additional 4 hours , Cse4-GFP is enriched in these heterochromatic regions ( Fig 1E ) . To further determine whether Cse4-GFP foci are formed by stable chromatin incorporation of Cse4 , we performed in situ chromatin-binding assays . GFP-Swi6 in the heterochromatin mutant dos1Δ ( also known as raf1Δ ) [44–46] appears diffuse in the nucleus due to the dissociation of Swi6 from chromatin , and the GFP signal can be readily washed away by Triton X-100 . But we found that Cse4-GFP is resistant to Triton X-100 extraction ( Fig 1F ) , suggesting that Cse4-GFP is stably associated with chromatin . We have shown previously that at the repressed condition , a single GFP focus formed in almost all cells carrying pREP1-Cnp1-GFP due to leaky expression from the promoter [37] . In contrast , no GFP signal was observed in cells carrying pREP1-CSE4-GFP at the same condition ( Fig 2A and 2B ) . In addition , at 24-hour induction , ~90% of cells carrying Cse4-GFP contain a single focus , whereas nearly all the cells overexpressing Cnp1-GFP exhibit multiple bright foci ( commonly 6–30 ) , or widespread fluorescent signal ( Fig 2A and 2B ) . Moreover , it takes ~20% more induction time to have Cse4-GFP signal sufficiently induced compared to Cnp1-GFP ( S2A Fig ) , and the additional induction time does not increase their mRNA expression ( S2B Fig ) , suggesting at the same time point Cse4-GFP protein level is lower than Cnp1-GFP . These observations prompted us to examine the level of Cse4 and Cnp1 in these conditions by western blot assays , and we found that protein level of Cse4 is significantly lower than Cnp1 ( Fig 2C ) . Overexpression of Cnp1 in fission yeast causes chromosome mis-segregation and growth defects [37] , while overexpression of Cse4 in budding yeast cells displays little phenotype [26 , 47] . We next examined the effect of overexpression of Cse4 in fission yeast . We found that overexpressing Cse4 in fission yeast leads to no obvious growth defects ( Fig 2D ) . Consistently , cells overexpressing Cnp1 is highly sensitive to microtubule-destabilizing drug , thiabendazole ( TBZ ) , but the drug only has minor effect on growth of cells overexpressing Cse4 ( Fig 2E ) . The significantly reduced expression level of Cse4 may , at least partially , explain why overexpressing Cse4 does not exert growth defects as Cnp1 overexpression . Both low level of transcription and high rate of protein degradation could contribute to the observed low expression level of Cse4 . We first analyzed the transcription level of Cse4-GFP by RT-qPCR , and found it similar to the level of Cnp1-GFP expressed at the same condition ( Fig 3A ) . We next investigated the protein stability of Cse4 and Cnp1 by western blotting . We found that while Cnp1 level persists up to 4 hours after treatment with cycloheximide , an inhibitor of protein synthesis , Cse4 is quickly degraded ( Fig 3B ) . Cse4 in budding yeast is regulated by ubiquitin-mediated proteolysis [26 , 27] . To examine whether Cse4 in fission yeast is also subjected to the degradation pathway , we expressed Cse4-GFP level in cells carrying a ts mutant of a 19S proteasome regulatory subunit , mts2-1 . After 4 hours of exposure to the restrictive temperature , Cse4-GFP protein level is significantly accumulated , indicating that Cse4 in fission yeast is also regulated by ubiquitin-mediated proteolysis ( Figs 3C and S3 ) . To examine the extent to which Cse4 is ubiquitinated , we performed affinity pull-down assays using TUBEs ( Tandem Ubiquitin Binding Entity ) in mts2-1 cells expressing Cnp1-GFP or Cse4-GFP . We observed a laddering pattern for both Cnp1 and Cse4 after immunoprecipitation with TUBEs , whereas the control sample immunoprecipitated with empty agarose beads does not show the laddering pattern , indicating that both proteins are polyubiquitylated ( Fig 3D ) . Furthermore , the laddering pattern in cells expressing Cse4-GFP is considerably enhanced , compared with Cnp1-GFP ( Fig 3D ) . This finding was corroborated by a reverse pull-down assay , where Cnp1-GFP and Cse4-GFP were immunoprecipitated with an antibody against GFP and analyzed by western blot analysis using a pan ubiquitin antibody ( Fig 3D ) . We therefore concluded that Cse4 in fission yeast is subject to efficient ubiquitin-dependent degradation , explaining why expression level of Cse4 is significantly lower than the level of Cnp1 in fission yeast . Despite sharing a conserved C terminal domain , the N terminus tails of Cse4 and Cnp1 are remarkably different . Cse4 contains a relatively long N-terminus consisting of 130 amino acids , whereas the N-terminus of Cnp1 has only 20 amino acids ( S4 Fig ) . We hypothesized that the N terminus tails of these proteins may contribute to the difference in stability between Cse4 and Cnp1 in fission yeast . To determine the role of Cse4’s N terminal domain , we created a strain carrying N-terminal deleted Cse4-GFP under the nmt1 promoter , Cse4-NΔ-GFP . We found that deletion of the N-terminal domain of Cse4 results in largely diffused but brighter GFP signal in the nucleus after the 22-hour induction , whereas most of cells carrying Cse4-GFP contain a single GFP focus at the same time ( S5A Fig ) . This , together with western blot analysis ( S5B Fig ) , demonstrated that N-terminal deleted Cse4-GFP exhibits a higher protein level . To further examine the role of Cse4’s N terminus , we fused it with the C-terminal domain of Cnp1 to generate Cse4N-Cnp1C-GFP . We also constructed a chimera containing the N-terminal domain of Cnp1 and the C-terminus of Cse4 , Cnp1N-Cse4C-GFP ( Fig 4A ) . We found that most cells overexpressing Cse4N-Cnp1C-GFP contain a single focus 24 hours after induction , similar to cells expressing Cse4-GFP under the same condition . In contrast , expression of Cnp1N-Cse4C-GFP at the same condition results in multiple foci , or widespread signal throughout the nucleus ( Fig 4B ) , which phenocopied cells overexpressing Cnp1-GFP [37] . Consistent with this , the level of Cse4N-Cnp1C-GFP is significantly lower than Cnp1N-Cse4C-GFP ( Fig 4C ) . The N-terminus of Cse4 and Cnp1 thus dictates their protein level and proper distribution . The N-terminus of both Cse4 and Cnp1 has been implicated in ubiquitin-dependent proteolysis [29 , 37] . Accordingly , we found that Cse4-NΔ-GFP expression is more stable by our protein stability assays ( S5C Fig ) . Furthermore , our in vivo ubiquitination assays revealed that N-terminus-deleted Cse4 is less ubiquitinated than WT Cse4 ( S5D Fig ) , demonstrating the importance of the domain in ubiquitin-mediated Cse4 degradation in fission yeast . We speculate that the long N terminus of Cse4 may facilitate the recognition by ubiquitin ligases that lead to higher ubiquitination . While regional centromeres are flanked with heterochromatin , point centromeres in budding yeast lack pericentromeric heterochromatin . To investigate whether the centromeric association of Cse4-GFP is affected by heterochromatin , we examined the distribution of Cse4-GFP in the clr4Δ mutant . Remarkably , we found that Cse4-GFP in clr4Δ is completely diffused in almost all cells observed ( Figs 5A and S6 ) . We also observed the same diffuse Cse4-GFP pattern in the Dicer mutant , dcr1Δ ( Figs 5A and S6 ) . To determine whether diffused Cse4-GFP in the clr4Δ is chromatin-bound , we performed in situ chromatin-binding assays . We found that , whereas Cse4-GFP in the wild-type cells forms stable foci that are resistant to Triton X-100 extraction , Cse4-GFP in clr4Δ is effectively washed away from the nucleus by the treatment ( Fig 5B ) , indicating that Cse4-GFP in the clr4Δ does not assemble stably with chromatin . These data suggest that heterochromatin promotes chromatin assembly and centromeric targeting of Cse4 in fission yeast . Aside from lacking centromeric targeting , we also observed that the Cse4-GFP signal is very faint in clr4Δ cells with Cse4-GFP fully induced for 28 hours , indicating that the protein is expressed at low level . Consistent with this , at 24-hour induction , while most WT cells contain a single focus of Cse4-GFP , no GFP signal is detectable in most clr4Δ cells ( Fig 5C ) . The induction time for Cse4-GFP in clr4Δ to yield GFP signal in nearly all cells is ~20% longer than in WT ( S2A Fig ) . Western blot analysis confirmed that Cse4-GFP protein level in clr4Δ is reduced by at least 50% ( Fig 5D ) . To test the stability of Cse4 in clr4Δ cells , we analyzed protein levels of Cse4-GFP over time by western blotting . We observed that after treatment of cycloheximide , Cse4 level decreases faster in clr4Δ cells than wild type , suggesting heterochromatin promotes stability of Cse4 in fission yeast ( S7 Fig ) . In addition , we found that the level of Cse4 ubiquitination is significantly enhanced in both clr4Δ and dcr1Δ by immunoprecipitation assays ( Figs 5E , 5F and S8 ) . These data indicate that RNAi and heterochromatin play a vital role in preventing ubiquitin-mediated degradation of Cse4 . Our results predict that if ubiquitin-proteasome pathway is disrupted in the clr4Δ mutant , the level of Cse4 will increase; yet due to loss of heterochromatin restriction , Cse4 can incorporate into chromatin but in a more random manner . To test this , we expressed Cse4-GFP in the clr4Δ mts2-1 double mutant . At the 23°C permissive temperature , Cse4-GFP is largely diffused in the nucleus without forming visible foci in clr4Δ mts2-1 ( Fig 5G ) . However , after 4 hours of incubation at the restrictive temperature of 37°C , which blocked proteasome activity , many distinct foci of Cse4-GFP ( commonly 6–30 ) were found and randomly distributed in the nucleus in the double mutant ( Fig 5G ) . The random distribution of Cse4-GFP foci likely results from lacking restriction by defined heterochromatin structure in the double mutant . Western blot assays confirmed that Cse4-GFP level is enhanced in clr4Δ mts2-1 cells at 37°C ( S9A Fig ) . Furthermore , in situ chromatin binding assay was performed to test the association of Cse4-GFP foci with chromatin . While diffused Cse4-GFP signal can be readily washed away by Triton-X100 in clr4Δ mts2-1 cells cultured at 23°C , Cse4-GFP foci resulting from proteasome inactivation at 37°C are stably associated with chromatin ( S9B Fig ) . These data support that heterochromatin promotes Cse4 stability and its association with centromere by protecting Cse4 from ubiquitin-dependent proteolysis . Although pericentromeric heterochromatin has been shown to be important for centromere function and identity , a previous study reported that Cnp1-GFP is still associated with centromeres in clr4 mutant [6] . In light of our findings that centromeric targeting of Cse4 is significantly impaired in clr4Δ cells , we tested to what extend Cnp1 distribution is affected by heterochromatin . We examined ~100 colonies generated by genetic cross of clr4Δ and cells expressing Cnp1-GFP driven by endogenous promoter . Largely consistent with the previous report , we found that in 80% of the colonies , Cnp1-GFP displays a single focus in clr4Δ cells . Remarkably , we also observed that 20% of colonies exhibited faint Cnp1-GFP signal diffused throughout the nucleus ( Fig 6A ) . It is to be noted that the colonies displaying total diffusion will adopt the single-focus phenotype when cultured in liquid rich medium ( YES medium ) , or maintained for generations on the plates . This suggests that the diffused Cnp1-GFP phenotype in clr4Δ cells is not stable , but nonetheless these findings indicate that heterochromatin indeed contributes to the proper localization of Cnp1 . The observation that heterochromatin is not entirely responsible for centromeric localization of Cnp1 suggests that heterochromatin works in parallel with other pathways to ensure proper positioning of CENP-A at centromeres . Consistent with this idea , while Cnp1-GFP in scm3-19 , a ts mutant of the CENP-A chaperone Scm3/HJURP , displays centromeric localization at the permissive temperature 23°C , we found that all colonies of the clr4Δ scm3-19 double mutant that we analyzed contain more than 12% of cells displaying diffused GFP signal at the permissive temperature ( S10 Fig ) . Unlike in clr4Δ cells , the diffused phenotype of Cnp1-GFP in the fraction of clr4Δ scm3-19 cells can be observed when cells are cultured on the plate and in the liquid culture , and are persistent over generations . These observations indicate while Cnp1 centromere association is mildly impaired in clr4Δ and scm3-19 single mutant cells , the defect in the double mutant is stronger , suggesting that heterochromatin may function in parallel with Scm3 to protect Cnp1 from degradation . To test whether heterochromatin affects ubiquitin-mediated Cnp1 degradation as observed for Cse4 , we first examined the protein level of Cnp1 in clr4Δ cells . We found that Cnp1 is significantly lower in clr4Δ than in WT ( Fig 6B ) . Consistent with this , we observed that after treatment of cycloheximide , Cnp1 level persists up to 2 hours in wild type cells , while in clr4Δ cells , Cnp1 is largely degraded within 1 hour , suggesting heterochromatin promotes stability of Cnp1 in fission yeast ( Fig 6C ) . Next , we performed affinity pull-down using TUBEs with mts2-1 strains expressing Cnp1-GFP in wild type and clr4 mutant backgrounds . Upon immunoprecipitation with agarose–TUBEs , we observed a minimal smearing pattern for Cnp1-GFP expressed in wild type cells . However , in clr4Δ cells , we found an enhanced smearing pattern in the high molecular mass region , indicative of polyubiquitination ( Fig 6D ) . Similar pattern is observed with the reverse pull-down assay ( S11A and S11B Fig ) . These data support that heterochromatin structure protects native CENP-A from ubiquitin-mediated degradation . We speculate that the peri-centrimeric heterochromatin domains may serve as a protective environment to restrict access of proteasome machinery to centromere . This model predicts that proteasome proteins are enriched at the centromere regions in heterochromatin-disrupted cells . We examined the level of myc-tagged Mts2 , the19S proteasome subunit , in centromeres by ChIP . We first confirmed that Cnp1 protein level is indeed accumulated in mts2-1 cells when Mts2 function is blocked by incubation at non-permissive temperature , indicating that the degradation of Cnp1 requires Mts2 ( Fig 7A ) . Our ChIP assays showed that Mts2 is highly enriched in centromeres in clr4Δ cells relative to wild type ( Fig 7B ) , demonstrating that heterochromatin is important for preventing the association of proteasome machinery with centromeres . We also found that the level of Mts2 is increased at pericentromere and subtelomeric regions in the absence of heterochromatin ( S12 Fig ) .
One of the most noticeable features of regional centromeres is that they are embedded in heterochromatin [1 , 8] . Here we demonstrated that heterochromatin mediates centromere specification by preventing ubiquitin-mediated degradation of CENP-A at centromeres . Our results reveal a previously unrecognized mechanism of heterochromatin in the regulation of centromeres , and represent a significant advance toward our understanding of the interaction between different chromatin domains . This work also provides insights to the dramatic evolutionary transition between regional centromeres and point centromeres . Although centromere is vital for all eukaryotic organisms studied to date , it is considered as one of the most rapidly evolving sequences of the genome [8 , 48] . CENP-A is considered as the epigenetic mark for specifying centromeres [1–3 , 49] . How CENP-A is precisely positioned to centromere remains poorly understood . Unlike regional centromeres , budding yeast contains a point centromere defined by the underlying DNA sequence [14 , 15] . In this study we expressed budding yeast CENP-A homolog Cse4 in the fission yeast , and found that despite having vastly different N-terminus tails , Cse4 targets to centromere and can at least in part substitute Cnp1 . Previous reports have also shown that Cse4 can targets to centromeres when expressed in human cells [50 , 51] . These data suggest that the function of CENP-A is largely conserved through evolution . Notably , overexpression of Cse4 leads to its association with heterochromatin , reminiscent of the behavior of overexpressed Cnp1 [37] . However , we found that Cse4 protein level is significantly lower than that of Cnp1 expressed in the same condition . Further experiments demonstrate that Cse4 is subject to efficient ubiquitin-mediated proteolysis , which explain the low level of Cse4 in fission yeast . Cse4 in budding yeast is also strongly regulated by ubiquitin-mediated degradation , and we speculate this which may at least partially explain why overexpression of Cse4 in budding yeast leads to no obvious defects [26 , 47] . We showed that deletion of N terminus of Cse4 results in increased level of Cse4 and diffuse nuclear distribution . Our in vivo ubiquitination assays indicated that ubiquitination is much reduced in the N-terminus-deleted Cse4 . Our domain-swap experiments confirmed that N terminus of Cse4 is largely responsible for its low protein stability . The C-terminus in Cse4 has also been shown to be subject to ubiquitination [30 , 52] . It is likely that the C-terminal domain of Cnp1 is also ubiquitinated . Since the domain is highly conserved , the terminus may have minor contribution to the difference in expression level of the two proteins . Although both Cse4 and Cnp1 are subject to ubiquitin-mediated proteolysis , higher level of ubiquitination is observed for Cse4 . We speculate that the long N terminus tail of Cse4 may present stronger affinity for E3 ligase , resulting in fast protein turnover rate for Cse4 . Together , our results showed that ubiquitination at N-terminus of CENP-A plays an important role in regulating its stability , which in turn governs its proper distribution in centromeres . Remarkably , we found the centromeric association of Cse4 is completely disrupted when heterochromatin and RNAi are impaired in fission yeast . In addition , we found that Cse4 is expressed at lower level in the heterochromatin mutants relative to wild type . Cse4 in these mutants is less stable and subject to enhanced ubiquitination . These results indicate that heterochromatin acts to protect Cse4 in centromeres from ubiquitin-mediated degradation . The alternative model is that centromere targeting may be affected in clr4Δ , resulting in the degradation of soluble pools of CENP-A that become unstable when not bound to chromatin . Although we cannot exclude the possibility , our data do not support this model: we found that Cse4-GFP appears diffused in the clr4Δ mts2-1 double mutant at the permissive temperature , but after blocking proteasome activity at the restrictive temperature , many distinct foci of Cse4-GFP are formed in the nucleus . The association of these Cse4-GFP foci with chromatin in the double mutant appears stable , as shown by our in situ chromatin binding assay . Furthermore , our ChIP assays demonstrated the19S proteasome subunit Mts2 is drastically enriched in centromeres in the clr4Δ mutant . This dramatic effect of heterochromatin on Cse4’s stability and localization is unexpected , given that previous reports showed that Cnp1 remains associated with centromere in the absence of heterochromatin [6 , 9] . Our closer examinations showed that some clr4Δ colonies indeed display faint , diffuse Cnp1-GFP signal in the nucleus , indicative of unstable association of Cnp1 with centromeres . We further found that in clr4Δ cells Cnp1 has reduced protein level , faster turnover rate and higher ubiquitination , similar to Cse4 in the mutant . Heterochromatin thus also protects Cnp1 from ubiquitin-mediated degradation . Consistent with our work , it has been shown that the 19S proteasome subunit Rpt3 also plays an important role in distribution of CENP-A [53] . Nevertheless , we cannot exclude the possibility that non-proteolytic functions of 19S proteasome may participate in the regulation of CENP-A positioning . The mild Cnp1 distribution defects in heterochromatin mutants suggest that redundant pathways may be involved in preventing Cnp1 degradation in centromeres . Indeed , we found that the clr4Δ scm3-19 double mutant showed the synthetic defects in Cnp1-GFP distribution . Scm3/HJURP is a conserved CENP-A chaperone that has been implicated in protecting Cse4 degradation in budding yeast [27 , 54] . On the other hand , the centromere association of Cse4 in fission yeast strongly depends on heterochromatin . It is possible that exogenous Cse4 is unable to efficiently utilize the other CENP-A protection pathway , such as Scm3 , in fission yeast . The higher turnover rate of Cse4 may also contribute to its strong dependence on heterochromatin protection . The heterochromatin-mediated safeguard mechanism for centromeres may help explain the long-standing puzzle of why centromeres are usually flanked by heterochromatin [1] . It may also provide at least partial explanation for the observation that Cse4 can localize to centromeres in HeLa cells [50 , 51] . Previous studies have shown that overexpression of CENP-A in fission yeast and Drosophila results in specific enrichment of CENP-A in heterochromatin [19 , 35 , 37] . CENP-A from budding yeast , Caenorhabditis elegans and human expressed in Drosophila is preferentially localized at pericentromeric heterochromatin [50] . Neocentromeres are often formed in heterochromatic regions in a variety of organisms [5 , 55–60] . In an especially dramatic case , a heterochromatin block without native centromeres can exhibit centromere activity in Drosophila [61] . Heterochromatin is also important for centromere localization of CENP-A in Neurospora crassa and mouse cell lines [62 , 63] . We suggest that protection of CENP-A from degradation by heterochromatin may be a common mechanism used for centromere assembly . How heterochromatin protects CENP-A from ubiquitin-mediated degradation remains to be elucidated . Using a repeat-specific reporter , we recently showed that the tandem arrays at pericentromeric heterochromatin in fission yeast are organized into a specific three-dimensional architecture [7] . We propose that pericentromeric heterochromatin forms a distinct higher-order structure that restricts the access of ubiquitin-proteasome machinery to centromeres , which in turn avoids degradation of CENP-A in the region ( Fig 7C ) . The unique spatial architecture of pericentromeric heterochromatin may account for why CENP-A prefers this region rather than other heterochromatin regions . How the dramatic evolutionary transition between regional centromeres and point centromeres occurred is a fascinating but unsolved mystery in centromere evolution [8] . The gaining of point centromere and the loss of heterochromatin and RNAi machinery appear to occur concomitantly during the evolution [8] . We speculate that lacking the protection of Cse4 by pericentromeric heterochromatin in budding yeast may contribute to the arising of small , compact “point” centromeres and the adaptation of the centromeres to being genetically defined . For small “point” centromeres , it is possible that the kinetochore complex and Scm3 can provide sufficient protection for centromeric Cse4 from degradation [26 , 27 , 32] . But large regional centromeres may require additional mechanisms , such as peri-centromeric heterochromatin , to prevent ubiquitin-mediated proteolysis of CENP-A at centromeres . We also noted that not all neocentromeres are assembled in or near heterochromatin [64 , 65] , suggesting that mechanisms other than heterochromatin are involved in neocentromere formation . It will be interesting in future studies to identify these mechanisms , which may provide important new insights into centromere specification and evolution .
Standard media and genetic analysis for fission yeast were used [66] . Cse4 , Cnp1 , Cse4-NΔ , and domain-swap constructs were cloned into the pREP1 vector under the nmt1 promoter in frame with GFP . Fission yeast strains used in this study are listed in the Supporting Information S1 Table . in situ chromatin-binding assays were performed as described previously [37 , 67] . Briefly , log-phase cells were collected and incubated at 32°C in ZM buffer ( 50 mM sodium citrate pH 5 . 6 , 1 . 2 M sorbitol , 0 . 5 mM MgAc , 10 mM dithiothreitol , 2 mg/ml Zymolase ) for 30 min . After centrifugation , cells were then washed twice with STOP buffer ( 0 . 1 M MES pH 6 . 4 , 1 . 2 M sorbitol , 1 mM EDTA , 0 . 5 mM MgAc ) . Cells were resuspended with EB buffer ( 20 mM PIPES–potassium hydroxide , pH 6 . 8 , 0 . 4 M sorbitol , 2 mM MgAc , 150 mM KAc ) ±1% Triton X-100 at room temperature for 7 min . Following fixation with 3 . 7% formaldehyde and 10% methanol , cells were examined by DeltaVision imaging system . Cells were imaged using the DeltaVision System ( Applied Precision , Issaquah , WA ) . Images were taken as z-stacks of 0 . 2-μm increments with an oil immersion objective ( ×100 ) . Standard DAPI staining and analysis methods for fission yeast nuclei were used . Exponentially growing mts2-1 cells carrying indicated pREP1 construct were induced in minimum media without thiamine . After inductions were confirmed by visualizing GFP signal under microscope , cells were incubated in 37°C for 4–6 hrs to inactivate proteasome . Cell lysates were prepared in 1x lysis buffer supplemented with 20 μM MG132 ( Selleck Biochemicals ) , 20 μM PR-619 ( LifeSensors ) , 5 μM 1 , 10-phenanthroline ( LifeSensors ) , 10 μM PMSF and protease inhibitor cocktail ( Sigma-Aldrich ) . Cell extracts were then incubated with an anti-GFP antibody ( Santa Cruz , sc-9996 ) overnight at 4°C . IgG-coated magnetic beads ( ThermoFisher Scientific ) were added , followed by incubation at 4°C for 2 hrs . After immunoprecipitation , beads were washed 3 times using lysis buffer . Proteins were eluted and analyzed by western blotting using an anti-pan-ubiquitin ( Cell Signaling , P4D1 ) antibody . Ubiquitinated proteins were pulled down using Tandem Ubiquitin Binding Entities ( TUBEs ) ( LifeSensors ) according to the manufacturer’s instructions . Briefly , protein extracts prepared as described above were incubated with TUBE agarose or empty agarose beads overnight at 4°C . The bound proteins were eluted from washed beads and analyzed by western blotting using an anti-GFP antibody ( Santa Cruz , sc-9996 ) . Cell extracts from log-phase cells were prepared using standard protocols . Extracted proteins were separated on SDS-polyacrylamide gels and blotted onto PVDF membranes . Blots were probed with anti-GFP ( Santa Cruz , sc-9996 ) , anti-tubulin ( Abcam , ab6160 ) , or anti-pan-ubiquitin ( Cell Signaling , P4D1 ) antibodies . ChIP assays were carried out as described [43] . Briefly , cells were grown to log phase at 30°C , and cross-linked by treatment with 1% formaldehyde for 30 mins with gentle shaking at room temperature . Immunoprecipitation was performed with protein A agarose ( KPL ) conjugated to the anti-myc antibody ( ab32 , abcam ) and anti-GFP antibody ( ab290 , abcam ) . Precipitated DNA was cleaned by MiniElute PCR purification Kit ( Qiagen ) . Two microliters of ChIP or WCE samples were analyzed by quantitative PCR using primers specific to centromeric core region cnt3 , pericentromeric regions otr1 , and sub-telomeric regions subT . act1+ was used as the control gene . Protein stability assays were performed as described [37] . Briefly , after cells were induced in minimum media without thiamine , cycloheximide was added to a final concentration of 100 μg/ml . Lysates from cells collected at the indicated time points were prepared , and analyzed by western blotting using anti-GFP ( Santa Cruz , sc-9996 ) and anti-tubulin ( Abcam , ab6160 ) antibodies . The “0” time point refers to samples taken immediately after cycloheximide was added . RT-PCR assays were performed as described [68] . Briefly , total RNA from log-phase cells was extracted using TRizol . After treatment with DNase I ( Promega ) , ~50 ng of RNA were analyzed using a One-Step RT-PCR kit ( Qiagen ) with primers specific for GFP . act1+ was used as an internal control .
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Centromere is a specialized chromosomal domain that is essential for faithful chromosome segregation during mitosis and meiosis . In most eukaryotes , centromeres are surrounded by the densely packed heterochromatin . The exact role of heterochromatin in centromere assembly remains elusive . Centromeres in most eukaryotes are defined epigenetically by a conserved histone 3 variant , CENP-A . Ubiquitin-mediated proteolysis prevents mislocalization of CENP-A at non-centromeric regions . In this work , we demonstrated that heterochromatin and RNAi mediate centromere specification by protecting CENP-A from ubiquitin-mediated degradation . This mechanism may provide answers to long-lasting questions , such as why centromeres are usually flanked by heterochromatin , and why neocentromeres often form at heterochromatic regions . Our study also has important implications in centromere evolution . One fascinating aspect of centromere evolution is the transition between epigenetically defined “regional centromeres” and genetically defined “point centromeres” . Our findings suggest that lacking pericentromeric heterochromatin in point centromeres may contribute to the dramatic transition between regional and point centromeres in centromere evolution .
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2018
|
Heterochromatin and RNAi regulate centromeres by protecting CENP-A from ubiquitin-mediated degradation
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The parasitic liver fluke Fasciola hepatica infests mainly ruminants , but it can also cause fasciolosis in people , who ingest the metacercariae encysted on plants . The drug of choice to treat fasciolosis is triclabendazole ( TBZ ) , which has been on the market for several decades . This is also true for the other available drugs . Accordingly , drug-resistant flukes have been emerging at an increasing rate making it desirable to identify alternative drug targets . Here , we focused on the fact that adult F . hepatica persists in the hostile environment of the bile ducts of infected organisms . A common way to render bile acids less toxic is to conjugate them to taurine ( 2-aminoethanesulfonic acid ) . We cloned a transporter from the solute carrier-6 ( SLC6 ) family , which was most closely related to the GABA-transporter-2 of other organisms . When heterologously expressed , this F . hepatica transporter supported the high-affinity cellular uptake of taurine ( KM = 12 . 0 ± 0 . 5 μM ) but not of GABA . Substrate uptake was dependent on Na+- and Cl- ( calculated stoichiometry 2:1 ) . Consistent with the low chloride concentration in mammalian bile , the F . hepatica transporter had a higher apparent affinity for Cl- ( EC50 = 14±3 mM ) than the human taurine transporter ( EC50 = 55±7 mM ) . We incubated flukes with unconjugated bile acids in the presence and absence of taurine: taurine promoted survival of flukes; the taurine transporter inhibitor guanidinoethansulfonic acid abolished this protective effect of taurine . Based on these observations , we conclude that the taurine transporter is critical for the survival of liver flukes in the bile . Thus , the taurine transporter represents a candidate drug target .
Liver flukes of the genus Fasciola are parasitic trematodes , which infest mammals all over the world . The two most prominent representatives of the genus Fasciola are Fasciola hepatica and Fasciola gigantica , which predominates in the temperate zones and tropical Africa and Asia , respectively [1] . The resulting fasciolosis imposes a substantial economic burden because of the decrease in milk production , weight gain and wool yield and due to sudden deaths of livestock animals [2–5] . People are infested by ingestion of metacercariae encysted on plants or ingestion of water containing metacercariae . This not only can give rise to regional pockets of endemic infections , e . g . on the Bolivian Altiplano [6] , but it is also relevant to human health worldwide: conservative estimates indicate that more than 2 . 5 million people are infested and suffer from various forms of fasciolosis [7] . The ingested metacercariae excyst in the duodenum . The process is triggered by chemical cues including elevated carbon dioxide levels , requires the sequential action of host and parasite proteases [8] and is contingent on the presence of bile acids [9] . The emerging juvenile flukes also depend on their proteases to invade and penetrate the host gut wall [10] . In the peritoneal cavity , the juvenile flukes migrate to the liver—presumably using the curvature of the abdominal wall as a guidance clue [11] . They subsequently burrow through the liver capsule and feed on the tissue for several weeks , until they are mature . The mature flukes invade the bile ducts , which allows for sexual reproduction . By contrast with F . hepatica , the Chinese liver fluke Clonorchis sinensis invades the bile duct within two days after excysting . Finally , Opisthorchis viverrini , the Southeast Asian liver fluke , invades the bile ducts via retrograde migration through the coledochus . However , in spite of the differences in their life cycles , F . hepatica , C . sinensis and O . viverrini eventually face the same hostile environment of the bile . A vaccine against F . hepatica is desirable , but there are many obstacles which impede the development of an effective active immunization [12] . Accordingly , fasciolosis is treated by anthelmintic chemotherapy . Triclabendazole has been the drug of choice for more than 35 years: triclabendazole is highly active against both adult and juvenile flukes and is well tolerated by the mammalian hosts . Predictably , flukes resistant to triclabendazole have emerged [13] . Resistance is also seen with other anthelmintic compounds which kill F . hepatica , e . g . albendazole , clorsulon , closantel , oxyclozanide and nitroxynil [13–15] . Most mammals do not mount an effective immune response to liver flukes [12 , 16] . Hence adult F . hepatica can survive for many years within the biliary tract: adult worms were retrieved after 11 years from an experimentally infected sheep [17] . A case report from an elderly patient suggests that F . hepatica can also reach this age in people [18] . Motile flukes have been visualized by radiological imaging techniques in the gallbladder and in the common bile duct of patients [19] . In fact , whole bile and some bile acids such as glycine-conjugated cholic acid and dehydrocholic acid stimulate the mobility of juvenile flukes [20 , 21] . This indicates that some bile acids provide a chemokinetic or chemotactic signal . However , bile acids , in particular , deoxycholic acid , are also toxic to flukes [21] . Thus , while juvenile and adult F . hepatica are attracted by bile constituents , protective mechanisms must have evolved , which allow them to survive the toxic effects of the bile , e . g . by conjugating taurine to bile acids . Here we surmised that flukes rely on taurine transport to cope with bile acids . We cloned a candidate taurine transporter , confirmed its biochemical activity and verified that inhibition of taurine transport rendered adult flukes susceptible to killing by bile acids .
Transcripts encoding the orthologue of the human taurine transporter SLC6A6 were identified from a sequence database including mRNA data of adult F . hepatica [22] . A fragment of about 0 . 8 kB was identified via the sequence analysis and thereafter extended by amplifying the cDNA ends . An in-frame stop codon is present 5 triplets upstream of the first ATG ( S1A Fig ) , which indicates that the translation start site has been correctly identified . In addition , the sequence flanking the first ATG conforms to a canonical Kozak sequence with guanine bases at positions -6 , -3 and +4 . The 3' end of the amplified fragment also comprised the polyadenylation signal ( starting with A2746 , S1A Fig ) and the initial portion of the poly-A tail indicating that the amplified fragment covered the entire mRNA . The open reading frame of 1944 bp encodes a protein of 647 amino acids ( S1A Fig ) corresponding to a relative molecular mass of 71 , 939 Da . The sequence was deposited at GenBank with the accession number: MG674191 as F . hepatica taurine transporter ( FhTauT ) . The exon-intron boundaries were identified by comparing the obtained cDNA sequence with the genomic sequence deposited in the database WormBase ( http://parasite . wormbase . org/ ) [23 , 24] . This analysis led to the prediction that the FhTauT was encoded by 14 exons ( S1B Fig ) . We also identified the putative taurine transporter in the F . gigantica transcriptome [19]: a comparison of the predicted amino acid sequences revealed that the two transporters differed at 12 of 647 positions resulting in 98% identity ( S2 Fig ) . We aligned the amino acid sequence of the putative taurine transporter of F . hepatica to three sequences of human SLC6 family members—i . e . the serotonin transporter ( SERT/SLC6A4 ) , the GABA transporter-2 ( GAT2/SLC6A13 ) , the taurine transporter ( TauT/SLC6A6 ) —and the putative GABA-transporter-2 of the trematode C . sinensis , another liver fluke . It is evident from Fig 1 that there is extensive sequence conservation within the hydrophobic core , which is comprised of the twelve transmembrane ( TM ) segments . The structure of several SLC6 transporters is understood in atomic detail . In the crystal structure of SERT [25] , the Na+ binding site is comprised of A96 and N101 in TM1 , S336 in TM6 and N368 in TM7 ( marked by red arrow heads in Fig 1 ) : these residues ( i . e . N73 , S303 and N335 ) are conserved in the F . hepatica taurine transporter with the exception of A96 in human SERT , which is replaced by a serine ( S68 ) . Similarly , the residues forming the binding site of the second sodium ion in human SERT ( G94 and V97 in TM1 , L434 , D437 and S438 in TM8 marked by blue arrow heads in Fig 1 ) , are identical in the F . hepatica taurine transporter ( G66 , V69 , L401 , L434 , S435 ) . In SERT , the chloride ion is coordinated by Y121 of TM2 , Q332 and S336 of TM6 and S372 of TM7 ( marked by green arrow heads in Fig 1 ) . These residues are also invariant ( Y93 , Q299 , Q303 , S339 ) . SLC6 transporters have a long second extracellular loop ( EL2 ) , which is stabilized by a disulfide bond . The candidate cysteine residues ( C171 and C181 marked by magenta arrow heads in Fig 1 ) are also present in the F . hepatica taurine transporter . SLC6 transporters have a variable number of N-linked glycosylation sites; SERT has a single site ( Fig 1 ) , but the dopamine transporter DAT ( SLC6A3 ) has three [26 , 27] . We identified two asparagine residues which conform to the NXS/T-glycosylation motif in the F . hepatica taurine transporter ( N183 and N189 , marked by brown arrow heads in Fig 1 ) . Finally , with the notable exception of the neutral amino acid transporters B0AT3/SLC6A18 and B0AT1/SLC6A19 [28] , SLC6 transporters harbor a SEC24-binding site in their C-terminus: this RI/RL-motif [29–31] that is also present in the F . hepatica taurine transporter ( R579/I580 , black arrow heads in Fig 1 ) . The +2 residue ( T582 ) is hydrophilic , which predicts that the F . hepatica transporter recruits SEC24C for ER-export [31] . We used the basic local alignment search tool ( BLAST ) to identify the closest relatives of the putative F . hepatica taurine transporter . As expected , this search retrieved SLC6 transporters from other digenean trematodes—i . e . , Clonorchis sinensis , Opisthorchis viverrini , Schistosoma mansoni , Schistosoma haematobium—and taurine or GABA transporters from other species ( e . g . , the deep-sea mollusc Bathymodiolus septemdierum , Drosophila melanogaster and Homo sapiens ) . A phylogenetic tree was constructed based on the identified sequences ( Fig 2 ) . The GABA-transporter 2 from C . sinensis and the F . hepatica taurine transporter showed the highest pairwise identity of 65% . The evolutionary relation of digenean trematodes has been clarified based on the comparison of 12 proteins encoded by their mitochondrial genomes [32]: this shows that Opisthorchiidae and Fasciolidae ( and Paragonimidae ) are closely related , while flukes of the Schisostomatidae family are assigned to a separate branch . The evolutionary tree depicted in Fig 2 only recapitulated this relation in part , because the ( hypothetical ) SLC6 transporter form Opisthorchis viverrini was assigned to the same branch as the Schistosoma transporters for GABA and serotonin . However , it is worth pointing out that the biochemical activity of the majority of these transporters has only been inferred: in fact , of the non-human transporters shown in Fig 2 , it is only clear that the taurine transporter of B . septemdierum mediates the uptake of its eponymous substrate [33] . It is therefore questionable that the assigned names on the phylogenetic tree are correct: it is , for instance , difficult to understand , why the serotonin transporter of S . mansoni should be more closely related to the S . mansoni GABA-transporter-2 than to the human serotonin transporter ( Fig 2 ) . In fact , when the sequences of Platyhelminthes deposited in the WormBase database were subjected to a homology search , several close relatives to the putative FhTauT were found . The corresponding alignment is shown in S3 Fig . It is evident from this alignment that the Digenean transporters ( marked by the gray area in S3 Fig ) are more closely related to each other than those of the other Platyhelminthes . We stress that there are still several uncertainties in this comparison because it is not clear whether all these transporters are taurine transporters . For Opisthorchis viverrini we managed to identify a second transporter highly homologous to the taurine transporter of F . hepatica . In contrast to the originally identified transporter ( underlined Opisthorchis viverrini in S3 Fig ) , the second transporter clusters as would be expected to the transporter of C . sinensis and not the the transporters of the Schistosoma family . We therefore conclude , although functional data are missing , that this second transporter is the transporter for taurine . For all other platyhelminthic transporters described in Fig 2 , we could not find other transporters aligning better to the taurine transporter of F . hepatica . The sequence comparison in Fig 1 and the evolutionary tree in Fig 2 highlight the close relation between taurine and GABA transporters . The substrate specificity of the putative F . hepatica transporter was verified by heterologous expression in HEK293 cells . The human taurine transporter was used as a reference . SLC6 transporters can be tagged with fluorescent proteins on their N-terminus without affecting their activity [35] and their trafficking through the secretory pathway [36–38] . Accordingly , we introduced the cDNA encoding the F . hepatica and the human taurine transporter into mammalian expression vectors , which fused the coding sequence of CFP and YFP , respectively , in frame to the N-terminus of the transporters . This allowed for visualizing the cellular distribution of the transporters by confocal microscopy after heterologous expression in HEK293 cells: the plasma membrane was delineated by staining with trypan blue . A larger fraction of the F . hepatica taurine transporter ( Fig 3A ) than of the human transporter ( Fig 3B ) seems to be retained within the cell . However , both transporters reached the cell surface . Accordingly , cells expressing the F . hepatica and the human taurine transporter and untransfected control cells were incubated in the presence of [3H]taurine and of [3H]GABA to determine the substrate specificity . We failed to detect any uptake of [3H]GABA ( S4 Fig ) . In contrast , cells expressing the F . hepatica taurine transporter accumulated substantially ( i . e . , about 10-fold ) higher amounts of [3H]taurine ( closed circles in Fig 3C ) than untransfected control cells ( open triangles in Fig 3C ) . Transport by both the F . hepatica ( Fig 3C ) and the human transporter ( Fig 3D ) was adequately described by a rectangular hyperbola . The KM of the human transporter was 41 . 0 ± 10 . 4 μM , which is in excellent agreement with that originally reported in a similar uptake assay ( 43±6 μM ) [39] . The apparent affinity of the F . hepatica transporter for taurine ( KM = 12 . 0 ± 0 . 5 μM ) was higher than that of the human transporter . In contrast , the maximum velocity of uptake was lower in cells expressing the F . hepatica taurine transporter ( 178 ± 2 pmol*10−6 cells*min-1 ) than in cells expressing the human taurine transporter ( 971 . 0 ± 60 . 3 pmol*10−6 cells*min-1 , Fig 3D ) . This can be accounted for—at least in part—by the lower surface expression of the F . hepatica taurine transporter ( Fig 3A ) . We also examined the ability of unlabelled GABA to inhibit uptake of [3H]taurine to estimate the affinity of the F . hepatica transporter for GABA: High concentrations of GABA were required to suppress taurine uptake by the F . hepatica transporter ( Fig 4A ) . From the monophasic inhibition curves , we calculated IC50-values of 5 . 6 ± 0 . 9 mM . As a reference , we assessed the affinity of the human taurine transporter for GABA in parallel ( triangles in Fig 4A ) : GABA was more potent in inhibiting the human than the fluke transporter; the estimated IC50 of GABA ( 1 . 6 ± 0 . 5 mM ) is in agreement with the reported KM of 1 . 46 mM [40] . β-Alanine was originally proposed as the alternative substrate of the taurine transporter [41] . Accordingly , we also compared the apparent affinity of the human and the F . hepatica transporter for β-alanine ( Fig 4B ) : β-alanine was more potent in inhibiting [3H]taurine uptake by the human transporter ( IC50 = 132 ± 54 μM ) than by the fluke transporter ( IC50 = 713 ± 260 μM ) . The affinity estimate for the human transporter was in line with the original observation that 100 μM β-alanine inhibited uptake by about 50% [39] . Substrate transport by SLC6 family members requires the co-transport of two or three Na+ ions . These provide the driving force for intracellular accumulation of the substrate . In addition , most SLC6 transporters are dependent on chloride , although the chloride gradient is immaterial , because the transporter completes the transport cycle in a chloride-bound return step [42] . We compared the ionic requirement of the F . hepatica ( Fig 5A & 5C ) and the human transporter ( Fig 5B & 5D ) by isoosmotic replacement of sodium with choline ( Fig 5A & 5B ) and of chloride with acetate ( Fig 5C & 5D ) . Both transporters required similar amounts of Na+ for half-maximum stimulation of transport ( EC50 = 51 . 4 ± 6 . 0 mM and 56 . 5 ± 7 . 1 mM for the F . hepatica and the human transporter , respectively ) . Similarly , the sodium saturation curves were sigmoidal with Hill coefficients of 1 . 6±0 . 1 and 2 . 3±0 . 1 for the F . hepatica and the human transporter , respectively . In contrast , the chloride saturation curves were hyperbolic ( Fig 5C & 5D ) . It is also evident from the comparison of Fig 5C and 5D that substrate uptake by the F . hepatica transporter required substantially lower concentrations of chloride ( EC50 = 13 . 9±3 . 1 mM ) than the human transporter ( 54 . 8±6 . 9 mM ) . The number of compounds which act as specific inhibitors of taurine uptake is very limited . Because of the close relation between GABA- and taurine transporters , we examined several compounds , which act as inhibitors of GABA uptake or of GABA degradation ( e . g . , nipecotic acid , tiagabine , vigabatrin ) , but these failed to inhibit the F . hepatica transporter . In fact , the only compound other than GABA and β-alanine ( cf . Fig 4 ) was guanidinoethyl sulfonate ( taurocyamine or GES ) . This compound was classified as a competitive inhibitor of mammalian taurine transporters [43] . We verified the mode of inhibition by determining uptake of [3H]taurine by cells expressing the F . hepatica ( Fig 6A ) and the human transporter ( Fig 6C ) in the presence of increasing concentrations of unlabeled taurine and of GES: in cells expressing the human transporter , the action of GES was consistent with competitive inhibition , because the IC50 of unlabeled taurine was progressively shifted to the right ( Fig 6C; IC50 = 41 . 2 ± 8 . 2 μM , 52 . 1 ± 9 . 5 μM and 98 . 9 ± 20 . 1 μM in the absence and presence of 0 . 1 and 0 . 3 mM GES , respectively ) . Accordingly , when the data were replotted in a Dixon plot , the resulting lines were parallel indicating that binding of taurine and GES was mutually exclusive ( Fig 6D ) . The affinity of the F . hepatica transporter for GES was about ten-fold lower than that of the human transporter; this can be seen by comparing the uptake in the absence of unlabeled taurine in Fig 6A and 6C , which was about 75% and 40% in the presence of 1 mM and 3 mM GES , respectively , for the F . hepatica transporter ( Fig 6A ) . Equivalent residual transport was seen at 0 . 1 and 0 . 3 mM GES with the human transporter ( Fig 6C ) . In addition , GES was a non-competitive inhibitor of taurine uptake by the F . hepatica transporter: the IC50 of unlabeled taurine were not shifted to the right in the presence of GES ( Fig 6A; IC50 = 11 . 7 ±4 . 1 μM , 18 . 7 ± 6 . 7 μM , and 16 . 0 ± 4 . 2 μM in the absence and presence of 1 and 3 mM GES , respectively ) . Transformation of these data into a Dixon plot produced a family of intersecting lines . This indicates a non-competitive inhibition , where taurine and GES can be bound simultaneously . We raised a rabbit polyclonal antiserum against the N-terminus of the F . hepatica taurine transporter using a purified fusion protein comprising maltose-binding protein ( MBP ) and the N-terminus ( i . e . , residues R2-R56 ) as the immunogen . We also generated the fusion of glutathione-S-transferase ( GST ) and the N-terminus of the F . hepatica taurine transporter . The purified GST-fusion protein was bound to GSH-sepharose 4B , which allowed for the enrichment of the taurine transporter-specific antibodies by affinity purification . The resulting antibody preparation was tested on cell lysates prepared from untransfected HEK293 cells and from cells expressing either a YFP-tagged version of the F . hepatica taurine transporter or the non-tagged protein ( Fig 7A ) . Consistent with the multiple glycosylation sites of the F . hepatica taurine transporter ( cf . Fig 1 ) , the antibody detected several bands of ~70 kDa and ~95 kDa in cell lysates containing the un-tagged ( Fig 7A , lane 2 ) and the YFP-tagged version ( Fig 7A , lane 3 ) , respectively . These immunoreactive bands were not seen in lysates prepared from untransfected cells ( Fig 7A , lane 1 ) . As an additional control , we also used an antibody against GFP for detection of the YFP-tagged transporter ( Fig 7B ) : the immunoreactive bands , which were visualized by the anti-GFP antibody , were identical to those detected by the antibody raised against the N-terminus ( cf . right-hand lanes in Fig 7A and 7B ) . Homogenates were prepared from adult F . hepatica; in addition , we enriched for tegumental cell membranes by subjecting adult flukes to a freeze-thaw cycle combined with vigorous vortexing . This procedure allows for collecting the particulate material from the tegument from the adult flukes and for the substantial enrichment of surface markers [43–45] . Although the sensitivity of the immunoblot sufficed to detect the heterologously expressed transporter in 1 μg of cell lysate ( Fig 7C , lanes 2 and 3 for the untagged and YFP-tagged transporter respectively ) , it was not possible to detect any immunoreactive material of the expected molecular mass in lysates from adult flukes ( Fig 7C , lane 4 ) . However , the preparation enriched in tegumental membranes contained immunoreactivity for the taurine transporter ( Fig 7C , lane 5 ) . Living adult F . hepatica can survive more than 14 days in Hédon-Fleig medium [46] . It is possible to maintain flukes for even longer periods—i . e . up to 6 months—in the presence of chicken serum [47] . However , chicken serum contains undefined amounts of taurine , albumin and lipoproteins . Thus , we carried out our experiments in Hédon-Fleig medium without any addition of chicken serum to preclude possible interference by carry-over of taurine , drug binding to albumin and binding of bile acids to lipoproteins . Accordingly , we placed adult flukes into the Hédon-Fleig medium . Under control conditions and in the presence of 50 μM taurine or of 2 mM GES , all flukes were alive and motile for at least 6 h ( Fig 8A ) . In contrast , if flukes were exposed to a mixture of unconjugated bile acids , i . e . cholic acid , chenodeoxycholic acid and deoxycholic acid as found in bovine bile [48] , flukes were progressively killed such that their median survival was about 3 hours ( open triangles in Fig 8A ) . We exposed a total number of 35 flukes to bile acids for 4 h , which was lethal for about two thirds ( second bar in Fig 8B ) . Flukes were protected against the deleterious effect of bile acids by the presence of 50 μM taurine in the medium , which afforded a statistically significant protection: survival increased to about 80% in the presence of bile acids and taurine ( third bar in Fig 8B ) . This protective effect was abolished , if GES was also present in the medium , with only 44% surviving ( fourth bar in Fig 8B ) . Consistent with the hypothesis that the taurine transporter is important for survival of the flukes in bile , we found that the transcripts for the taurine transporters are low in the early life stages ( fertilized egg , metacercariae ) and increase substantially during maturation of juvenile flukes ( S5 Fig ) .
The SLC6 transporter family has four branches: ( i ) the neurotransmitter transporters ( i . e . , transporters for dopamine , norepinephrine and serotonin ) , ( ii ) the glycine and proline transporters , ( iii ) the amino acid transporters SLC6A15 to SLC6A20 and ( iv ) the GABA-transporter subfamily , which mediates the uptake of GABA , betaine , creatine and taurine [49] . The evolutionary history of this latter branch was traced by sequence comparisons and by examining chromosomal synteny [50]; the following sequence of events was proposed: gene duplication of the ancestral transporter gene gave rise to the GABA-transporter-1 ( GAT-1/SLC6A1 ) and the creatine transporter-1 ( CT1/SLC6A8 ) . The taurine transporter ( TauT/SLC6A6 ) is the third gene , which appeared during evolution , presumably by duplication of the creatine transporter-1 . In chordates , GAT3 ( SLC6A11 ) arose from TauT . GAT2/BGT1 ( betaine/GABA-transporter-1 , SLC6A12 ) and GAT4 ( SLC6A12 ) were later additions during vertebrate evolution . Here we identified an SLC6 family member in F . hepatica . We classified this transporter as a taurine transporter based on both its sequence homology with the other protostomial taurine transporter , for which the biochemical activity has been verified , i . e . the TauT of B . septemdierum [33] and an in-depth characterization of its biochemical specificity: our data unequivocally show that taurine is the preferred substrate of the transporter . In fact , the affinities of the alternative substrates β-alanine and GABA were about 50- and 350-fold lower , respectively , than that of taurine . Taurine uptake was dependent on both , Na+ and Cl- . This was to be expected: with the notable exception of SLC6A17 , which is thought to function as a Na+-dependent vesicular amino acid transporter [51 , 52] , all eukaryotic SLC6 transporters function as Na+/Cl—dependent plasma membrane transporters . The majority relies on a stoichiometry of two sodium ions and one chloride ion . This was also true for the F . hepatica taurine transporter: chloride enhanced transport velocity in a hyperbolic fashion , but sodium stimulated taurine uptake with a sigmoidal concentration-response curve resulting in a Hill-coefficient close to 2 . This 2:1 stoichiometry is also consistent with the finding that the residues , which define the binding sites for the two sodium ions and the chloride ion are conserved in the F . hepatica taurine transporter . Taken together , our observations showed that the F . hepatica taurine transporter differed from its human orthologue in several respects: ( i ) the chloride affinity of the F . hepatica taurine transporter was substantially higher than that of the human transporter . From a teleological perspective , this finding can be rationalized as an adaptation to the ionic composition of the bile: the chloride concentration in mammalian bile is substantially lower than that of plasma [53] . Thus , an affinity in the range of 12 mM assures that the fluke transporter operates at close to saturation of the chloride site regardless of the changes in ionic composition resulting from hormonal stimulation of bile flow [53] . ( ii ) Similarly , the KM of the F . hepatica transporter for taurine was lower than that of the human orthologue . The concentration of taurine in human plasma is in the range of 50 μM [54]; thus the KM of the human taurine transporter is close to the extracellular levels . Although taurine was originally identified in ox bile [55] , the concentration of free taurine in bile is—to the best of our knowledge—not known . We suspect that the higher affinity of the F . hepatica taurine transporter reflects an adaptation to the lower concentration of taurine in bile . ( iii ) The pharmacology of taurine transporters has not yet been explored in depth . In spite of the limited availability of inhibitors , our observations show that the F . hepatica transporter differs substantially from that of the human taurine transporter: GABA and β-alanine were about 3 and 5-fold less potent , respectively , in inhibiting the F . hepatica than the human transporter . The affinity of GES for the F . hepatica was also lower . Importantly , GES was a non-competitive inhibitor of the F . hepatica transporter . The non-competitive mode of inhibition can be rationalized by taking into account that SLC6 transporters harbor two binding sites , namely the vestibular S2 site and substrate binding site proper , which is referred to as S1 site [49 , 56] . The non-competitive action may arise if GES binds preferentially to the S2 site: occupancy of the vestibular site and of the substrate binding site proper is not mutually exclusive and results in non-competitive inhibition [57] . At the very least , our observations justify the assumption that the taurine transporter of F . hepatica differs enough to allow for the development of selective specific inhibitors , which target the fluke transporter but not the mammalian orthologue . The mechanism by which taurine accumulation protects F . hepatica from bile acid toxicity is not known . In mammals , taurine deficiency or genetic deletion of the taurine transporter results in pleiotropic effects , which culminate in retinal degeneration , liver and kidney disease , skeletal muscle wasting , etc . [58 , 59] . It is generally accepted that taurine is an osmolyte , which protects cells against various types of stress at least in part by stabilizing proteins against denaturation [60] . Bile acids , in particular deoxycholic acid , are chaotropic and promote unfolding of proteins [61] . Thus , it is plausible to posit that taurine accumulation in flukes is a safeguarding mechanism , which blunts bile acid toxicity . The alternative hypothesis is to assume that flukes use taurine as a substrate to conjugate free bile acids; the resulting conjugated bile acids are subsequently re-exported into the bile by an ABC-transporter . This hypothetical mechanism of detoxification requires at least two components , ( i ) a bile acid-CoA: amino acid N-acyltransferase and ( ii ) a bile salt export pump . In fact , adult F . hepatica express many ABC transporters . Previously , a murine monoclonal antibody , which had been raised against a peptide derived from the first nucleotide binding domain ( Y705-K718 ) of human ABC-B11 , was used to immunoblot lysates of adult F . hepatica: an immunoreactive band was detected , albeit of only 80 kDa , which is half the size of the human orthologue [62] . Hence , it is not clear , if adult flukes express a bile salt export pump . In addition , we failed to find any evidence for the presence of a bile acid-CoA: amino acid N-acyltransferase by analyzing the deposited genomic sequence [23 , 24] . Regardless of the underlying mechanism , our observations suggest that the taurine transporter is essential for the survival of F . hepatica in hostile environments . The protective action of intracellular taurine is presumably the driving force for the early appearance of the taurine transporter during evolution [50] . We suspect that the hypothetical GABA-transporter-2 of C . sinensis is , in fact , also a taurine transporter . The same is likely to be true for the other trematode SLC6 transporters in Fig 2 , i . e . , O . viverrini hypothetical protein , the S . haematobium GABA-transporter-2 and the S . mansoni serotonin transporter . Hence , designing specific inhibitors may also be of interest to explore the role of these transporters in the biology of these parasites . Parasites are by definition auxotrophic . Thus they must rely on transporters to obtain nutrients and other solutes , which they require for survival . Thus solute carriers are likely to represent drug targets , which allow for the control of parasitic disease . Our observations show that the taurine transporter is essential for survival of F . hepatica in the presence of bile acids . We anticipate that inhibitors of the taurine transporter may not only be useful to eliminate the adult stage of F . hepatica from the liver of affected individuals but also of the other liver flukes , which infest people , i . e . C . sinensis and Opisthorchis viverinni .
Rabbits were immunized at the “Department für Biomedizinische Forschung , Medical University of Vienna” . This institution holds a permission ( BMWF-66 . 009/0266-II/3b/2013 ) by the Austrian Ministry of Science to immunize rabbits according to §26 Austrian Animal Testing Law of 2012 ( TVG 2012 [63] ) . All efforts were made to minimize animal suffering and to reduce the number of animals used . If not stated otherwise , cell culture plastic dishes and pipettes were from Sarstedt AG&Co . , Nuembrecht , Germany , chemical and reagents including cell culture media were from Sigma Aldrich . According to European Regulation ( EC 854/2004 ) [64] , the livers of slaughtered cattle have to be inspected by veterinarians for a possible infestation of animals with parasites using visual , palpation and incision inspection [65] . Based on this surveillance , livers associated with liver flukes are discarded together with other abnormalities [65] . Before this , we inspected these livers for the presence of F . hepatica . Adult flukes were collected from the infected bile ducts of freshly slaughtered cattle in local abattoirs ( Eschenau & Salzburg , Austria ) and washed with phosphate-buffered saline ( PBS; composition: 2 . 7 mM KCl , 1 . 5 mM KH2PO4 , 137 mM NaCl , 4 . 3 mM Na2HPO4 x 2H2O , pH 7 . 3 ) and either maintained in Hedon Fleig solution ( 120 . 7 mM NaCl , 4 mM KCl , 1 . 9 mM MgSO4 , 0 . 9 mM CaCl2 , 18 . 5 mM NaHCO3 , 10 mM HEPES , 15 mM D-glucose adjusted pH to 7 . 3 ) [46] or frozen in liquid nitrogen and stored at -80°C . Total RNA was isolated from freshly isolated adult flukes using Trizol ( Sigma Aldrich , Mannheim , Germany ) Reverse transcription was performed using “Transcriptor High Fidelity cDNA Synthesis Kit ( Roche Diagnostics GmbH , Mannheim , Germany ) , using either gene specific primers or oligo dT primers . Starting from a previously deposited cDNA sequence 5’ and 3’ ends of the cDNA were identified via RACE ( rapid amplification of cDNA ends ) technology , using 5’/3’ RACE Kit , 2nd Generation ( Roche Diagnostics GmbH , Mannheim ) . Recognition sites for XhoI and KpnI were added to the full-length cDNA of FhTauT by PCR . The resulting PCR product was cloned via XhoI and KpnI to peYFP-C1 ( Takara Bio Europe , France ) generating a transporter tagged with a yellow fluorescent protein at its N-terminus . To generate a non-tagged transporter , FhTauT was also cloned to pcDNA3 . 1 ( Invitrogen , Carlsbad , USA ) . HsTauT was cloned in a similar way to peCFP-C1 via BamHI and HindIII thereby generating a transporter tagged with the cyan fluorescent protein . Generated sequences were validated by Sanger sequencing ( LGC Genomics , Berlin , Germany ) . The cDNA sequence of FhTauT was deposited at GenBank ( NCBI , Bethesda , USA ) under the accession number: MG674191 . The partial initial sequence of Fh TauT mRNA was obtained from the database published by Gasser et al . [22] and full-length mRNA was obtained by applying RACE techniques ( for see PCR amplification of cDNA and cloning ) . The genomic sequence was obtained from the database WormBase ( https://parasite . wormbase . org ) [23] and is based on the published F . hepatica genome ( PRJEB6687 ) [66] . We analyzed the expression of FhTauT in various life stages by searching the compilation of RNAseq data deposited in the WormBase with the built-in BLAST tool ( PRJEB6904 ) [66] . We focused exclusively on the gene encoding FhTauT and extracted the expression levels of various exons at individual lifecycle stages of the parasite . We normalized the number of reads per million base pairs to that seen in fertilized Fasciola eggs ( expression levels at this stage = 1 , hence log2 = 0 ) . Exon-intron boundaries of the final extended FhTauT sequence were detected using the splign web interface ( https://www . ncbi . nlm . nih . gov/sutils/splign/splign . cgi ) [67] . The visualization of exons and introns organization in the genome was done by Exon-Intron Graphic Maker ( http://wormweb . org/exonintron ) . Orthologous sequences were identified using the basic local alignment tool ( BLAST ) web interface provided by NCBI using non-redundant protein sequences database ( nr ) [68] . Sequence alignment and phylogenetic analysis were performed using MEGA 7 [34] Multiple Sequence Comparison by Log-Expectation ( MUSCLE [69] ) and Clustal W [70] respectively . The phylogenetic trees were using the neighbor-joining method [71] . The results were displayed using ENDscript ( http://endscript . ibcp . fr/ ) [72] . Accession numbers for sequences included can be found in the Material & Methods section . For the construction of a phylogenetic tree ( S3 Fig ) covering parasites only , orthologous sequences of Platyhelminthes deposited in the WormBase ( except Protopolystoma xenopodis , Trichobilharzia regenti and Schmidtea mediterranea which has only truncated versions available ) were exported into Molecular Evolutionary Genetics Analysis ( MEGA ) software version 7 based on their best E value , score and identity ( S6 Tab . ) , and aligned with ClustalW . In addition a sequence from F . gigantica was included , obtained from the database published by Gasser et al . [22] . Phylogenetic analysis was performed using MEGA 7 [34] . ( neighbor-joining , 1000-replicate , bootstrap ) . The amino acid data were corrected for a gamma distribution ( level set at 1 . 0 ) and with a Poisson correction . The NetNGlyc 1 . 0 Server ( www . cbs . dtu . dk ) was used to search for putative N-glycosylation sites in the final sequence of FhTauT [73] . We used the crystal structure of Hs SERT , derived from Coleman et al . ( PDB ID:5I6X , DOI: 10 . 2210/pdb5i6x/pdb ) to compare the protein structure with FhTauT and other transporters [25] . Human embryonic kidney-293 ( HEK293 ( ATCC CRL-1573 , LGC standards Wesel , Germany ) ) cells , were grown in Dulbecco`s modified Eagle medium ( DMEM ) supplemented with 10% fetal bovine serum ( FCS Nuaille , France ) , 100 units/ml penicillin and 100 μg/ml streptomycin at 37°C and with 5% CO2 in a humidified incubator . Cells were transfected with plasmids encoding fluorescently tagged or untagged versions of F . hepatica ( FhTauT ) and H . sapiens ( HsTauT ) taurine transporter using Jet Prime transfection reagent ( Polyplus-transfection , France ) . To generate monoclonal cell lines stably expressing the transporters , transfected cells were subjected to and selected with the concentration of 0 . 2 mg/ml geneticin ( G418 ) for 10 days . Surviving cells forming colonies were separated and analyzed for membrane localization of transporters and transport of taurine . After the selection process cells were kept at 50 μg/ml G418 to keep the selection pressure . For saturation uptake experiments , cells were plated the day before the experiment at a density of about 50 , 000 cells/well onto poly ( D-lysine ) -coated 48-well culture plates ( CytoOne , USA ) . On the day of the experiment , cells were washed with prewarmed Krebs-HEPES buffer twice ( KHB ) ( 120 mM NaCl , 3 mM KCl , 2 mM CaCl2 , 2 mM MgCl2 , 20 mM glucose , 10 mM HEPES , pH 7 . 3 ) . Then , cells were incubated in KHB containing tracer amounts ( 0 . 1 μM ) of [3H]taurine , ( 19 . 1 Ci/mmol ) ( Perkin Elmer , USA ) together with increasing concentrations of non-labeled taurine 10 min . The reaction was terminated after 10 minutes by removing the medium followed by rapid rinsing of cells with ice-cold assay buffer . Subsequently , cells were lysed with 0 . 5 ml of 1% sodium dodecyl sulfate ( SDS ) and transferred into scintillation vials for liquid scintillation counting . Non-specific uptake was determined by incubating cells in the presence of the blocker β-alanine ( 100 mM ) before and during the experiment and subtracted from uptake values . Data were fit to a Michaelis-Menten equation . Chloride-free KHB was prepared to examine the chloride dependence of both , FhTauT and HsTauT using acetate salts instead of chloride salts . Likewise , NaCl was replaced by choline chloride to examine the sodium requirement . Uptake experiments were performed as described above using 0 . 1 μM [3H]taurine in sodium- and chloride-free modified KHB , respectively , mixed with increasing amounts of NaCl containing KHB thereby varying Na+ and respectively Cl- concentration from 0–120 mM . The inhibition experiments were performed in analogous manner with 0 . 1 μM [3H]taurine and the logarithmically spaced concentrations of inhibitors . GABA uptake was assessed by incubating HEK293 cells ( 105/ well ) stably expressing the human GABA-transporter-1 ( HsGAT1 , diamonds ) , the F . hepatica taurine transporter ( FhTauT , circles ) and the human taurine transporter ( HsTauT , triangles ) in KHB containing concentrations of [3H]GABA covering the range of 0 . 3 μM to 3 mM for three minutes . The amount of labeled [3H]GABA was kept constant and the specific activity was progressively diluted by the addition of unlabeled GABA ( from 9 Ci/mmol to 9 Ci/mol ) . The reaction was stopped by the addition of ice-cold KHB followed by three rapid washes . Cells were detached and the accumulated radioactivity was determined by liquid scintillation counting [74] . For confocal imaging , HEK293 cells stably expressing YFP-FhTauT and CFP-HsTauT were seeded onto poly-D-lysine–coated glass-bottomed chambers 24 h prior to the experiment . Cells were imaged using a 60x oil immersion objective ( Plan Apo VC , Nikon , Austria ) on a confocal laser scanning microscope ( A1R+ , Nikon , Vienna , Austria ) . The fluorophores YFP and CFP were excited using a 488 nm and 403 . 5 nm laser line , respectively , at 1–5% of maximal intensity . Emission of CFP and of YFP was detected with a standard PMT ( photomultiplier tube ) detector equipped with a 435 nm emission filter ( 50 nm bandpass ) and a GaAsP detector equipped with a 525 nm filter ( 50 nm bandpass ) . The cell membrane was visualized by incubating the cells in a trypan blue solution ( 0 . 05% ) for 5 min . Fluorescence of trypan blue was excited using a 561 . 9 nm laser line , the emission was detected with a GaAsP detector with a 595 nm filter ( 50 nm bandpass ) . Freshly isolated flukes were washed twice with PBS . Afterwards , flukes were homogenized at 4°C in HME buffer ( 10 mM HEPES , 1 mM MgCl2 , 0 . 1 mM EDTA , and pH 7 . 4 ) using an Ultra-Turrax dispersing instrument ( Janke&Kunkel , IKA-WERK , Germany ) . The sheared flukes underwent two freeze/thaw cycles in liquid nitrogen . After repeated sonication , the lysate was centrifuged at 10 , 000 g for 15 minutes at 4°C . The pellet was resuspended in 5 ml of buffer ( 1 mM EDTA , 0 . 1% sodium deoxycholate , 0 . 1% SDS , 140 mM NaCl , 10 mM Tris . Cl ( pH 7 . 4 . ) supplemented with one tablet of protease inhibitors per 20 ml ( cOmplete Protease Inhibitor Cocktail , Roche ) ) , incubated overnight on a rocking platform and centrifuged at 13 , 000 g at 4°C for 10 min . Supernatants from cell lysates were mixed with Laemmli buffer ( 0 . 1% 2-mercaptoethanol , 0 . 01% bromophenol blue , 10% glycerol , 2% SDS , 62 . 5 mM Tris . HCl , pH 6 . 8 ) and used for SDS polyacrylamide gel electrophoresis . For Fasciola tegument preparation we used the “freeze-thaw and vortex” method described by Roberts et al . [44] with minor modifications . About 5 g adult flukes ( corresponding to 20 flukes ) were flash frozen in liquid nitrogen and then thawed on ice in 5 ml of cold RPMI-1640 including protease inhibitors as described above . The tegument was detached by vigorous vortexing for 1 min from the bodies of the flukes , and the supernatant was filtered through a metal sieve . The denuded bodies were pelleted by centrifugation at 1000g for 30 min at 4°C . The resulted pellet was resuspended in HME buffer , and freeze-thaw was done twice . The produced sample was sonicated thrice . The sample was centrifuged at 40 , 000g for 2 hours at 4°C . The pellet was solubilized with Laemmli buffer . Proteins were separated via SDS-PAGE electrophoresis and transferred from the gel to a nitrocellulose membrane . Membranes were blocked in Tris-buffered saline ( TBS ) containing 0 . 1–0 . 5% Tween 20 and 3% bovine serum albumin ( BSA ) or 3% skimmed milk . For detection , the purified antibody against N-terminus was used at a dilution from 1:100 to 1:50 . The following commercial antibodies were used-rabbit polyclonal anti-GFP ( Ab290 Abcam; 1/5000 ) , anti-rabbit IRDye 680RD or anti-rabbit IRDye 800CW ( LiCOR Biosciences Fluorescence signal on membranes was detected by Licor Odyssey CLx , ( Imaging System ) . Two rabbits were immunized with a fusion protein construct consisting of maltose binding protein , and the amino-terminus of FhTauT ( RQEFSFPSKSRTELAATSSIHPV FEVKDECSIIPVSSSLANKKEVEKSPREQWKR ) . The immunization was carried out at the Medical Unversity of Vienna , Div . Laboratory Animal Science and Genetics , Himberg , Austria . Short: The antigen solutions for the immunization of one rabbit were prepared by emulsifying 100 μg MBP fusion protein in 750 μl 1x PBS ( aqueous solution ) and 1 ml ( in ) complete Freund’s adjuvant ( oleaginous solution ) . The emulsions were kept at 4°C , and the immunization was performed within 1 h . Antibodies were affinity purified out of serum from immunized rabbits . For this 6-His and glutathione-S-transferase ( GST ) was fused to the same N-terminal peptide of FhTauT as mentioned above and expressed in E . coli BL21 . Subsequently , the protein eluted by cOmplete His-Tag Purification Resin ( Sigma Aldrich , Mannheim , Germany ) under denaturing conditions and urea content was reduced from 8 M to 5 M with a saline solution . To optimize the coupling to Affi-gel 10 ( BioRad , California , USA ) , eluates were pH adjusted based on their isoelectric point titrating either NaOH or HCl . To block free binding sites of the Affi-gel 10 , 100 μl 1 M ethanolamine pH 8 . 0 per ml Affi-gel 10 suspension were added for 1 h at room temperature . Antibodies were eluted with alkaline elution buffer ( pH 11 . 5 ) . 1 ml fractions were collected in Eppendorf tubes prefilled with 100 μl acidic neutralization buffer ( pH 2 . 45 ) . Protein-containing fractions were pooled , and the protein concentration was determined using the bicinchoninic acid ( BCA ) protein assay reagent ( Pierce , ThermoFisher Scientific ) . F . hepatica was isolated from the bile ducts of cows slaughtered at Alpenrind GmbH ( Salzburg , Austria ) . Flukes were kept in modified Hedon Fleig`s solution at 37°C and 5% CO2 in a humidified atmosphere . Before the experiment , flukes were maintained at 37°C for three days after extraction to clear them from bile acids . Living flukes ( judged by observing movement after gently tapping them with metal forceps ) were transferred into flasks containing either Hedon Fleig`s Solution alone or Hedon Fleig`s Solution supplemented with combinations of bile acids ( 5 mM cholic acid ( CA ) , 1 . 5 mM deoxycholic acid ( DCA ) and 0 . 2 mM chenodeoxycholic acid ( CDC ) ) , 50 μM taurine and 2 mM guanidinoethyl sulfonate ( GES ) . Survival of flukes was determined again after four hours of treatment . Data are expressed as arithmetic means ± S . E . Statistics Nonlinear regression analysis was carried out for uptake assays to determine the Km values using the Sigma Plot software program ( version 12 . 5; Systat Software , Chicago , IL , USA ) or GraphPad Prism software ( version 5 . 04 , GraphPad Software , La Jolla California , USA ) . Hill coefficients for Na+ and Cl- dependent experiments were obtained from the sigmoidal curves using the softwares as noted above . To generate a phylogenetic tree ( Fig 2 ) we used the following proteins: C . sinensis , accession no: GAA52609 . 1; O . viverrini , XP_009175490 . 1; S . mansoni , XP_018646439 . 1; S . haematobium , XP_012792810 . 1; B . septemdierum , BAF95543 . 1; D . melonagaster , NP_651930 . 2; H . sapiens , P31645 . 1; H . sapiens , NP_003034 . 2; H . sapiens NP_057699 . 2 .
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The liver fluke F . hepatica imposes an economic burden by infesting domestic ruminants . In addition , the WHO classified human fasciolosis as a disease of vital global public health importance , because several million people worldwide are infested by this trematode . Because it is currently not possible to vaccinate against this parasite , anthelmintic drugs are the treatment of choice for both , animals and people . During the last decades , the proportion of flukes resistant to drugs has steadily increased . F . hepatica persists in the hostile environment of the host bile duct . Accordingly , the fluke must be endowed with defense mechanisms , which protect it against the toxic actions of bile acids . This working hypothesis was explored by cloning a candidate taurine transporter of F . hepatica . The experiments verified that the transporter mediated the sodium- and chloride-dependent uptake of taurine . It requires only low concentrations of chloride , which indicates adaptation to the ionic composition of the bile , and it is protective: inhibition of the transporter renders flukes susceptible to killing by bile acids . By showing that the taurine transporter represents an Achilles heel of F . hepatica , these observations point to a new anthelmintic therapeutic strategy .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"taxonomy",
"invertebrates",
"biliary",
"system",
"medicine",
"and",
"health",
"sciences",
"liver",
"body",
"fluids",
"chemical",
"compounds",
"helminths",
"bile",
"animals",
"invertebrate",
"genomics",
"trematodes",
"phylogenetics",
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"methods",
"sequence",
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"information",
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"alignment",
"bioinformatics",
"fasciola",
"hepatica",
"flatworms",
"fasciola",
"biological",
"databases",
"chemistry",
"chlorides",
"evolutionary",
"systematics",
"animal",
"genomics",
"bile",
"ducts",
"eukaryota",
"sequence",
"databases",
"anatomy",
"physiology",
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"and",
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"biology",
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] |
2018
|
Identification and characterization of the Fasciola hepatica sodium- and chloride-dependent taurine transporter
|
To preserve genome integrity , the S-phase checkpoint senses damaged DNA or nucleotide depletion and when necessary , arrests replication progression and delays cell division . Previous studies , based on two pol2 mutants have suggested the involvement of DNA polymerase epsilon ( Pol ε ) in sensing DNA replication accuracy in Saccharomyces cerevisiae . Here we have studied the involvement of Pol ε in sensing proper progression of DNA replication , using a mutant in DPB2 , the gene coding for a non-catalytic subunit of Pol ε . Under genotoxic conditions , the dpb2-103 cells progress through S phase faster than wild-type cells . Moreover , the Nrm1-dependent branch of the checkpoint , which regulates the expression of many replication checkpoint genes , is impaired in dpb2-103 cells . Finally , deletion of DDC1 in the dpb2-103 mutant is lethal supporting a model of strand-specific activation of the replication checkpoint . This lethality is suppressed by NRM1 deletion . We postulate that improper activation of the Nrm1-branch may explain inefficient replication checkpoint activation in Pol ε mutants .
DNA integrity of living organisms is affected by perturbations that induce replication stress including nucleotide depletion or collision with lesions encountered in DNA exposed to alkylating agents [1] . Therefore , each cell must constantly monitor its genome integrity and coordinate DNA replication with cell division in order to avoid genetic instability [2] . Cell cycle checkpoints that monitor the accuracy of each phase of the cycle play crucial role in this control . The replication checkpoint monitors DNA duplication , and when activated , regulates transcription of specific genes , arrests replication progression , stabilizes replication forks , increases the dNTP pool , suppresses late-origin firing , delays cell division and finally restarts DNA synthesis after removal of replication stress [3–10] . It also prevents homologous recombination ( HR ) at double strand breaks ( DSB ) and stressed replication forks during S phase , presumably by blocking DNA ressection , to prevent genetic instability [11 , 12] . Checkpoint mechanisms encompass many proteins that act as sensors , mediators and effectors in a cascade of phosphorylation events [13] . In the first step , uncoupling of helicase and polymerase activities , unsynchronized leading and lagging strand replication or replication fork collapse result in accumulation of ssDNA [14 , 15] . After an activation threshold is reached [16] , large stretches of RPA-coated ssDNA recruit the apical protein kinase Mec1 bound to Ddc2 [17] . Then , the Ddc1 subunit of the 9-1-1 sensor checkpoint clamp ( Ddc1-Rad17-Mec3 in Saccharomyces cerevisiae ) is recruited to the ds-ssDNA junctions and activates the signaling network [18] . The checkpoint response is completely dependent on the 9-1-1 complex in G1 phase while in G2 Dpb11 is also involved in this process [19] . In the S-phase , multiple factors are needed to trigger checkpoint activation including Dna2 in addition to Ddc1 , Dpb11 [20–22] reviewed in [13 , 23] . It has been shown that a ddc1Δ dpb11-1 double mutant is partially defective in phosphorylation of the checkpoint effector kinase , Rad53 [20 , 24] , indicating that there is an additional S-phase checkpoint activation pathway . Since Dna2 is probably involved in this additional activation mechanism , in the triple dpb11Δ ddc1Δ dna2Δ mutant only negligible phosphorylation of Rad53 was detected [21] . Finally , there is also evidence that DNA polymerase epsilon ( Pol ε ) is involved in the 9-1-1 independent activation pathway ( Dpb11 recruitment to stalled replication forks ) [25] suggesting separation of replication stress sensors on the leading and lagging DNA strands [20 , 26] . Upon checkpoint activation , the phosphorylated signaling kinase Mec1 , transmits the signal to the downstream effector kinase Rad53 [27] . Its activation during replication stress is facilitated by checkpoint mediator protein Mrc1 [28 , 29] which promotes Mec1-Rad53 interactions [30] . Importantly , both Mec1 and Rad53 are essential genes in S . cerevisiae while not in Schizosaccharomyces pombe [31] . Rad53-dependent control of the replication stress response is divided into two branches: ( i ) the well-characterized Dun1-Crt1 pathway , also called DNA damage response ( DDR ) branch [32 , 33] , which mainly up-regulates the dNTP pool , and ( ii ) the Nrm1-MBF pathway , also called the G1/S cell cycle ( CC ) branch [34 , 35] , which up-regulates dozens of genes involved in many processes e . g . , TOS4 , TOS2 , MCD1 , CDC21 [36] . Pol ε is one of the major replicative polymerases that generally replicates the leading DNA strand while DNA polymerase delta ( Pol δ ) replicates the lagging strand [37–40] . Recently , an in vitro study of a reconstituted replisome has shown that Pol ε is targeted to the leading strand by the CMG complex ( Cdc45 , Mcm2-7 and GINS ) while Pol δ is targeted to the lagging strand by PCNA ( proliferating cell nuclear antigen ) [41] . Moreover , a chromatin immunoprecipitation based method ( eSPAN ) was used to demonstrate the same strand bias patterns of Pol δ and Pol ε [42] . Pol ε is composed of the catalytic Pol2 subunit and three non-catalytic subunits Dpb2 , Dpb3 and Dpb4 [43–45] , for review see [46 , 47] . Dpb3 and Dpb4 subunits are involved in stabilization of Pol ε interaction with DNA , and their deletion affects replication fidelity [48] . Pol2 and Dpb2 subunits are essential in yeast , although deletion of the N-terminal polymerase catalytic domain of Pol2 gives viable cells [49 , 50] . In contrast , its C-terminal half is necessary and sufficient to support growth and is involved in both interaction with the Dpb2 subunit and S-phase checkpoint activation [50–52] . The interaction of Dpb2 subunit with Psf1 , a subunit of the GINS complex , is important for the CMG complex assembly . Therefore , Dpb2 is involved in initiation of DNA replication but also links Pol ε to the CMG complex during elongation [53–57] . Finally , Pol ε –GINS interaction enables the preferential recruitment of Pol ε over Pol δ to the leading strand [41] . The dpb2 mutants isolated in our laboratory demonstrate temperature-sensitivity and an increased number of replication errors ( MMR-dependent mutator phenotype ) [58 , 59] . In these mutator strains , Pol ζ participates in DNA replication more often although the mutator phenotype of dpb2 mutants results not only from this error-prone TLS polymerase activity [60] . Moreover , these Dpb2 mutants are impaired in interaction with Pol2 and the GINS subunits Psf1 and Psf3 [56 , 58 , 61] which may result in increased participation of Pol δ on the leading strand and be partially responsible for the mutator phenotype [61] . In this work , we investigate the involvement of the Dpb2 subunit of Pol ε in triggering the response to replication stress . For this purpose , we use the dpb2-103 mutant carrying T342I S343F T345I P347S P348S substitutions , isolated in our laboratory [58] . We found that this mutant demonstrates phenotypes characteristic for replication checkpoint mutants . The dpb2-103 cells are sensitive to MMS ( methyl methanesulfonate ) and HU ( hydroxyurea ) , and fail to delay cell cycle progression when treated with these agents . Although , dpb2-103 cells undergo checkpoint-induced Rad53 phosphorylation , they cannot properly activate the Nrm1/MBF branch of downstream response . Finally , we observed a lethal effect of dpb2-103 mutation combined with ddc1Δ . We propose that the observed synergy suggests independent roles in checkpoint activation and that 9-1-1 may recognize damage on the lagging strand while dpb2 , as a subunit of Pol ε , acts on the leading strand .
Studies of the replication stress checkpoint have suggested the involvement of the catalytic subunit of DNA polymerase epsilon , Pol2 , in checkpoint activation [62 , 63] . Later , it was suggested that Dpb2 , the essential non-catalytic subunit of Pol ε interacts with Mrc1 , the checkpoint mediator , and that thus Dpb2 may also be involved in activation of the S-phase checkpoint through modulation of Pol2-Mrc1 interactions [64] . Dpb2 variants that contribute to a spontaneous mutator phenotype have been analyzed in our laboratory for many years [58–61] . Replication stress can be generated either by nucleotide depletion using HU or by blocking replication due to fork collision with MMS-generated DNA lesions , which are detected only during replication [1 , 65] . To determine whether Dpb2 protein is involved in proper execution of replication checkpoint , first we analyzed the sensitivity of yeast cells with the dpb2-103 allele to the genotoxic agent methyl methanesulfonate ( MMS ) or to hydroxyurea ( HU ) , the ribonucleotide reductase inhibitor ( Fig 1 ) . When compared to wild type cells , those with the dpb2-103 allele demonstrate increased sensitivity to both MMS and HU , although these cells were not as sensitive as the canonical S-phase checkpoint deficient mutant mec1-21 ( Fig 1A , 1B and 1C ) . Yeast cells challenged with genotoxic or replication stress activate the checkpoint and delay their cell cycle progression . Slowing down the progression through S-phase gives more time to complete perturbed DNA replication and may result from inhibition of dormant or late origin firing [1 , 3 , 47] or inhibition of replication elongation [66] . This delay can be observed by flow cytometry analysis of DNA synthesis progression in the population of yeast synchronized in G1 and released under specific conditions [9 , 67 , 68] . We synchronized dpb2-103 mutant yeast cells with α-factor and released them from G1 in the absence or presence of MMS or HU . Then , we performed a flow cytometry analysis of DNA content to monitor G1-S-G2 transitions . Under MMS treatment in minimal media at 23°C , dpb2-103 cells reached the 2C DNA content after 240 min while wild-type cells remained at the G1-S transition after the same time in the same conditions . ( Fig 2 ) . Under HU treatment dpb2-103 cells entered S-phase very slowly , whereas wild-type cells remained blocked in G1 phase . These results demonstrate that , similarly to the mec1-21 checkpoint mutant , dpb2-103 cells are defective in delaying cell cycle progression and DNA synthesis when challenged with replication stress . The checkpoint-induced delay of cell cycle progression in cells exposed to HU enables replication fork stabilization and DNA synthesis restart after release from replication stress . Therefore , we synchronized dpb2-103 cells in G1 , and released them in the presence of HU . After 90 minutes , we washed out HU and shifted cells into fresh medium . Unexpectedly , unlike the control strain mec1-21 cells , the dpb2-103 cells were able to restart DNA synthesis after release from HU ( S1 Fig ) . This result shows that the dpb2-103 mutant retains partial S-phase checkpoint activity , perhaps due to 9-1-1 checkpoint clamp sensing from the lagging strand . Previous work has suggested the involvement of the leading strand DNA polymerase ε in replication checkpoint activation [62 , 69] . At the same time , the 9-1-1 ( Ddc1-Rad17-Mec3 ) complex has been proposed to be involved in sensing lagging strand replicative stress [20] . If the 9-1-1 checkpoint clamp and Pol ε act in parallel , strand-specific pathways to induce the response to replication stress , one can expect reduced ability to induce the checkpoint in the double mutant . To test whether Dpb2 is the Pol ε subunit involved in inducing the leading strand pathway of the replication stress checkpoint , we decided to introduce a DDC1 deletion in the dpb2-103 cells . Interestingly , the attempts to substitute DDC1 with nourseothricin resistance cassette ( NAT1 ) were unsuccessful . Similarly , the dissection of tetrads obtained from a DPB2/dpb2-103 DDC1/ddc1Δ strain ( Fig 3A ) failed to generate dpb2-103 ddc1Δ cells , suggesting that the double mutant dpb2-103 ddc1Δ is inviable . In order to verify this , we attempted to introduce a DDC1 deletion into dpb2-103 cells carrying the pMJDPB2 plasmid [58] that provides Dpb2 protein . Transformants obtained in this experiment were cultured and serial dilutions were plated on YNBD medium and YNBD supplemented with 5-FOA to obtain plasmid-free clones . As expected , in contrast to the wild-type , dpb2-103 and ddc1Δ strains the dpb2-103 ddc1Δ cells became inviable after plasmid loss ( Fig 3B ) supporting the conclusion that the double mutant phenotype is lethal . The S-phase checkpoint induction deficiency of canonical mec1 or rad53 mutants is rescued by increasing dNTP formation by deletion of the ribonucleotide reductase inhibitor gene SML1 [4 , 70] . Therefore , we attempted to obtain dpb2-103 ddc1Δ sml1Δ cells through tetrad dissection of an appropriate heterozygous strain . However , after prolonged incubation , we obtained only small colonies of inviable double or triple mutants ( S2 Fig ) . These results demonstrate that the sml1Δ ( increased dNTP level ) does not rescue lethality of dpb2-103 ddc1Δ cells . Together , these results demonstrate synthetic lethality of the dpb2-103 mutation combined with deletion of the DDC1 gene . Checkpoint activation in the S-phase induces a cascade of phosphorylation events . To test the stage at which checkpoint activation fails in dpb2-103 cells , first we analyzed activation of the checkpoint kinase Rad53 . [27 , 71] . We compared the phosphorylation of Rad53 in dpb2-103 cells after MMS or HU treatment to the Rad53 status in checkpoint defective mec1-21 and pol2-12 cells . Migration of the phosphorylated form of Rad53 in polyacrylamide gels is retarded compared to unmodified Rad53 . In dpb2-103 cells , after MMS or HU treatment during 180 minutes , phosphorylated Rad53 was detected ( S3 Fig ) . As expected , in the checkpoint defective control strain mec1-21 , after either MMS or HU treatment no Rad53 phosphorylated form was observed . In pol2-12 cells Rad53 was phosphorylated after HU treatment and residual phosphorylation was observed under MMS-induced genotoxic stress ( S3 Fig ) . These observations suggest that although the dpb2-103 mutant seems to be impaired in S-phase checkpoint activation , the checkpoint kinase Rad53 is phosphorylated . However , it is not clear whether the protein is phosphorylated properly and the downstream signal amplified and propagated correctly . Rad53-dependent phosphorylation of Dun1 ( DNA-damage un-inducible ) up-regulates the dNTP pool , primarily through two mechanisms . First , Dun1 phosphorylates and thus inhibits the Crt1 ( constitutive RNR transcription ) repressor which regulates a small part of the checkpoint-dependent transcriptional response i . e . the RNR2 , RNR3 and RNR4 genes which encode subunits of the ribonucleotide reductase RNR [32 , 72] . Crt1 also represses the expression of gene HUG1 ( hydroxyurea and UV and gammaradiation induced ) whose product inhibits RNR through binding the Rnr2 subunit [73 , 74] . In parallel , Dun1 phosphorylates and promotes degradation of Sml1 , the inhibitor of RNR [4 , 75] Moreover , Dun1-dependent phosphorylation of Dif1 ( damage-regulated import facilitator 1 ) , results in inactivation of the nuclear import of the Rnr2 and Rnr4 subunits of RNR , resulting in their cytoplasmic localization [76 , 77] . Therefore , to test whether in the dpb2-103 cells the checkpoint was interrupted downstream of Rad53 , we analyzed the degradation of Sml1 and induction of RNR3 and HUG1 genes . The amount of the Sml1 protein was analyzed immunologically in extracts from cells treated with MMS for 60 or 120 minutes and compared with untreated cells . In wild-type cells , under genotoxic stress Sml1 is degraded after 60 minutes . Similar results were observed for dpb2-103 cells treated with MMS ( Fig 4A ) . Next , using quantitative RT-PCR we analyzed the expression of the ribonucleotide reductase gene RNR3 and gene HUG1 encoding the RNR inhibitor [73 , 74] . Expression of these two genes is upregulated by the Dun1-Crt1 branch of replication stress response . Wild-type or dpb2-103 cells were synchronized in G1 and released into S-phase in the presence of 200 mM HU . The amount of RNR3 or HUG1 transcripts was normalized to wild-type untreated cells . After 120 and 240 minutes of HU treatment , the RNR3 and HUG1 expression levels in dpb2-103 were similar to those observed in wild-type cells under the same treatment ( Fig 4B ) . It is noteworthy , that the RNR3 mRNA levels at the time of release from G1 and after 120 or 240 from release into S-phase were about 2-fold higher than in wild-type cells . In the control experiment , in mec1-21 cells , induction of RNR3 or HUG1 was not observed . These results demonstrate that the dpb2-103 cells activate the Dun1-Crt1 branch in response to HU or MMS induced replication stress . The replication checkpoint pathway downstream of Rad53 also encompasses a second branch , parallel to the Dun1/Crt1 , i . e . the Nrm1/MBF branch . The Nrm1 co-repressor ( negative regulator of MBF targets 1 ) together with the MBF ( MluI-binding factor ) repressor complex recognize the MCB ( MluI cell-cycle box ) DNA sequence in promoter regions of dozens of genes to repress transcription upon exit from G1 phase . Under replication stress , Rad53-mediated phosphorylation of the Nrm1 repressor prevents its binding to MBF promoters and allows upregulation of a set of MCB G1/S transition genes [34 , 35 , 78–83] . As a consequence , deletion of NRM1 and expression of MCB genes increases cell survival of checkpoint-deficient rad53Δ or mec1Δ yeast cells challenged with replication stress [78 , 79] . Therefore , we hypothesized that upregulation of G1/S transition genes would also rescue dpb2-103 sensitivity to replication stress . We saw that indeed nrm1Δ bypasses the sensitivity of dpb2-103 cells in the presence of HU or MMS ( Fig 5A ) . Interestingly , this was not the case for ddc1Δ cells–deletion of NRM1 does not rescue HU sensitivity resulting from DDC1 deletion ( Fig 5A ) . Flow cytometry shows that asynchronous dpb2-103 cells have perturbed cell cycle , i . e . lower 1C DNA content and slow progression through S-phase ( Figs 2 and 5B ) . Moreover , light scattering measurements indicate that dpb2-103 cells are larger than wild-type cells ( Fig 5C ) . This observation is confirmed by microscopic observations of dpb2-103 cells ( S4 Fig ) . Deletion of NRM1 in dpb2-103 cells partially suppressed these effects: the DNA content in dpb2-103 nrm1Δ cells shows higher 1C DNA content and lower proportion of S-phase cells ( Fig 5B ) . Moreover , forward scatter ( FSC ) measurement indicates a decrease in cell size of dpb2-103 mutants after NRM1 deletion ( Fig 5C ) . Together these results demonstrate that upregulation of MBF G1/S transition genes rescues several dpb2-103 phenotypes . To support the hypothesis that activation of G1/S transition genes ( the Nrm1/MBF branch ) is impaired in dpb2-103 cells under replication stress , we tested the induction of TOS2 , TOS4 , MCD1 and CDC21 MCB genes repressed by Nrm1 and upregulated during S phase to promote cellular tolerance to replication stress [34] . Tos4 contains an FHA ( ForkHead-associated ) domain which interacts with components of the HADAC ( histone deacetylase ) complex involved in the response to various environmental stresses including replication stress [84] . Mcd1 is a subunit of cohesion complex involved in sister chromatide cohesion and chromosome condensation [85] , Tos2 is involved in morphogenesis [86] , Cdc21 is a thymidylate synthase [87] . Wild type and dpb2-103 cells were synchronized in G1 and released into S-phase in the presence of 200 mM HU . The amount of TOS2 , TOS4 , MCD1 and CDC21 RNA was normalized to wild-type untreated cells synchronized in G1 ( time “0” ) ( Fig 6A and S1 Table ) . In parallel , cell cycle progression of these cells was monitored by flow cytometry analysis of DNA content ( Fig 6B ) demonstrating that both wild-type and dpb2-103 cells reached the S-phase 60–90 minutes after release from G1 . Interestingly , a difference between wild-type and dpb2-103 cells , in transcription of these genes can be observed even in normal growth conditions . In wild-type cells , expression of G1/S transition genes is upregulated after release from G1 block , reaches the maximum level after 60 minutes , and decreases after 90–120 minutes . In contrast , in dpb2-103 cells , G1/S transition transcripts are most abundant after 30 minutes of growth and reach the minimum after 60–90 minutes . More important , HU-generated replication stress induced elevated transcription of TOS2 , TOS4 , MCD1 and CDC21 genes in wild-type cells but not in dpb2-103 cells as observed at 90 and 120 minutes time points . ( Fig 6A ) . We conclude there is a defect in the Nrm1 branch of the checkpoint pathway . Positive effects of nrm1Δ on dpb2-103 survival , DNA content and cell size suggest that nrm1Δ may restore viability of dpb2-103 ddc1Δ cells . Therefore , we introduced nrm1Δ into dpb2-103 ddc1Δ pMJDPB2 cells and attempted to obtain plasmid-free cells on 5-FOA . Indeed , in contrast to dpb2-103 ddc1Δ , we were able to obtain viable dpb2-103 ddc1Δ nrm1Δ cells without the plasmid carrying the gene encoding WT Dpb2 ( Fig 6C ) . Similar results were obtained after tetrad dissection from a dpb2-103/DPB2 ddc1Δ/DDC1 nrm1Δ/ nrm1Δ diploid strain ( Fig 6D ) , demonstrating that the lethal effect of the dpb2-103 ddc1Δ is suppressed by derepression of genes that are up regulated in checkpoint proficient cells challenged with replication stress . This strengthens our conclusion that the Nrm1 pathway is affected in dpb2-103 and that Dpb2 and Ddc1 are involved in two separate branches of the checkpoint activation pathway .
Early studies of Pol ε suggested that its catalytic subunit , Pol2 , is involved in replication checkpoint activation . This function has been mapped to the essential C-terminal part of the protein as shown in temperature sensitive pol2-11 and pol2-12 mutants , which encode subunits lacking 31 or 27 C-terminal amino acids , respectively [62 , 88] . Besides replication perturbations , these mutants demonstrate a subset of checkpoint deficiency phenotypes including impaired DUN1 activation after MMS or HU treatment , low viability and elongated spindle formation after release from G1 synchronization into HU [62] . Therefore , such pol2 mutations allow entry into mitosis despite uncompleted DNA replication . The C-terminus of Pol2 is also involved in the interaction with Dpb2 , the second essential subunit of Pol ε [58 , 59 , 63] . This interaction is facilitated by cell-cycle dependent phosphorylation of Dpb2 by CDK in late G1 phase . Inactivation of phosphorylation sites of Dpb2 in the pol2-11 strain dramatically reduces its viability , demonstrating that the Dpb2-Pol2 interaction is essential [89] . Pol2 also interacts with Mrc1 the fork-associated protein that mediates the Mec1-dependent activation of Rad53 [64] . Moreover , mrc1Δ pol2-11 cells are inviable and overexpression of MRC1 rescues pol2-11 temperature sensitivity [64] . The Dpb2 subunit of Pol ε plays an essential role in maintaining the proper architecture of the replisome as it links the Pol ε with GINS and therefore the CMG helicase complex ( Cdc45 , Mcm2-7 and GINS ) through the interaction of the Dpb2 with the Psf1 and Psf3 GINS subunits [53 , 55–57 , 90] . Therefore , it is not surprising that increased amounts of Dpb2 [22] , or the four subunits of GINS [53] suppress the pol2-11 mutation . Interestingly , both pol2-11 and dpb2-103 mutations impair interaction between Pol2 and Dpb2 [58 , 59 , 63] . Consequently , mutations in DPB2 affecting proper interactions of Dpb2 with either Pol2 or GINS may disrupt the replisome integrity and influence correct activation of the DNA replication checkpoint on the leading strand . Therefore , the dpb2-103 mutant encoding Dpb2-103 which has impaired interaction not only with the catalytic subunit Pol2 but also with Psf1 and Psf3 subunits of the GINS complex ( S2 Table ) , was a good candidate for studies of DNA replication checkpoint-defective phenotypes . Using flow cytometry , we found that dpb2-103 mutant cells , similarly to mec1-21 mutant cells , failed to delay DNA replication after release from G1 block into MMS . ( Fig 2 ) . Checkpoint deficient mutant cells ( mec1 or rad53 ) have been shown previously to be unable to delay replication progression [68 , 91] . However , when compared to mec1-21 , dpb2-103 cells under MMS treatment progress through the S phase slowly and hardly reach the G2 phase , suggesting possible residual checkpoint activation ( Fig 2 ) . Another agent that induces replication stress , HU , slows DNA replication progression due to nucleotide depletion [8] resulting in elongating the time of origin firing [92] . Our flow cytometry experiments with G1 synchronized cells released into HU confirm that wild-type cells remain in early S even after 240 minutes . In contrast , the dpb2-103 mutant released from G1 synchronization into HU progressed through the S phase , albeit slowly , likely due to insufficient nucleotide precursors ( Fig 2 ) . One can therefore speculate that S phase progression of this mutant under HU treatment results from inefficient delay of DNA replication combined with alleviation of nucleotide depletion by the slight induction of RNR3 expression ( Fig 4B ) . This hypothesis is reinforced by the observation that when compared to the wild type strain , dpb2-103 cells progress very slowly through unperturbed S phase ( Fig 2 ) . Checkpoint-deficient cells such as mec1 mutants are also unable to resume DNA synthesis after transient HU treatment ( S1 Fig ) [20 , 31] . However , in our experiments , dpb2-103 cells resume DNA replication after the HU-generated block is removed in both permissive and restrictive temperature ( S1 Fig ) . This observation explains why dpb2-103 cells are less sensitive to these drugs when compared to the mec1 mutant ( Fig 1 ) . We also observed that similarly to pol2-11 and pol2-12 mutants [88] untreated dpb2-103 cells are larger , ( Fig 5C ) , and that DNA content in asynchronous cells indicates that the relative proportion of S phase dpb2-103 cells is higher when compared to DPB2 cells ( Fig 5B ) demonstrating cell cycle control perturbations . However , the relative amount of cells in S phase may also be due to replication perturbations resulting from impaired interactions both within Pol ε and between Pol ε and the CMG helicase complex [59 , 61] . Nonetheless , these observations reinforce the conclusion that the cell cycle control is perturbed in the dpb2-103 mutant most probably due to inefficient replication checkpoint activation . Because Dpb2 is a subunit of Pol ε , the leading strand polymerase [38 , 41] , it has been suggested that its role in sensing replication perturbations is oriented mainly toward the leading strand [20] . Then , the signal from the lagging strand would come from the 9-1–1 ( Ddc1-Rad17-Mec3 ) checkpoint clamp which is loaded specifically at the 5’ junctions of RPA-coated ssDNA and duplex DNA [93] . The existence of two parallel leading and lagging strand-specific checkpoint activation pathways would explain partial checkpoint activation in the pol2 mutants , the dpb2-103 or the ddc1Δ cells . Flow cytometry analysis of DNA content in ddc1Δ cells treated with MMS demonstrated progression through S phase similar to that observed in our study for dpb2-103 cells [18] , although none of these mutants abolishes Rad53 phosphorylation in response to MMS or HU treatment [20 , 71] ( S3 Fig ) . Consequently , it was suggested that in S . cerevisiae ddc1Δ cells the partial checkpoint activation is mediated by Dpb11 recruited to the replication fork by Pol ε in a 9-1-1 independent manner [20 , 25] . Therefore , it is not surprising that our experiments showed that the double dpb2-103 ddc1Δ mutant is lethal ( Fig 3 ) , supporting a model of separate sensing of replication stress on the two DNA strands and points out the involvement of Dpb2 in this process . The interpretation that the synthetic lethality of dpb2-103 and ddc1Δ results from the fact that replication defects in dpb2-103 cells can only be bypassed by a proficient replication checkpoint is also possible . However , given that the ddc1Δ mutant is only partially impaired in checkpoint activation and even combined with the dpb11-1 mutation retains low Rad53 activity that prevent replication fork breakdown this would not explain the synthetic lethality of dpb2-103 and ddc1Δ . Therefore , we favor the hypothesis of separation of replication problems sensing on leading and lagging strands ( Fig 7A ) . SML1 deletion can rescue mec1Δ or rad53Δ lethality , although it cannot restore checkpoint activation . However , after tetrad dissection of a heterozygous triple mutant we obtained colonies of very sick dpb2-103 ddc1Δ sml1Δ strains ( S2 Fig ) , what is in accordance with our observation that the Dun1/Crt1 pathway , which regulates Sml1 degradation is properly activated in dpb2-103 cells . It also demonstrates that the lethal effect of mec1Δ or rad53Δ mutations ( rescued by sml1Δ ) results from replication checkpoint activation/execution defects other than those occurring in dpb2-103 ddc1Δ cells . The involvement of Pol ε and Pol δ in the majority of replication of the leading and lagging DNA strands , respectively , is very well documented . However , it has been proposed recently , that Pol δ is the major replicase of both DNA strands [94] . However , mutation rate data obtained using Pol2 and Pol3 mutants that incorporate unique strand specific substitutions and studies of ribonucleotide incorporation into DNA by Pol ε and Pol δ as well as DNA in vitro studies [37 , 40 , 95 , 96] strongly support the model in which Pol ε acts as the major leading strand DNA polymerase . Moreover , the model in which Pol δ is the major replicase of both strands still locates Pol ε in association with the CMG complex on the leading strand with a role in correcting replication errors and proofreading rNMPs [94] . The replication checkpoint activation results in phosphorylation of the Rad53 effector kinase and subsequent cellular response to DNA synthesis problems . The best analyzed branch of this response is the Rad53-Dun1-dependent upregulation of dNTP pool [33 , 75] which normally limits DNA replication , but is upregulated 6- to 8-fold under replication stress to promote fork progression [8 , 97] . Interestingly , although the dpb2-103 mutant is impaired in correct response to replication stress , we detected Rad53 phosphorylation ( S3 Fig ) , degradation of the RNR inhibitors Sml1 and induction of expression of the RNR3 and HUG1 genes ( Fig 4 ) . We can therefore conclude that dpb2-103 cells activate the Dun1-Crt1 branch of replication stress response correctly ( Fig 7B ) . This suggests that Pol ε checkpoint mutants are partially proficient in activation of first steps of replication stress response although with modifications in the pattern of Rad53 phosphorylation . Such incomplete phosphorylation may be undetectable in the gel retardation assay of Rad53 which has been shown to contain multiple phosphorylation sites [98–100] . Alternatively , the Pol ε signal may act downstream of or independently of Rad53 in checkpoint activation . Importantly , these observations rule out the possibility that inefficiency of replication checkpoint activation in dpb2-103 cells results from the fact that the number of affected origins is not high enough to reach an activation threshold as demonstrated for the orc2-1 mutant defective in initiation of DNA replication and Rad53 phosphorylation under MMS treatment [16] . The results of our analysis of the second branch of Rad53-dependent response to replication stress , the Nrm1/MBF pathway , clarify the checkpoint-deficiency phenotypes of the dpb2-103 mutant . Rad53-dependent phosphorylation of the Nrm1 corepressor of MBF genes prevents its binding to MBF promoters in response to the S phase checkpoint [80] to activate expression of many genes involved in the replication stress response [36] . We found that NRM1 deletion suppresses the MMS and HU sensitivity of dpb2-103 cells ( Fig 5A ) and that dpb2-103 nrm1Δ cells partially rescue their cell size as well as their DNA content , demonstrating proficient progression through S phase ( Fig 5B and 5C ) . We also tested nrm1Δ dependent checkpoint-deficiency phenotypes rescue in pol2-12 cells . Our flow cytometry analysis shows , that the pol2-12 mutant demonstrates a defect in S-phase progression ( although less severe when compared to dpb2-103 cells ) , and that deletion of NRM1 restores proper DNA content ( S5 Fig ) . Moreover , NRM1 deletion alleviates pol2-12 HU and temperature sensitivity ( S5 Fig ) . These results suggest that the dpb2-103 and pol2-12 mutations in Pol ε may similarly affect the replication stress response . The observed nrm1Δ dependent rescue of dpb2-103 phenotypes during unperturbed growth can be explained by upregulation of Nrm1-regulated genes at the G1/S transition , which are prematurely downregulated in dpb2-103 cells , when compared to the wild-type cells ( Fig 6 ) . Moreover , we observed that expression of Nrm1-repressed genes TOS2 , TOS4 , MCD1 , CDC21 , which is upregulated in wild-type cells even after 90–120 minutes in response to replication stress , remain uninduced in dpb2-103 cells ( Fig 6 ) . Finally , the lack of Nrm1 repressor ( nrm1Δ ) partially rescued the synthetic lethality of dpb2-103 ddc1Δ cells; a similar lethality bypass by nrm1Δ has been observed for rad53Δ and mec1Δ mutants [78 , 79] . This demonstrates that checkpoint deficiencies in dpb2-103 cells are due mainly to impaired derepression of Nrm1-regulated genes ( Fig 7B ) . However , uncovering of the mechanism of Dpb2-dependent derepression of the Nrm1 branch of the replication stress response needs further investigation . The question remains whether the failure of dpb2-103 mutant to fully activate the replication checkpoint results from direct involvement of the Dpb2 in replication stress sensing / activation , protein stability changes , impaired phosphorylation or from defects in Pol ε association within the replisome . Indeed , mutations in dpb2-103 partially impair interaction of Dpb2 with the catalytic subunit Pol2 of Pol ε and strongly impair its interaction with the Psf1 subunit of GINS . However , it would be expected that the destabilization of Pol ε in the replication fork and possibly dissociation would induce replication checkpoint activation rather than abolish it . Therefore , we favor the hypothesis of direct involvement of Dpb2 in the replication stress response , which still needs further investigation .
S . cerevisiae strains listed in Table 1 . were grown in standard media [101] [102] . When nutrition selection was not required yeast complete medium YPD ( 1% bacto-yeast extract , 2% bacto-peptone , 2% glucose liquid or solidified with 2% bacto-agar ) was used . Yeast transformants were selected on YPD supplemented with appropriate antibiotics ( Hygromycin B 300 μ-ml or Nourseothricin 100 μg-ml ) . When necessary yeasts were selected for prototrophy on YNBD minimal medium ( 0 . 67% yeast nitrogen base without amino acids , 2% glucose , liquid or solidified with 2% bacto-agar ) supplemented with appropriate amino acids and nucleotides . For selection of URA3-plasmid-free cells , YNBD medium supplemented with 1 mg/ml 5-fluoroorotic acid ( 5-FOA ) was used [103] . Escherichia coli DH5α ( F- , gyrA96 , recA1 , relA1 , endA1 , thi1 , hsdR17 , supE44 , deoR , Δ ( lacZYA-argF ) U169 , [φ80Δ ( lacZ ) M15] ) cells were grown routinely at 37°C in L broth–liquid or solidified with 1 . 5% agar and supplemented when needed with ampicillin ( 100 μg-ml ) . For MMS sensitivity tests , yeast strains were grown in YPD medium until OD600 reached 0 , 6 , harvested and resuspended in 0 , 9% NaCl . Appropriate dilutions were plated on YNBD medium supplemented with MMS ( 0% , 0 , 01% , 0 , 02% or 0 , 03% ) . Colonies were counted after 5 days incubation at 23°C . For HU sensitivity tests , yeast strains were grown in YPD medium until OD600 reached 0 , 6 before adding HU to 200 mM final concentration . Samples were collected at indicated time points , washed with distilled water and plated on YNBD medium . Colonies were counted after 5 days incubation at 23°C . Yeasts were precultured overnight in YNBD medium at 23°C and appropriately diluted in YNBD medium to grow at 23°C until OD600 reached 0 , 4 . Cells were harvested , resuspended in fresh YNBD medium with the α-factor mating pheromone ( 4 mg/ml ) and grown for 2–3 hours at 23°C . Then to release them from α-factor , cells were harvested and washed three times with water . Next they were released from G1-arrest into fresh YNBD medium and incubated at 23°C . When necessary , cells were released from G1-arrest in YNBD medium containing 0 , 05% MMS or 200 mM HU . Samples were taken at indicated time points and fixed in 70% ethanol . Ethanol-fixed cells were harvested , washed and resuspended in 1 ml of sodium citrate ( 50 mM , pH 7 , 0 ) . After brief sonication they were treated with RNaseA ( 0 , 25 mg-ml ) at 50°C for 1 hour and with proteinase K ( 1 mg/ml ) for another hour at 50°C . Then , samples were diluted in sodium citrate containing propidium iodide ( 16 μg-ml ) and incubated overnight at 4°C . The DNA content was identified by measuring the propidium iodide fluorescence signal ( FL2 ) using Becton Dickinson FACSCalibur and the CellQuest software ( BD Bioscience ) . To evaluate the size of yeast cells , the forward scatter ( FSC ) was analyzed . Yeast cells were grown until OD600 reached 0 , 4–0 , 6 . Then , 0 , 05% MMS or 200 mM HU was added and cells were grown for 2 h . Cells were collected and prepared for SDS-PAGE as described previously [107] . For immunodetection , goat polyclonal anti-Rad53 antibody ( sc-6749 ) from Santa Cruz Biotechnology ) , rabbit polyclonal anti-Sml1 antibody ( AS10 847 ) from Agrisera and mouse monoclonal anti-actin antibody ( MAB1501 ) from Millipore were used . Total RNA was isolated using the Syngen Tissue RNA Mini Kit ( Syngen Biotech , POLAND ) as indicated in the manufacturer’s instruction . Reverse transcription was performed using the RevertAid™ First Strand cDNA Synthesis Kit ( ThermoFisher Scientific ) and Real-Time PCR was done using Real-Time 2xHS-PCR Master Mix SYBR ( A&A Biotechnology ) and LightCycler 480 ( Roche ) . Transcript levels were normalized to actin mRNA ( ACT1 ) .
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The viability of living organisms depends on the integrity of their genomes . Each cell has to constantly monitor DNA replication and coordinate it with cell division to avoid genomic instability . This is achieved through pathways known as cell cycle checkpoints . Therefore , upon replication perturbation , DNA synthesis slows down and cell division is delayed . For that , a specific signal is induced and propagated through a mechanism that have already been identified but still need investigations . We have isolated a mutated form of Dpb2 , the essential subunit of DNA polymerase epsilon ( Pol ε ) holoenzyme . This mutated form of Pol ε impairs proper activation of the cellular response to replication stress . We show that yeast cells with mutations in the DPB2 gene fail to activate the Nrm1-regulated branch of the checkpoint , which controls numerous genes expressed in response to replication stress . Moreover , our results support the model of parallel activation of replication checkpoint from the leading and lagging DNA strands . This strongly suggests that Pol ε , the leading strand replicase , is involved in replication checkpoint activation from this strand . Our results contribute to the understanding of mechanisms of cellular response to replication stress , which are necessary to preserve genome stability .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"phosphorylation",
"flow",
"cytometry",
"cellular",
"stress",
"responses",
"cell",
"cycle",
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"cell",
"division",
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"saccharomyces",
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"proteins",
"spectrophotometry",
"yeast",
"biochemistry",
"cytophotometry",
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"acids",
"post-translational",
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] |
2017
|
Mutations in the Non-Catalytic Subunit Dpb2 of DNA Polymerase Epsilon Affect the Nrm1 Branch of the DNA Replication Checkpoint
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Mycetoma is a devastating neglected tropical disease , caused by various fungal and bacterial pathogens . Correct diagnosis to the species level is mandatory for proper treatment . In endemic areas , various diagnostic tests and techniques are in use to achieve that , and that includes grain culture , surgical biopsy histopathological examination , fine needle aspiration cytological ( FNAC ) examination and in certain centres molecular diagnosis such as PCR . In this retrospective study , the sensitivity , specificity and diagnostic accuracy of grain culture , surgical biopsy histopathological examination and FNAC to identify the mycetoma causative organisms were determined . The histopathological examination appeared to have better sensitivity and specificity . The histological examination results were correct in 714 ( 97 . 5% ) out of 750 patients infected with Madurella mycetomatis , in 133 ( 93 . 6% ) out of 142 patients infected with Streptomyces somaliensis , in 53 ( 74 . 6% ) out of 71 patients infected with Actinomadura madurae and in 12 ( 75% ) out of 16 patients infected with Actinomadura pelletierii . FNAC results were correct in 604 ( 80 . 5% ) out of 750 patients with Madurella mycetomatis eumycetoma , in 50 ( 37 . 5% ) out of 133 Streptomyces somaliensis patients , 43 ( 60 . 5% ) out of 71 Actinomadura madurae patients and 11 ( 68 . 7% ) out of 16 Actinomadura pelletierii . The mean time required to obtain the FNAC result was one day , and for the histopathological examinations results it was 3 . 5 days , and for grain it was a mean of 16 days . In conclusion , histopathological examination and FNAC are more practical techniques for rapid species identification than grain culture in many endemic regions .
Mycetoma is a chronic granulomatous subcutaneous inflammatory infection , endemic in subtropical and tropical regions , but it is reported globally [1 , 2] . It is characterised by a painless subcutaneous swelling , multiple sinuses formation and a discharge that contain grains [3 , 4] . The clinical presentation can give a clue to the diagnosis , but without further diagnostic testing it will lead to misdiagnosis and inaccurate treatment [5] . Mycetoma can be caused by different bacteria ( actinomycetoma ) or fungi ( eumycetoma ) [6 , 7] . More than 70 different micro-organisms were reported to cause this infection , and hence it is essential to identifying the causative agents to the highest level of resolution which in turn will contribute to choosing appropriate treatment [8 , 9] . In endemic regions , the most commonly used tools are culturing of the grains , surgical biopsy followed by histopathological examination and fine needle aspiration cytological ( FNAC ) examination [10 , 11] . Currently , culturing the grains culture is still considered to be the golden standard for species identification in many centres [12 , 13] . However , this technique is tedious , time-consuming due to the slow growth rate and it needs expert microbiologists to identify the causative agents based on the macroscopic appearance of the isolates . Furthermore contamination is common . Patients on medical treatment may have non-viable gains , and hence it is difficult to identify the causative organism [14 , 15] . To overcome these difficulties , histological examination is often used complementary to culture . In a histopathological examination , it is easy to discriminate between fungal and bacterial causative agents [16 , 17] . However , identification to the species level is more challenging and considered far from reliable [18 , 19] . At the Mycetoma Research Centre ( MRC ) , University of Khartoum , Khartoum , Sudan FNAC is a common tool to identify the causative organisms . It is less invasive and time-consuming compared to the histopathological and culture techniques [20 , 21] . However , to the best of our knowledge there was no study performed in which the sensitivity and specificity of the two techniques for the identification of the mycetoma causative organisms were compared . With this background , this study was conducted at the Mycetoma Research Centre were 8500 confirmed mycetoma patients were seen and treated . In this retrospective study , the records of these patients were reviewed , and patients who undergone the three diagnostic tests were included .
Following the Mycetoma Research Centre Institutional Review Board ethical approval , all the histopathological , cytological and microbiological reports of the patients seen in the Mycetoma Research Centre over a 27-year period ( January 1991 to January 2018 ) were reviewed . The data were collected in the pre-designed data collection sheet . The patient demographic characteristics , results of the three techniques were collected . In this study , only patients in whom the causative organisms were identified by culture and had undergone both a fine needle aspirate for cytological examination and deep-seated excisional biopsy for histopathological examination were included . ( Fig 1 ) . The number of true-positive ( TP ) , false-positive ( FP ) , true-negative ( TN ) , and false-negative ( FN ) test results was calculated for each technique and was compared to the culture which considered as our gold standard method . According to these results , sensitivity , specificity , positive predictive value , negative predictive value , and accuracy were calculated for each test . Accuracy was calculated as the proportion of true results ( both true positives and true negatives ) among the total number of cases . The grains were obtained by surgical biopsy and/or FNAC . For the latter , a 25-gauge needle was inserted into the lesion , and aspirates were taken . The yield of grains was assessed visually by the number and size of grains obtained . If the yield was low , a second aspiration was taken with a 23-gauge needle . When excessive bleeding from the lesions was encountered , a 27-gauge needle was used . The obtained sample usual splited into two parts; one was transported immediately to the microbiology department for culturing , the other part was sent to the histology department for histopathological and cytological . The mycetoma grains were washed three times in sterile normal saline . When a fungus was expected based on the clinical data accompanied the request form and the grains color and consistency ( fungal grains tend to be hard and are either white , yellow and black in color according to the causatives agent ) , the grains were cultured on Sabouraud dextrose agar with gentamicin sulphate ( 400 μg/ml ) for fungal grains at 37 °C . When an actinomycete was expected according to the clinical data and ultrasound report as well as the grain color and consistency ( actinomycetoma grains tend to be soft and smooth ) grains were cultured on Blood agar , Colombia agar , Glucose yeast extract agar , Brain-heart infusion , Löwenstein—Jensen agar and Modified Sabouraud agar supplemented with 0 . 5% yeast extract . After that the plates were incubated at 37°C for 7 to 14 days . When a fungus was grown on the Sabouraud plate , it was identified based on its macroscopic and microscopic morphology . Table 1 demonstrate the characteristic of different eumycetoma causatives agent for macroscopically and microscopically identification . Macroscopically the appearance of M . mycetomatis colonies is quite variable . At the beginning the colonies tend to be white , and upon maturing they change to yellow or brown . Some strains of M . mycetomatis are able to produce a brown pigment in the culture . The texture varies from smooth , flat or heaped . Madurella mycetomatis is differentiated from T . grisea by its ability to grow at temperatures up to 40°C and its inability to assimilate sucrose ( Table 2 ) . When colonies are obtained , presumptive species identification is based on macroscopical and microscopical appearance of the species . Typical colonies of Nocardia spp and Streptomyces spp are dry to chalky in consistency , usually folded . The color will range from yellow to gray white . A . madurae and A . pelletieri strains produce cream- and red-pigmented mycelia respectively and lack aerial filaments on initial isolation . Ziehl-Neelsen staining is used to determine if the isolate is acid fast . Nocardia spp will stain positively and Actinomadura spp will stain negatively . Different biochemical tests will be performed to identify the causative agent to the species level . These include the degradation of adenine , casein , and hypoxanthine; growth on adonitol; aesculin hydrolysis; glycerol; glycogen; D-raffinose; L-rhamnose; D-turanose; D-xylose; and L-aspartic acid ( Tables 3 and 4 ) . The aspirate was allowed to air dry and was stained using Diff-Quick stain . The stained aspirates were examined by an expert histopathologist for the presence of the following cytomorphological features: smears cellularity , the host inflammatory tissue reaction , the presence and types of the causative organisms’ grains . Species identification was based on species-specific criteria . In general , M . mycetomatis grains can be either small or large , are light to dark brown in colour and have irregular outlines and a crushing artefact when stained with hematoxylin and eosin ( H&E ) ( Fig 2A ) . S . somaliensis grains are difficult to see in H&E stained sections , they stain bright pink to hazy pink in colour , are often oval to irregular shaped and can be as aggregates ( Fig 2C ) . A . madurae grains are small oval shaped , and it stained pink to red colour in H&E and tend to be as one mass without any fractures . A . pelletierii grains are small rounded to oval shaped , and they stained deep blue in H&E stained sections and tend to be fractured . All patients underwent surgical biopsy under anaesthesia , which was fixed in 10% formalin and processed further into paraffin blocks . 3-5-μm sections were obtained and stained with H&E . In our Histopathological laboratory the histopathologist issued the report with the species name according to the following criteria i . e . species specific criteria which have been used by all of them . M . mycetomatis grains tend to be large , light to dark brown in colour with irregular outlines . They tend to fracture when sections are cut . M . mycetomatis has two different types of grains , and these are the filamentous and vesicular . The filamentous type , is the most common type and consists of brown septated and branched hyphae that may be slightly more swollen towards the edges ( Fig 3D ) . S . somaliensis grains are rounded to oval in shape , with homogenous appearance in tissue sections . They appear faint yellow in unstained sections , and the grains are not well stained with H&E . Moreover , as a result of sectioning they may show longitudinal cracks , the filaments are fine ( measured between 0 . 5–2 μm in diameter ) , closely packaged and embedded in cement matrix ( Fig 3B ) . A . pelletierii grains are small , round to oval in shape and semicircular and sickle like shapes have been observed as well . The filamentous structures are pretty difficult to be detected . However , a careful and meticulous examination of the periphery of the grains may show some of them . A . pelletierii grains stain deep violet with H&E , which is very characteristic and allows the definitive diagnosis without a need for culturing techniques ( Fig 3C ) . A . madurae grains ranged from yellow to white . Therefore , it can be difficult to discriminate them from the surrounding fat . Histologically the grain size ranges from small to large . The large grains have a characteristic variegated pattern . The periphery of the grain is opaque , homogenous and deep purple when stained with H&E stain , while the centre is less densely stained . Additionally , the periphery of the grains shows an eosinophilic material ( Fig 3A ) . Smaller grains are more homogeneous and are difficult to distinguish from A . pelletierii . However , even the small grains of A . madurae have a more deeply stained purple fringe , which is not seen in A . pelletierii .
In this study , 991 patients out of 7940 patients were eligible and were included in the analysis . Their ages ranged between 5 and 75 years old . The majority were males 737 ( 74 . 3% ) , and most of them were students 327 ( 32 . 9% ) and farmers 167 ( 16 . 8% ) . The majority of the patients ( 837 out of 991 ) , gave a history of discharge that contained grains and the majority of these grains were black ( 565; 57% ) ) followed by yellow ( 104;10 . 5% ) , white ( 60; 6 . 1% ) and red grains ( 14; 1 . 4% ) . In this cohort , the majority of patients , ( 72 . 6% ) had no history of local trauma , only 191 ( 19 . 3% ) patients did recall a local trauma and the remaining 73 ( 7 . 4% ) patients were not certain . Based on the culture reports of the grains , in 750/991 ( 75 . 6% ) of the patients the mycetoma was caused by M . mycetomatis , in 142/991 ( 14 . 4% ) it was caused by S . somaliensis , in 71/991 ( 7 . 16% ) it was caused by A . madurae and in 16/991 ( 1 . 6% ) it was caused by A . pelletieri . In 11 patients no growth was reported from the grains obtained during the sample collection . The time to growth differed case by case and ranged between 5 and 28 days . In this study , out of the 991 mycetoma cases , the correct species identification was obtained for 912 cases using histopathological examination . Using FNAC , the correct diagnosis was obtained in 708 cases . The histopathological examination confirmed the diagnosis of M . mycetomatis in 714 of 750 cases with 95 . 2% sensitivity , 95 . 4% specificity and diagnostic accuracy of 95 . 3% . For FNAC only 604 out of 750 M . mycetomatis cases were identified , resulting in a sensitivity of 80 . 5% , a specificity of 88 . 4% and a diagnostic accuracy of 82 . 4% . Out of 142 S . somaliensis cases , 133 were also identified with histopathological examination with 93 . 7% sensitivity , 98 . 9% specificity and diagnostic accuracy of 98 . 2% . With FNAC only 50 out of 133 S . somaliensis cases were identified , resulting in a sensitivity of 35 . 2% , a specificity of 99 . 3% and a diagnostic accuracy of 90 . 1% . 53 out of 71 cases with A . madurae identification were identifuied by histopathological examination , with a sensitivity of 74 . 7% , 99 . 5% specificity and diagnostic accuracy of 97 . 7% . FNAC identified 43 out of 71 cases with a sensitivity of 60 . 6% , specificity of 94 . 4% and diagnostic accuracy of 91 . 9% . For A . pelletierii out of 16 cases; 12 were also identified with histological examination with 75 . 0% sensitivity , 100% specificity , and diagnostic accuracy of 99 . 6% . For FNAC a sensitivity of 68 . 8% , a specificity of 99 . 7% and a diagnostic accuracy of 99 . 2% were obtained . With the histopathological examination , false negative result was reported in 36/750 M . mycetomatis cases , 9/142 S . somaliensis cases , 18/71 A . madurae cases and 4/16 A . pelletieri cases . To determine the false negative results reasons , the histopathological slides were re-examined . There were various reasons for the false negative , and that included the absence of mycetoma histopathological architecture resulted in overlooking the causative agent ( Fig 4 ) . Furthermore , in some blocks , the grains were absent; either because the tissue was not homogenously infected by the causative agent and that the part which was taken for histology or the section contained no grains . This latter might be overcome by examining multiple sections at different depths of the histology blocks especially when inflammation and necrosis are noted . False positive results were obtained in 28 of the cases . This was attributed to the presences of numerous structures that can mimic the appearance of M . mycetomatis and that included vegetables , synthetic fibres and algae which can resemble fungal hyphae and calcification ( Fig 5 ) . In overall , using histology correct species identification was obtained in the majority of cases . The mean time to identify the culture isolates was 16 days ( range 5 to 28 days ) , for histology it was 3 . 5 days ( range 2 to 5 days ) , and for cytology , it was one day ( range 1 to 2 days ) . This demonstrated that reliable species identification using histology was obtained in 92 . 0% of cases within an average time reduction of 13 . 5 days , for cytology this was 71 . 4% of cases with time reduction of 15 days , indicating that adding histology or cytology to the diagnostic techniques used for species identification resulted in an earlier start of treatment .
The accurate identification of mycetoma causative agents is considered the cornerstone for the initiation of appropriate therapy . Hence a rapid and accurate diagnostic tool to achieve the definitive species identification is considered a critical part in patient treatment and management [21–23] . Different laboratory techniques for species identification are in use , including culture , histopathology , [7 , 25] , FNAC [8 , 24] , serological assays and imaging [26–29] as well as different molecular diagnostic tools [30–35] . However , not all these assays are available in endemic regions . In the Mycetoma Research Centre , culturing of the grains , histopathology and FNAC are routinely performed and have been used for the past 27 years . In this communication we have used the data collected for the last 27 years to assess the sensitivity , specificity and diagnostic accuracy of histopathology and cytology in the identification of mycetoma causative agents in comparison to the current golden standard: culturing . This study showed that the histopathology was more accurate to FNAC in terms of species identification . Our results are in line with that reported previously by Yousif and colleagues [36] . They reported 90 . 9% agreement when histopathology was compared to FNAC for the diagnosis of M . mycetomatis ( 90 . 9% ) while for actinomycetoma causative agents it was only 60% . The lower diagnostic agreement of actinomycetoma causative agents could have been caused by morphological similarities of these microorganisms . Furthermore , both techniques are operator dependent and need intensive training and experience which could have its reflections on the accuracy . Mycetoma can be caused by more than 70 different causative agents [37] , but the distribution of these species is not everywhere the same which could cause differences in diagnostic accuracy in different regions . In some of the mycetoma endemic regions , mycetoma is caused by closely related species . Morphologically these organisms may look similar which could cause a challenge in the identification of these organisms . In Mexico , the most common causative agents are Nocardia brasiliensis and Nocardia asteroides [37] , two closely related species which are difficult to differentiate from each other based on histopathology [38 , 39] . In Senegal , the most common causative agents of eumycetoma are M . mycetomatis and Falciformispora senegalensis which both can cause black grain mycetoma [37 , 40] . In the black grains of F . senegalensis , the centre is non-pigmented , and the cement is absent , whereas at the peripheries the grains are dark coloured and brown cement is present . However , this is also seen in black grains of Trematosphaeria grisea and certain grains of M . mycetomatis . Hence an expert pathologist is needed to differentiate between these organisms [41] . The study performed here was a retrospective study , looking back at the records of the Mycetoma Research Centre for the past 27 years . During that time molecular identification of the causative agents was not performed and culture was considered the golden standard . Recently in the study conducted by Borman and colleagues demonstrated that using morphological identification , misidentifications occurred in many cases [42] . Out of 28 previously identified Trematosphaeria grisea isolates , 22 were , other fungal species [42] . For actinomycetoma causative organisms , misidentifications also have been described . In 2008 , Quintana and associates demonstrated that half of the S . somaliensis isolates obtained from Sudan appeared to be Streptomyces sudanensis [43] . Furthermore , next to A . madurae and A . pelletieri also Actinomadura latina was described [44] . Therefore a current ongoing study is including molecular diagnosis to determine the true etiology and predictive value of culture , FNAC and histology . With the introduction of molecular diagnosis in our centre we already made the first step in this respect . In this study , the sensitivity of histopathological technique was superior to that FNAC for all species tested . In that study they studied the performance of FNAC in comparison to histology in 19 different mycetoma patients . Out of these 19 patients , five patients had to be excluded due to inadequate aspirated materials . From the 14 remaining patients , 10 were diagnosed as M . mycetomatis with histopathology , and 4 were actinomycetoma . With this limited number of patients they could conclude that FNAC could identify the causative agent in 9 out of 10 M . mycetomatis patients . One patient identified by histology could not be identified with cytology , again confirming that histology was superior to FNAC in respect to species identification [15] . A result confirmed in our current study , as in our study 146 patients with M . mycetomatis mycetoma were missed with FNAC . However , of the three different identification methods used , FNAC was the most rapid and resulted in species identification within 1 day , instead of 3 . 5 days for histology or 16 days for culture . FNAC is a simple and rapid diagnostic technique which can be used at the one-stop diagnosis clinic and in epidemiological and field surveys . However , it has many limitations: it is an operator dependent technique can be painful and can lead to deep-seated bacterial infections . FNAC is less invasive than a deep-seated biopsy , as only a small puncture hole is obtained . With a deep-seated biopsy a larger area of the lesion is removed thereby also exposing a larger part of the lesion to secondary bacterial infections and creating a bigger risk for dissemination of the infection . Currently , deep-seated biopsies are only performed to obtain a diagnostic sample , not to reduce the burden of infection at the site of the lesion . This , because the lesion can be extensive , even when on the outside only a small lesion is seen . At the moment the fine needle aspirate is often taken blindly without guidance of ultrasound imaging which creates a risk that the operator might miss the pockets which contains grains . With the use of the ultrasound-guided aspiration , the diagnostic yield of the technique will improve which in its turn could enhance the number of cases in which positive species identification might be obtained . The grains culture remains in many centres the cornerstone for the diagnosis of mycetoma . moreover , morphological identification of mycetoma causatives agent may be some times be difficult to achieved due to the overlapping and similarities encountered between different species as demonestrated in Table 1 , However culture is a time-consuming procedure and an experienced microbiologist is needed to identify the organisms to the species level [45] . Cross-contamination is a common problem . Recently an identification scheme of eumycetoma causative agents has been published which was based on the pysiological properties of the causative agent , indicating that it is possible to use physiological properties to identify eumycetoma causative agents more reliably [1] . These include culturing at 37°C and growth on actidione , L-sorbose , glycetol , potassium 2-keto-gluconate , methyl-D-glucopyranoside , inositol and D-sorbitol . However , these tests are more time consurming and delay the identification of the causative agent which will potentially leed to delay in patient treatment . However , by complementing culturing with histology or FNAC a preliminary identification might be obtained earlier . Especially , since the treatment of mycetoma infections is dependent on the causative agent . Actinomycetoma is treated differently than eumycetoma . [22] . Correct identification to the species level will influence clinical decision making . The first diagnostic discrimination needed is the distinction between actinomycetoma causative agents and eumycetoma causative agents , since this would implicate either antibacterial treatment or a treatment based on surgery and antifungal treatment . In this situtation fine needle aspiaration cytology and histopathological examination were found to be highly sensitive in the discrimination between actinomycetoma from eumycetoma based on the morphological characteristics; therefore , and based on our experience we recommended the used of FNAC in a low resources centers like in the rural centers were there is no Histopathology laboratories . For the actinomycetoma causative agents , it is currently not known if the choice of antibacterial agent is dependent on the causative agents and studies are needed to evaluate if differrent treatment regimens are needed for each of the different bacterial causative agents . When this appears to be the case , identification to the species level becomes essential . For eumycetoma causative agents , surgery is always combined with itraconazole . However , Medicopsis romeroi and Madurella fahalii , were not susceptibile towards itraconazole in vitro . This would indicate that discrimination between these species and the susceptible species would be mandatory and could potentially effect clinical management . Clinical evaluation of such cases and the effect on the therapeutic success rates are needed . However , before such a study can be performed proper species identification needs to be obtained . In summary , the Cytopathologist/Histopathologist need to be aware of the many mimics that can look like the mycetoma causatives agent , and we highly recommended that the pathologists after issuing the diagnosis should recommend correlation with microbiology or provide a cautionary statement to advise clinicians of the limitations of identifying organisms with histopathologic/cytopathologic examination .
|
In mycetoma endemic regions , the medical and health settings are commonly suboptimal , and only a few diagnostic tests and techniques are available . That had badly affected the patients’ proper diagnosis and management and thus the late presentation of patients with advanced disease . In this retrospective study , the experience of the MRC on the common in use diagnostic tests in the period between 1991 and 2018 is presented . In this study , the sensitivity , specificity rates and diagnostic accuracy of grain culture , surgical biopsy histopathological examination and FNAC to identify the mycetoma causative organisms were determined . The histopathological examination appeared to have better sensitivity and specificity . Furthermore , the grain culture identification needs high experience , it is the tedious procedure , and cross-contamination is common hence misdiagnosis is frequent . It can be concluded that histopathological examination and FNAC are more practical techniques for rapid species identification than grain culture in many endemic regions with poor diagnostic setting .
|
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2019
|
The Accuracy of Histopathological and Cytopathological Techniques in the Identification of the Mycetoma Causative Agents
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Synaptic receptors in the human brain consist of multiple protein subunits , many of which have multiple variants , coded by different genes , and are differentially expressed across brain regions and developmental stages . The brain can tune the electrophysiological properties of synapses to regulate plasticity and information processing by switching from one protein variant to another . Such condition-dependent variant switch during development has been demonstrated in several neurotransmitter systems including NMDA and GABA . Here we systematically detect pairs of receptor-subunit variants that switch during the lifetime of the human brain by analyzing postmortem expression data collected in a population of donors at various ages and brain regions measured using microarray and RNA-seq . To further detect variant pairs that co-vary across subjects , we present a method to quantify age-corrected expression correlation in face of strong temporal trends . This is achieved by computing the correlations in the residual expression beyond a cubic-spline model of the population temporal trend , and can be seen as a nonlinear version of partial correlations . Using these methods , we detect multiple new pairs of context dependent variants . For instance , we find a switch from GLRA2 to GLRA3 that differs from the known switch in the rat . We also detect an early switch from HTR1A to HTR5A whose trends are negatively correlated and find that their age-corrected expression is strongly positively correlated . Finally , we observe that GRIN2B switch to GRIN2A occurs mostly during embryonic development , presumably earlier than observed in rodents . These results provide a systematic map of developmental switching in the neurotransmitter systems of the human brain .
Chemical synapses are complex molecular structures allowing neurons to communicate and process information . In mammals , the molecular composition of most post-synaptic receptors has been characterized , together with their downstream signal transduction pathway [1–3] . Interestingly , many of the key synaptic proteins have multiple variants coded by similar genes , and the actual protein composition of synapses changes across brain regions [4–6] , life periods [7 , 8] and medical conditions [9] . Changing the protein composition of a synapse by switching from one protein variant to another , or changing the fraction of synapses or cells expressing a protein variant , allows the brain to tune various biophysical properties of synapses . These include the temporal profile of synaptic currents , or its plasticity characteristics [10–13] . Presumably , the brain can select which protein variants would be used in a given condition to tune synaptic characteristics to match the condition [14] . We refer here to proteins that have such variants as condition-dependent variants ( CDVs ) . The idea of protein variants that are utilized differently along development can be well illustrated by the NMDA receptor . This predominant receptor controlling synaptic plasticity has been shown to “switch" its protein composition during development [11 , 15 , 16] . NMDA receptors consist of four units . Two of these units are NR1 subunits , expressed in virtually all neurons . The other two non-NR1 subunits , NR2A—D and NR3A-B have a distinct expression pattern through life [11 , 17 , 18] . In the prenatal and neonatal rat brain , NMDA receptors predominantly contain NR1/NR2B , but their protein composition later changes such that by adulthood ( 14–16 weeks ) receptors contain mostly NR1/NR2A . The change in subunit composition from NR2B to NR2A affects the kinetics of excitatory post-synaptic currents ( EPSCs ) [10–12] , the binding-site affinity[19] and the sensitivity to pharmacological agents [11 , 20] , together affecting synaptic plasticity and information processing . Age-dependent changes of NMDAR subunit expression were further detected in several brain structures ( cortex [12 , 20 , 21] , cerebellum [5 , 22] , and hippocampus [10] ) and species ( human [15] , mice [23 , 24] ) , and were further validated using protein profiling [25] . Similar age-dependent changes have been reported in AMPA [26] , Kainate [27] , and GABA [28] receptors . Tuning the variant composition of synaptic receptors has been suggested to be a widely used mechanism underlying meta-plasticity [29–31] . Given the evidence for age-dependent changes in protein variant composition , the natural question arises: which synaptic proteins in the human brain switch their variant subunits , at what periods during development , and in which brain regions ? Previous studies focused on a handful of molecules in specific brain structures , reporting switches in small groups of proteins and regions . This paper aims to systematically map potential CDVs by detecting pairs of genes whose RNA expression profiles switch through development . Clearly , a switch observed in the expression profile of a gene pair does not necessarily reflect a switch at the protein level , and these candidates have to be further validated using proteomic experiments . When considering developmental switches , two types of correlations between pairs of genes are of interest . First , as demonstrated in Fig 1 , the expression trends of two genes along life may be ( anti ) correlated . This correlation reflects changes that are driven by age , and are consistent across the population of sampled brains . Importantly , two genes might have opposing or similar ageing trends even if they are not directly co-regulated , simply because they follow opposite trends through life . A second important aspect of switching protein variants is that they may be commonly controlled , either through direct regulatory mechanisms such as transcription regulation , splicing and RNA editing , or through indirect control mechanisms involving brain-wide systems and pathways . Common regulation of a protein pair , either direct or indirect , can be reflected in the two genes fluctuating together from one subject to another . We address these two types of relations when characterizing developmental Switching . Changes in expression profiles could be interpreted as reflecting several different underlying physiological changes . Since expression is usually measured at the resolution of tissue layer or brain area , expression changes could reflect changes at the level of synapses , cells or tissue . First , within-synapse changes , where individual synapses change their protein composition such that one protein variant replaces another . Second , within-cell changes , where individual cells change their synaptic composition such that synapses with some protein variants are replaced by others . Finally , within-tissue changes , where the proportion or number of cells expressing some protein variants change . In all these cases , the information processing properties of the neural circuit are modified . Here we take a systematic , genome-wide , approach to search for developmental CDVs by analyzing the temporal gene expression profiles measured in a set of post-mortem human brains of various ages . We study two types of temporal relations among a pair of candidate CDVs: life-long expression trends , and subject-to-subject expression correlation that goes beyond the development trend . The changes captured by this approach may reflect changes in the distribution of synapses within a cell or changes in the distribution of cells in a tissue , and provides candidates for future deeper analysis . We developed a procedure to detect potential CDV pairs , and use it to analyze seventeen brain-related molecular pathways including all the major neuromodulators . The analysis revealed both known and novel CDVs in glycine receptors , where the switching proteins are different from those observed in rodents , and in inhibitory serotonin receptors , providing new evidence for a common regulatory mechanism .
We start by demonstrating that a switch between two NMDA receptor subunits , NR2B and NR2A is reproduced in the transcriptome of postmortem human brains measured with microarrays and RNAseq . In rats , NR2A and NR2B exhibit prominent switching in the cerebellum during development , both at the mRNA and protein levels [5 , 22 , 25 , 35] . Here we traced the expression levels of the human genes GRIN2A and GRIN2B , encoding for the protein variants NR2A and NR2B , as measured in 57 human brains ( see methods ) . Fig 1A and 1B shows a clear “switch” from GRIN2B to GRIN2A in the cerebellum , consistent with the switch reported in rodents . The switch is occurs around birth and does not occur in other brain regions , where GRIN2B maintains high expression level while GRIN2A rises . We now proceed to map systematically the synaptic proteins which exhibit a developmental switch from one variant to another . Clearly , considering all protein pairs is wasteful and would yield many false positives . We therefore limit the set of candidate protein pairs using prior knowledge about their functional role and sequence similarity . Specifically , we used the KEGG pathways repository [2] to focus on proteins that participate in synaptic pathways , including signal transduction pathways of all neurotransmitters ( a total of 17 KEGG pathways ) . We then used KEGG to define subgroups of functionally-related proteins which are likely to contain CDVs ( see Methods ) . Then , we calculated the similarity of the two amino-acid sequences of each candidate pair , and kept pairs with sequence similarity above 30% ( see Methods ) . Low sequence similarity is usually associated with different functionality and dissimilar protein sequences can be excluded from consideration [40] . For all protein pairs remaining as candidate CDVs , we compute two measures of correlation for every brain region: the correlation of the two expression profiles along life and the age-corrected correlation capturing subject-to-subject fluctuations . We now describe separately the top-ranked protein pairs for each of these two measures , and later discuss in detail two specific pairs which have significant correlation in both . We considered gene pairs coding for proteins in seventeen brain-related pathways . For each pair of proteins , we calculated the correlation of their expression profiles as measured in from three datasets: Kang et al . ( 2011 ) for 16 brain regions using mRNA levels measured using microarrays across 57 subjects; Colantuoni et al . ( 2011 ) for one brain region with 269 subjects; and RNA-seq data for 16 brain regions provided by Brainspan ( see Methods ) . Overall , we examined 843 unique pairs in 17 brain regions , 8 . 3% of which have a statistically significant anti-correlation ( FDR-corrected Pearson correlation q-value<0 . 01 ) . To test if this abundance of correlated pairs is significant , we repeated the calculation using other groups of gene pairs . Specifically , we computed the correlation among all paralog gene while grouping pairs by their sequence similarity , and also using random pairing of these paralog genes pairs ( see Methods ) . Fig 1C and 1D shows that the expression of paralog genes , regardless of their sequence similarity , has a similar distribution of correlations as the expression of random pairs ( from the same set of paralog genes ) . However , significantly many more pairs selected from the KEGG pathways to be functionally related exhibit strong correlations . This result suggests that many functionally-related pairs of genes in brain-related molecular pathways exhibit a development switch . To further look into the specific switches found , Fig 2 gives the negative log10 of the FDR q-values for the top 20 genes in the 17 brain regions examined , ranked by the average score across 17 regions , based on microarray measurements . A similar figure based on RNA-seq data is given in S2 Fig . The two data sources largely agree ( S3 Fig ) . The full list of all significant CDV pairs is given in a supplemental webpage at http://chechiklab . biu . ac . il/~ossnat/brain-paralogs/ . Many of the top-ranked human CDVs pairs are consistent with experiments in rodents or previous literature . For instance , our analysis reveals a significant switch of the human genes GRIN2B and GRIN2C in the cerebellum starting at the late fetal stage LF7 ( FDR-corrected Pearson correlation q-value <10−10 , see S1A and S1C Fig ) . This is consistent with previous reports of a switch of GRIN2B and GRIN2C in rodent cerebellum during early development [5 , 16] . In rodents , the switch occurs in cerebellar granule cells , after their migration from the external germinal layer to the inner granular layer [38] . As another example , in rats , in-situ hybridization studies of the cerebellar granule cells have detected a pronounced increase in the expression and contribution to postsynaptic receptors of the GABA receptor α6 subunit in the first two postnatal weeks ( between P6 and P14 ) [4 , 39 , 40] . This increase was limited to internal granule layer [4 , 39 , 40] . The expression of the α3 subunit has declined in this time period [4] . Here we found a rise in the RNA levels of GABRA6 , the gene encoding for the α-6 subunit of the GABA receptor , with a parallel decrease in RNA levels of GABRA3 , the gene encoding the α-3 subunit ( S1B Fig , q-value <10−12 ) . Other subunits may be changing as well . The above results point to numerous protein pairs whose expression profile in the population shows anti-correlated trends , as in a developmental switch . We now turn to study how the expression of these genes fluctuates around these trends . When two genes fluctuate together , as illustrated in S5 Fig , it means that their expression changes in a coordinated way from one subject to another . Such coordination could be due to a direct regulatory mechanisms such as transcription regulation , splicing and RNA editing , or through indirect control mechanisms involving other systems and genes and operating at a slow timescale . Importantly , the two types of correlations we analyze , namely correlation of expression trends and correlation of fluctuations around the trend , are independent in the sense that the presence of trend correlation does not necessarily lead to subject-to-subject correlations and vice versa . The two types of correlations could reflect different underlying mechanisms . Computing the residual correlations of non-stationary signals has been previously studied in various domains [41] . Commonly , the trend is modelled in a parametric or non-parametric way , which allows subtracting the underlying population trend . Often , trends are assumed to be linear , and the method of partial correlations is used to compute the trend-corrected fluctuations ( see Methods for details ) . The expression trends in our data are far from linear , and we therefore take a non-linear detrending approach . To estimate age-corrected correlations in development expression data while correcting for the possible effect of age , we estimated the underlying population trend by fitting a model to the temporal profile of each gene , based on the expression in the population . Specifically , after testing several models , we used here a cubic spline model ( See Methods ) . Fig 3 illustrates how correcting for age reveals significant correlations among gene pairs . First , Fig 3A depicts an example of a spline fit to HTR1A and HTR5A . Fig 3B and 3C describe age-corrected correlation between the expression levels of the two genes before and after correcting for trend due to age . While the correlation before correcting for the trend is negative ( ρ = -0 . 58 , FDR corrected Pearson correlation q-value = 2 . 5·10−5 ) , the age-corrected correlation is strongly positive ( ρ = 0 . 53 , q-value = 2 . 2·10−4 ) . The positive age-corrected correlation is therefore masked by the negative correlation induced by the trend . One model that is consistent with this finding is that the two genes share a common regulation mechanism that affects the mRNA levels of both genes in a similar way . We repeated computing age-corrected correlations for all candidate gene pairs . Fig 3D provides a heat map with the magnitude of age-corrected correlation , measured using log10 ( q-value ) , for the top 20 correlated pairs and also for additional pairs that are discussed in detail below . Estimating age-corrected correlations can be sensitive to various biases . First , variability in RNA quality or density across subjects could yield spurious correlations that are not limited to the set of genes studied . To estimate the magnitude of this effect , we computed age-corrected correlations in a set of house-keeping genes ( HKG ) [42] , and found that strongly correlated HKG pairs are significantly less common than observed in pathway genes ( S6 Fig ) . This suggests that the correlations we observe are significantly more abundant in the pathway genes we studied than in random gene pairs . Correlations could also stem from fluctuations across subjects in the fraction of neurons sampled . Our estimates show that such variability is not likely to explain the strong correlation effects that we observe ( Supplemental results ) . Together , the two correlation measures discussed above , anti-correlation along life and age-corrected correlation , yield a large number of gene pairs with trends that are anti-correlated , and a significant age-corrected correlation ( see full list in supplemental webpage ) . We now discuss in detail two such examples: glycine receptors and serotonin receptors . Glycine is a major inhibitory neurotransmitter in the central nervous system , operating by causing an influx of chloride which hyperpolarizes a cell when glycine receptors are activated . Disruption in glycine receptor expression or ion channel function can result in hyperekplexia , a rare neurological disorder . Glycine receptors ( GlyRs ) could include two subunits: α , which has four variants α1 , α2 , α3 , α4 , and β , which has a single variant , and they appear as pentameric α homomers or as αβ heteromers[43] . The β-subunit binds gephryn [44 , 45] , a protein that anchors the glycine receptor to the cytoskeleton ( Fig 4A ) , and allows clustering of heteromeric glycine receptors at the synapse [46–48] . Heteromeric GlyR likely accounts for most of the GlyRs in the adult CNS [43] . Homomeric α GlyR still form functional GlyRs [49] , and is found in embryonic neurons [50] , but since they do not bind gephryn , homomeric α1-α3 GlyRs are most likely to be extrasynaptic [43] . In humans , the gene encoding for α4 is a pseudogene and is not expressed [51] , but all other subunits were found to be expressed in several brain regions including the cerebral cortex [52] , the striatum [53] and the amygdala [54] . In the rat , GlyRs have been shown to switch from homomeric α2 receptors to heteromeric α1β receptors by postnatal day 20 [13] . As a result most glycinergic neurotransmission in the adult rat brain is mediated by α1β receptors [43] , composed of three α1 and two β subunits [47 , 55] . Here we find that human GlyRs follow a different pattern , switching from GLRA2 to GLRA3 . This switch is significant across the cortex ( q-value <0 . 01 , Figs 2 and 4B , Supplemental webpage ) , but not in the cerebellum , where the expression of both subunits decreases with age ( Fig 4C ) , or the thalamus . Since synapses containing homomeric α3 GlyRs are weakly expressed in the mature vertebrate brain [43] , it is possible that the switch we detected in the expression levels of GLRA2 and GLRA3 reflects a shift from homomeric α2 GlyRs to heteromeric α3β GlyRs , similar to the switch reported from α2 to α1β in rats . This hypothesis is supported by the expression profile of GLRB , which is highly correlated ( p-value <0 . 01 ) with the profile of GLRA3 in all brain structures except the cerebellum and the striatum ( for example , the correlation coefficients in the IPC , ρ = 0 . 70 , p-value < 10−8 and in the amygdala ρ = 0 . 81 , p-value <10−11 , Pearson ) . Furthermore , GLRA2 and GLRA3 have strong positively age-corrected correlation in the hippocampus ( q-value < 10−7 , Fig 4D ) , and other regions ( mediodorsal nucleus of the thalamus , q-value = 9 . 05·10−4; amygdala , q-value = 0 . 0062 ) . This suggests that GLRA2 and GLRA3 might be commonly regulated ( directly or indirectly ) in the postnatal human brain . A second example of a novel switch is the pair of serotonin-receptor genes HTR1A and HTR5A . Serotonin is a major neuromodulator involved in regulation of mood [56–58] , aggressive behavior [59] , and sleep–wake cycle [60 , 61] . Importantly , serotonin is involved in major mood pathologies like depression and aggressive behavior , and selective serotonin reuptake inhibitors ( SSRIs ) are today the most widely used anti-depressants . There are seven families of serotonin receptors , each containing multiple subunits . We focus here on two specific proteins 5-HT1A and 5-HT5A which are both compounds of Gαi-coupled inhibitory receptors 5-HT1 and 5-HT5 . The two receptors 5-HT1 and 5-HT5 share many properties [62] . 5-HT1 acts as an autoreceptor on serotonergic neurons [63 , 64] , and mediates hyperpolarization of 5-HT on prefrontal neurons [65] . 5-HT5 receptors activate the same signal transduction pathways as 5-HT1 through G-αi coupled protein [62] . 5-HT5 receptors were found to act as autoreceptors [66] and induce inhibitory influence on neuronal excitability [67] . The areal expression pattern of both 5-HT1A and 5-HT5A is similar , as they are both expressed in the amygdala , the cerebellum , the hippocampus , and in cortical layers III and V . Both receptors are highly expressed in pyramidal neurons in the cerebral cortex and in the hippocampus [68 , 69] . We find that in the prefrontal cortex the expression of HTR1A significantly decreases during early embryonic development while the expression of HTR5A increases ( Fig 5B ) . This is also observed in a second human postmortem dataset collected in the prefrontal cortex [33] ( Fig 5C ) . This switch is significant in the primary visual cortex ( FDR q-value<10-4 ) , and in the prefrontal cortex ( q-value<10-5 ) . These findings complement a recent study by Lambe and colleagues , who analyzed serotonin expression from 59 human subjects aged 6 weeks to 50 years [70] . They reported a developmental increase of HTR5A expression in the human prefrontal cortex , paralleled by a stable expression of HTR1A during post-natal development [70] . The analysis here shows that these findings continue a trend that started during embryonic development . When correcting for the population trend across life , HTR1A and HTR5A are significantly positively correlated in seven out of sixteen brain regions , including the hippocampus ( ρ = 0 . 725 , q-value <10-7 , Fig 5D ) , the primary visual cortex ( ρ = 0 . 53 , q-value <10-3 , Fig 3C ) and other cortical areas ( see Supplemental webpage ) . At the same time , they are slightly negatively correlated in the cerebellum ( ρ = -0 . 3 , q-value = 0 . 05 ) , striatum ( ρ = -0 . 048 ) , and prefrontal cortex ( ρ = -0 . 1 ) . This suggests that the two genes might be commonly regulated , and that this regulation may depend on brain region . As discussed above , the developmental switch of the NR2 subunit of the NMDA receptors from NR2B to NR2A has gained significant interest , since it affects the functional properties of the NMDA synapse , possibly reducing its plasticity . In rodents , both NR2B and NR2A represent an important fraction of juvenile and adult NMDARs [14] . We therefore turned to look into the details of the NR2A/2B switch in three brain regions: Cortex , Hippocampus and Cerebellum . Fig 6 shows the expression profiles of GRIN2A and GRIN2B as measured using RNA-seq ( top row ) and microarrays ( bottom row , [32] ) for three brain regions: The dorsolateral prefrontal cortex ( DFC ) , hippocampus ( HIP ) and the cerebellum ( CBC ) . In the cortex ( Fig 6A and 6D ) and hippocampus ( Fig 6B and 6E ) , GRIN2B levels appear to remain at the same level throughout life , while GRIN2A levels rise during embryonic development and remain steady ( microarrays ) or rise slightly ( RNA-seq ) after birth . In the cerebellum ( Fig 6C and 6F ) , GRIN2B levels slowly decline throughout life , and GRIN2A levels rise abruptly around birth , such that postnatal levels are higher than prenatal ones . It should be noted that the RNA transcript counts may not reflect protein levels directly , since different genes may have different degradation and translation rates , such that the number of protein molecules per RNA may vary across genes . Interestingly , it appears that in humans , the time of the developmental switch is early , and does not extend into childhood or adolescence as observed in rodents [14 , 24] where changes of expression patterns occur within the first two postnatal weeks [22] . This could suggest that in humans , the switch from NR2B to NR2A is not related to the decrease in behavioral plasticity observed during late childhood and puberty . This topic awaits further experiments and analysis which is beyond the scope of the current paper .
Developmental switching of protein variants that function as subunits of synaptic receptors is a mechanism that allows the brain to tune functional properties of synapses [71 , 72] and underlies meta-plasticity [29–31] . Here we described a systematic way to find context dependent variants ( CDVs ) that switch during development . The procedure we propose detects pairs of substitutable proteins , based on their structure and function similarity and the dissimilarity of their abundance profile . We used this method to detect candidate pairs of CDVs in elements of seventeen brain-related pathways . We investigated more deeply two pairs of candidate CDVs . First , we found that human glycine receptors switch from α2 to α3 subunits . This switch differs from previous results in rodents , and raises the hypothesis that glycine receptors in the adult human brain contain α3β heteromers . Second we find a switch of serotonin receptors 5-HT1A to 5-HT5A in the cerebellum and the hippocampus . These two serotonin receptor proteins co-vary across subjects exhibiting high age-corrected correlations and suggesting that they may be directly or indirectly regulated . Our results are based on a coarse measure of average expression levels in a region . As such , they are limited in several ways . First , mRNA levels may not reflect protein levels in these brain areas , hence these results should be viewed as providing candidates for future proteomic measurements . Importantly however , since the trend correlations that we observe are based on changes of expression level within a gene through life , the results are invariant to any linear transformation of the expression measurements . Second , multiple variants may be switching concurrently , and the analysis can be extended beyond pairs of genes . Third , a switch in aggregated mRNA level measured as an average in a brain tissue could result from changes at various levels . It could reflect a process by which cells start expressing a different mix of subunits in their receptors , or express a different mix of receptors . It is also possible that the mixture of cells in the tissue changes , such that cells expressing one receptor become more abundant . In the context of the above findings , such changes in cell composition are particularly likely in early developmental stages in the cerebellum , where neural migration and wiring matures later than in other areas . Regardless of the level where the developmental switch takes place , it could affect the information processing and plasticity properties of the network . The analysis in this paper focused on switches at the gene level . Recent RNA sequencing measurements now allow extending it to exon level , which could detect context-dependent splice variants . Indeed , there is evidence that switching between splicing alternatives is a key event in neuron differentiation [73] , and a systematic study of developmental changes in splicing in human and mammalian brain would be of great interest . The vast majority of switches that our analysis detected occurred around late embryonic development , and many have stretched into infancy and childhood . This is consistent with the massive changes in expression levels that many genes exhibit around birth [32 , 33] . This can have important implications regarding the stability of early learned experience , since it suggests that many synaptic connections get replaced around birth , possibly affecting the capacity of the network to retain early experiences . Quantifying the shapes of the transition curves and the subject-to-subject interactions among subunits of the receptors can shed further light on how subunits are utilized at various developmental stages . This can benefit from using parametric models of curve shapes to capture timing information [74] . The two pairs of switching CDVs we discussed , serotonin and glycine receptors , are characterized by a strong anti-correlated population trend , together with age-corrected correlation which was positive in many regions . This demonstrates that the regulation of these pairs involves various control mechanisms operating on multiple scales . The precise nature of the regulation mechanisms operating on these pairs awaits further study .
We analyzed three gene expression datasets . The first dataset was collected by Kang et al . using microarrays from the brains of 57 human donors [32] . The dataset contained transcriptome of 17 , 565 mainly protein-coding genes collected from 11 cortical and 5 sub-cortical brain regions . We included all subjects older than 10 post conceptual week ( PCW ) , leaving a total of 53 subjects aged 10PCW to 82 years . The data were originally quantile-normalized and log2-transformed . When presenting results , we followed the classification of subjects into 13 age groups made in [32] and specified in Table 1 . Classification into age groups was not used in the analysis . The second dataset , collected by Colantuoni et al . [33] , contains mRNA microarray measurements of 30 , 176 genes in the prefrontal cortex of 269 human subjects . Donor ages range from 18PCW to 78 years . When computing correlation p-values this data was sub-sampled uniformly to bring all results to a common scale . The third dataset , collected by the Brainspan consortium and described in [34] , contains RNA sequencing measurements collected from the same set of brain tissues as [32] . We downloaded the version available on the Brainspan website on November 2014 ( Gencode v10 summarized to genes ) . In this work we considered all pathways in the KEGG pathway collection [1] that are classified under Nervous system , Substance dependence , and Neurodegenerative diseases . We also examined the pathway Neuroactive ligand-receptor interaction , which contains G-protein-coupled receptors and ion channels . Overall we analyzed seventeen pathways listed in Table 2 , all retrieved from the KEGG pathway repository ( www . genome . jp/kegg/pathway . html ) on September 2012 . To reduce the fraction of false positives , we limited candidate protein pairs to proteins that reside within the same functional element in KEGG pathway repository [2] . These KEGG elements group together proteins with common functionally and interaction partners . Often , but not necessarily , these proteins belong to the same family . We also tested filtering candidate proteins based on protein families , but found that KEGG elements were usually more functionally-coherent than protein families , and at the same time less specific than protein sub-families . In many cases , members of the same family that are functionally distinct are split into separate pathway elements . For example , the family of Glutamate-gated ion channels ( UniProtKB ) includes all the subunits of NMDA , AMPA and Kainate receptors . In KEGG’s Glutamenergic synapse pathway , this family is split into three elements: KAR , AMPAR and NMDAR—one per receptor type . In some cases , proteins in a KEGG functional element do not share similar functions . For instance , the Gi/o element contains 22 guanine-nucleotide-binding proteins , some of which belong to the G-alpha family ( GNAI1-3 , GNAO1 ) , and others to the WD-repeat-G-beta family ( GNB1-5 ) . Some function mainly as inhibitors ( GNAI ) while others are mainly engaged in activation ( GNAO ) . Also , some elements contain a protein subunit that appears in every receptor , such as the NR1 subunit in the NMDA receptor , and these should not be considered as CDVs . To reduce detection of unrelated proteins as CDVs , we excluded protein pairs with amino-acid sequence similarity lower than 30% , since they are unlikely to share a similar function [36 , 37 , 75–77] . Protein sequences were aligned using the Needleman-Wunsch global algorithm . Following the definition used for global alignment in BLAST 2 Sequences [78] we used blosum62 as the scoring matrix , and set the gap costs to 11 for gap existence and 1 for each extension . The sequence similarity was defined as the fraction of identical residues out of the number of residues in the longer sequence . We analyzed all pairs of human genes pairs defined in Biomart on January 15th 2015 as paralogs and having entrez ids , yielding a total of 74984 pairs . We grouped these pairs by their sequence overlap as computed by Biomart , and computed the distribution of anti-correlation scores for each of the groups . We also computed the distribution of correlation p-values for a baseline set of 100K pairs , drawn uniformly at random from all gene measurements in the data that have an entrez id . Fig 1C and 1D depicts the distribution of pairs as a function of their correlation p-values for microarrays data ( Fig 1C ) and Brainspan RNA-seq data ( Fig 1D ) . To find genes that show opposite trend in their expression profile , we measured the Spearman correlation between the expression profiles of each pair of genes in every brain region . Analysis was limited to differentially expressed genes , by excluding genes whose expression range ( maximum–minimum ) was lower than 1 . 5 in log2 scale . We verified visually that this threshold excluded genes whose expression did not change considerably over time . The measure of correlation between two signals xt , yt , formally ( xt−x¯ ) ( yt−y¯ ) , captures how the two signals jointly fluctuate around their corresponding means x¯=1n ( x ) ∑xt and y¯=1n ( y ) ∑yt . However , when the signals are non-stationary , the mean of samples taken from a local period is itself changing within time . A natural idea is therefore to ‘detrend’ the data: learn models of the trend x¯ ( t ) , y¯ ( t ) and use them to compute the fluctuations around the trend Correlation_arround_the_trend= ( xt−x¯ ( t ) ) ( yt−y¯ ( t ) ) . Importantly , this detrended correlation measure is very different from the correlation of the trends Trend_Correlation= ( x¯ ( t ) −x¯ ) ( y¯ ( t ) −y¯ ) . This is illustrated in S5 Fig , where the trend correlation is negative–illustrated by the two solid lines , while the correlation around the trend is positive–illustrated by the arrows capturing the fluctuations around the lines . While the derivation above used Pearson ( linear ) correlations between the signals , any measure of correlation ( like Spearman ) can be applied to the trended data . In the context of the current paper the correlation of the trend capture the changes in expression through life at the population , and the correlations around the trend capture subject-to-subject fluctuations . When the trend is modeled as a linear function , as in S5 Fig , the resulting correlation around the trend is known as partial correlations . Other , non-linear models have also been proposed . For instance , Podobnik and Stanley [41] have analyzed a piece-wise linear model , where time is divided to non-overlapping boxes , and each period is used to model the local trend . The current paper takes a more general approach to modelling the trend , using smoothing splines . Specifically , to correct for age-dependent trends , we first learned a model of the expression profiles , by fitting a cubic smoothing spline to the expression profile of each gene separately as in [79] . The number of knots was set to the minimal number for which the spline satisfied the condition 1n∑i=1n ( yi−f ( xi ) ) 2<σ^2 , where σ^2 is the estimated variance of the residuals computed by taking the mean of the variances for a sliding window of 10 data points . In a small minority of cases this condition could not be satisfied . In those cases , we relaxed the bound by using 2σ^ . Fig 3A illustrates the spline fitting for two Serotonin genes . We used a leave-one-sample-out procedure to estimate the goodness of fit . Specifically , for each data point i , we computed the squared error for the predicted value y^i computed by fitting the model with all points except point i . The goodness of fit was then computed as R2=1−∑i=1n ( yi−y^i ) 2∑i=1n ( yi−y¯ ) 2 , where y¯=1n∑i=1nyi . R2 measures by how much we reduce the variance compared to a baseline model of y^i=y¯ . It is bounded by 1 and is usually positive , since the spline can assume the form y^i=y¯ as a special case . A score around zero usually means that the data show no trend and some fits have a negative score due to overfitting , since we are evaluating the fit using held out data . S2 Fig shows the distribution of R2 values attained by the model compared to the distribution over the same data after shuffled in time . The spline models were on average superior to regularized polynomial regression using polynomials of degrees 1 to 3 .
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Synapses change their properties during development affecting information processing and learning . Most synaptic receptors consist of several proteins , each having several variants coded by closely related genes . These protein variants are similar in structure , yet often differ slightly in their biophysical attributes . Switching a synapse from using one variant to another provides the brain with a way to fine-tune electrophysiological properties of synapses and has been described in NMDA and GABA receptors . Here we describe a systematic approach to detect pairs of context-dependent variants at a genome-wide scale based on a set of post-mortem expression measurements taken from brains at multiple ages . We take into account both the profile of expression as it changes along life and also the detrended age-corrected correlation among genes . This method characterizes the landscape of developmental switches in brain transcriptome , putting forward new candidates pairs for deeper analysis . The abundance of switching between context-dependent variants through life suggests that it is a major mechanism by which the brain tunes its plasticity and information processing .
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[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[] |
2015
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Gene Expression Switching of Receptor Subunits in Human Brain Development
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The human monoclonal antibody ( mAb ) HK20 neutralizes a broad spectrum of primary HIV-1 isolates by targeting the highly conserved heptad repeat 1 ( HR1 ) of gp41 , which is transiently exposed during HIV-1 entry . Here we present the crystal structure of the HK20 Fab in complex with a gp41 mimetic 5-Helix at 2 . 3 Å resolution . HK20 employs its heavy chain CDR H2 and H3 loops to bind into a conserved hydrophobic HR1 pocket that is occupied by HR2 residues in the gp41 post fusion conformation . Compared to the previously described HR1-specific mAb D5 , HK20 approaches its epitope with a different angle which might favor epitope access and thus contribute to its higher neutralization breadth and potency . Comparison of the neutralization activities of HK20 IgG , Fab and scFv employing both single cycle and multiple cycle neutralization assays revealed much higher potencies for the smaller Fab and scFv over IgG , implying that the target site is difficult to access for complete antibodies . Nevertheless , two thirds of sera from HIV-1 infected individuals contain significant titers of HK20-inhibiting antibodies . The breadth of neutralization of primary isolates across all clades , the higher potencies for C-clade viruses and the targeting of a distinct site as compared to the fusion inhibitor T-20 demonstrate the potential of HK20 scFv as a therapeutic tool .
The HIV-1 envelope ( Env ) glycoprotein is the main target for neutralizing antibodies . Thus a successful HIV-1 vaccine must induce broadly cross-clade neutralizing antibodies as an essential correlate of protection against infection [1] . The HIV-1 genome and especially its env gene is highly variable between and within clades [2] , which is partly responsible for the difficulty in developing a suitable vaccine candidate [3] , [4] . Consequently , the search for conserved targets is the basis of current attempts to develop an effective HIV-1 vaccine . Trimeric Env is composed of the receptor binding domain gp120 , which is non-covalently associated with the membrane-anchored fusion protein gp41 . Infection of target cells is initiated by the attachment of Env to the CD4 receptor [5] , [6] , which triggers conformational changes that expose the hypervariable loop 3 ( V3 ) [7] , thus priming it for co-receptor CCR5 or CXCR4 interaction [8] , [9] . Together CD4 and co-receptor interactions are thought to induce conformational changes in the fusion protein subunit resulting in exposure and subsequent insertion of the fusion peptide into the target cell membrane which produce the fusion intermediate pre-hairpin structure that bridges viral and cellular membranes [10] , [11] . During this process heptad repeat regions 1 ( HR1 ) and 2 ( HR2 ) are transiently exposed [12] permitting interaction with peptide inhibitors of fusion such as T-20 [13] , [14] . Subsequent refolding of the pre-hairpin structure into the post-fusion conformation [15] , [16] , [17] , [18] leads to the apposition of viral and cellular membranes catalyzing membrane fusion [19] . The fusion-intermediate conformation of gp41 is an attractive target for neutralizing antibodies due to its relative high sequence conservation . Broadly cross-clade neutralizing antibodies 2F5 , 4E10 and Z13 target the membrane proximal region most likely during epitope exposure in the fusion-intermediate pre-hairpin conformation [20] , [21] , [22] . A number of monoclonal antibodies directed against HR1 exposed in the pre-hairpin conformation of gp41 have been isolated from phage display libraries , which show variable neutralization profiles depending on the neutralization assays used . MAb D5 was isolated from a naïve human library [23] and MAb DN9 from a Fab library generated from bone marrow RNA from an HIV-1 infected individual [24] , while the rabbit single chain mAb 8K8 was derived from a phage library [24] prepared from rabbits immunized with a gp41 HR1 mimetic [25] . Several HR1-specific Fabs were also isolated from a human non-immune phage library [26] , [27] and Fab 3674 was in vitro matured [28] . Notably , immunization strategies employing HR1 peptide mimetics led to the generation of a polyclonal antibody response capable of neutralizing Tier 1 primary isolates [29] . The crystal structure of the D5 Fab in complex with the gp41 mimetic 5-Helix [30] reveals that D5 binds orthogonal to the axis of the HR1 trimer . The principal interaction site is a conserved hydrophobic pocket on gp41 [31] that is the target for HR2 [15] , [16] , D-peptides and various peptide mimetics [25] , [32] , [33] , demonstrating that D5 binding blocks the transition into the six-helix conformation required for entry [31] . We previously reported the isolation of the gp41-specific antibody HK20 from immortalized memory B cells of an HIV-1 infected individual , which targets the conserved hydrophobic pocket in gp41 HR1 [34] . HK20 has a considerable breadth of neutralization on isolates from multiple clades . However the neutralizing potency was low and target cell dependent [34] . Here we present the crystal structure of the HK20 Fab in complex with gp41 5-Helix as well as a detailed characterization of HK20 neutralizing properties . The structure reveals that HK20 occupies a conserved hydrophobic pocket within the HR1 triple stranded coiled coil , indicating that HK20 binding inhibits membrane fusion by interfering with HR2 refolding into the six helix bundle post fusion conformation . Although HK20 and D5 occupy the same site on HR1 , their angle of interaction vary substantially , which might account for the differences in neutralization potencies . Comparison of the neutralization activities in different assays reveals a much higher potency for smaller HK20 Fab and scFv over IgG , suggesting steric as well as temporal constraints . HK20 scFv neutralize all pseudoviruses tested as well as infectious viruses by targeting a conserved site that is not affected by T-20 resistance mutations .
The crystal structure of HK20-5-Helix complex was solved by molecular replacement and refined to a resolution of 2 . 3 Å ( Table 1 ) . The variable domains ( VH and VL ) of the Fab approach the epitope in an ∼60° angle with respect to the 5-Helix trimer axis ( Figure 1A ) . HK20 employs the complementarity determining regions ( CDR ) H2 , H3 and L3 to contact two adjacent HR1 helices ( Figure 1B ) . The tip of CDR H2 is a central determinant of interaction ( Figure 2A ) and positions Ile53 and Phe54 into a hydrophobic HR1 pocket ( lined by HR1 chain a Leu565 , Leu568 , Thr569 and HR1 chain c Val570* and Ile573* ) ( Figure 2B ) . Furthermore CDR H2 Asp55 makes a water mediated contact to the carbonyl of Lys574 followed by a hydrophobic contact of Ile56 . CDR H3 contacts HR1 chain a . The aromatic ring of Tyr97 is within π stacking distance to His564 and its orientation is supported by a hydrogen bond to the carbonyl of CDR H1 Arg31 . The carbonyl of CDR H3 Ser99 hydrogen bonds to Trp571 NE1 , whose aromatic ring forms a hydrophobic sandwich with the ring structure of Pro100b , itself stacked by Tyr100c ( Figure 2C ) . Tyr100c also participates in a network of hydrogen bonds by contacting Gln575 , which makes a double dent hydrogen bond to CDR H2 Asn58 ( Figure 2D ) . The binding contribution of the light chain is minor and restricted to hydrophobic contacts by Asp93 and Leu94 to Ala578 and Ala582 of HR1 ( Figure 2D ) . A water molecule coordinated by CDR H3 Ser98 and the carbonyl of Tyr97 contacts HR2 His 643 . Docking demonstrates that three Fabs or antibodies could bind simultaneously to all three epitopes once they become exposed during the fusion reaction ( Figure 3 ) . The crystal structure reveals that the tip of HK20 CDR H2 occupies the hydrophobic pocket on HR1 that , in the post-fusion conformation , is filled by HR2 residues Trp626 , Trp631 and Ile635 ( Figure 4A ) [15] , [16] . In addition , the side chain of Trp571 rotates by ∼90° to accommodate binding to CDR H3 ( Figures 4A , B and 2C ) . Thus the HK20 interaction blocks entry by preventing the folding of HR2 onto HR1 which is required to catalyze fusion of viral and cellular membranes in order to establish infection [35] . Fab D5 , previously isolated from a human naive phage display library , uses similar structural principles for interaction with HR1 [31] . Its CDR H2 tip reaches into the HR1 pocket employing Ile53 and Phe54 for interaction ( Figure 4C ) with Phe54 occupying the identical position of HK20 CDR H2 Phe54 ( Figure 4B ) . In contrast , D5 residue Gly55 replaces the important HK20 CDR H2 residue Asp55 . D5 binding is supported by a shorter CDR H3 compared to HK20 ( Figure S1 in Supporting Information S1 ) , whereas D5 CDR H3 Pro97 is sandwiched between HR1 chain A residues His564 and Trp571 ( Figure 4C ) . Other D5 contacts are provided by CDR L3 and by two interactions with HR2 residues , including a water mediated contact to HR2 His643 [31] as observed in case of HK20 interaction . Although HK20 and D5 VH domains are encoded by very similar germline genes , namely VH1-69*5 and VH1-69*1 , HK20 shows four amino acid somatic mutations in CDR H2 , while D5 uses the germline sequence of CDR H2 for interaction ( Figure S1 in Supporting Information S1 ) . The second important contribution of interaction comes from the CDR H3 region , which is completely different in both antibodies since it is encoded by D6-6*01 and J3*02 gene segments ( HK20 ) and D1-14*1 and J4*02 gene segments ( D5 ) . Both antibodies employ different light chain gene segments ( HK20 VL , V33*1 and J4*02; D5 VL , V5*01 and J4*1 ) . In spite of structural differences described above , both mAbs show a similar interaction footprint on the 5-Helix structure , with HK20 and D5 occupying a surface of 1061 Å2 and 1156 Å2 , respectively ( Figures 5A , B ) . Notably , Cα super positioning of both complexes reveals that HK20 approaches its epitope on HR1 in a different angle than D5 . While D5 binds almost perpendicular to the 5-Helix trimer axis [31] , HK20 approaches the epitope in a ∼60° angle between the 5-Helix trimer axis and the HK20 major axis ( Figure 6A ) . This difference in interaction is more visible when the Cα atoms of the Fabs are super positioned , which reveals the dramatic change in HR1 trimer axis orientation between the two complexes ( Figure 6B ) . The close up of the Cα super positioning highlights the distinct and common features of interaction of CDR H3 ( Figure 6C ) and H2 ( Figure 6D ) indicating that the same epitope can be targeted by different approach angles . Surface plasmon resonance measurements showed an affinity constant ( KD ) of 3 . 3 nM for HK20 and 3 . 1 nM for D5 IgG binding to a gp41 HR1 mimetic . Both HK20 and D5 show similar association rates of ka = 2 . 20±0 . 015 105 M−1 s−1 and ka = 3 . 35±0 . 022 105 M−1 s−1 and similar dissociation rates of kd = 7 . 27±1 . 05 10−4 s−1 and kd = 10 . 3±0 . 089 10−4 s−1 , respectively , suggesting that the minor difference in affinity does not explain the differences in neutralization as outlined below . The contribution of somatic mutations was investigated by comparing , employing a 5-Helix-based ELISA , HK20 , D5 and HK20 variants in which the VH and/or VL were reverted to the germline configuration . HK20 and D5 showed comparable EC50 values ( 0 . 1 µg/ml ) , while the HK20 variants in which the heavy chain alone or both heavy and light chains were reverted to germline showed approximately 50-fold lower binding ( EC50 6 . 2 and 4 . 8 µg/ml ) . In contrast the germline reversion of VL alone had no measurable effect on binding using the ELISA assay ( Figure S2A in Supporting Information S1 ) . These binding results were also confirmed by the lack of neutralizing activity by the germline HK20 version or by the HK20 variant in which the light chain was reverted to germline when tested in parallel with HK20 in the HOS . CD4-R5 target cell assay against clade A and C HIV-1 isolates ( Figure S2B and C in Supporting Information S1 ) . These results are consistent with structural data described above and demonstrate that somatic mutations in the light chain do not contribute significantly to binding , while those in the heavy chain represent the positive contribution of somatic hyper-mutations to the affinity maturation of mAb HK20 . HK20 was an affinity selected antibody isolated from the memory B cell repertoire of an HIV-1 infected individual . To establish whether similar antibodies would be generally produced in the context of the immune response to HIV-1 infection we developed a competitive assay suitable for the detection and quantification of HK20-like antibodies in patients' sera . Serial dilutions were tested for their capacity to inhibit binding of biotinylated HK20 to immobilized 5-Helix . Twenty out of 33 sera from HIV-1 infected individuals showed significant titers of HK20-inhibiting antibodies , while the remaining did not show significant inhibition , in spite of variable binding to 5-Helix ( Figure 7A and B ) . We conclude that the HK20-footprint is targeted by antibodies in a significant fraction of HIV-infected patients . HK20 and D5 IgG were compared for their capacity to neutralize a panel of 18 HIV-1 Tier-1 and Tier-2 isolates spanning 6 clades . The assays were performed using either TZM-b1 or HOS . CD4-R5 as target cells . In the TZM-b1-based assay HK20 neutralized 4 out of 18 HIV-1 isolates with IC50 values below 300 µg/ml ( <2000 nM ) , while D5 neutralized only one Tier 1 virus ( Figure 8 and Table S1 in Supporting Information S1 ) . In contrast in the HOS-based assay , HK20 neutralized all 18 isolates tested with IC50 values ranging from 7 to 1173 nM , while D5 neutralized only 11 out of 18 isolates with IC50 values ranging from 126 to 1930 nM ( Figure 8 and Table S1 in Supporting Information S1 ) . Thus , in spite of a similar molecular interaction with the target epitope , HK20 shows a higher potency and breadth of neutralization as compared to D5 . The HR1 region recognized by HK20 is only transiently exposed and has limited accessibility during the membrane fusion reaction [36] [37] . Our previous finding that HK20 Fab showed higher potency than IgG in an HOS-based assay [34] would be consistent with steric constraint in the accessibility of this epitope . We therefore tested whether further reducing the size of HK20 to single chain Fv ( scFv ) would increase neutralizing activity in the most demanding TZM-bl assay . HK20 IgG , Fab and scFv were compared in a TZM-b1-based assay against a panel of 45 Tier 1 , 2 and 3 HIV-1 pseudoviruses ( Figure 9A and Table S2 in Supporting Information S1 ) . The HK20 Fab showed high breadth and potency , since it neutralized 43 out of the 45 viruses with IC50 values ranging from 14 to 1000 nM ( Figure 9A and Table S2 in Supporting Information S1 ) . HK20 scFv showed on average a 2–6 times higher potency as compared to the Fab and neutralized all 45 pseudoviruses with IC50 values ranging from 6 to 737 nM ( Figure 9A and Table S2 in Supporting Information S1 ) . Interestingly , HK20 scFv neutralized clade C isolates more potently than clade B viruses . This observation was further supported by testing HK20 scFv against a larger panel of clade B and C isolates ( 27 and 25 isolates , respectively , p = 0 . 0023 ) ( Figure 9B and Table S3 in Supporting Information S1 ) . We then compared HK20 scFv with the T-20 peptide ( Fuzeon ) in both HOS and TZM-bl based assays using a panel of 20 isolates ( Figure 9C and Table S4 in Supporting Information S1 ) . HK20 scFv neutralized the same HIV-1 isolates with IC50 values 10–100 fold lower in HOS cells as compared to TZM-b1 cells ( IC50 values ranging from 0 . 8 to 174 nM and 43 to 430 nM , respectively ) . T-20 neutralization was also more efficient in the HOS cells as compared to TZM-bl cells , with IC50 values ranging from <2 . 23 to 20 nM and 0 . 04 to 4 . 4 nM in TZM-bl and HOS assays , respectively . The breadth of HIV-1 neutralization by HK20 scFv is very high considering the high number of isolates tested and compares favorably with the reference broadly neutralizing mAbs b12 ( 27 out of 45 ) , 2G12 ( 12 out of 45 ) , 2F5 ( 22 out of 45 ) , 4E10 ( 44 out of 45 ) and 447-52D ( 6 out of 45 ) ( Table S5 in Supporting Information S1 ) . Given the limitations inherent to pseudoviruses and cell lines we compared HK20-scFv , T-20 and TriMab ( a cocktail of the broadly neutralizing mAbs 2F5 , 2G12 and b12 ) in a PBMC-based neutralization assays using nine primary replication-competent HIV-1 isolates representative of clades A , B , C , D and E ( Figure 9D and Table S6 in Supporting Information S1 ) . HK20 scFv neutralized 8 out of the 9 isolates tested with IC90 values ranging from 95 to 1667 nM . Interestingly , the two HIV-1 isolates neutralized more potently ( i . e . Du174 and 92BR025 ) were clade C viruses . In conclusion these results demonstrate that reduction of size dramatically increased HK20 scFv breadth and potency of viral neutralization in different assays , including PBMC-based assays .
In this study we present the crystal structure of the HR1-specific human mAb HK20 in complex with 5-Helix . HK20 binds to the same region recognized by the previously described mAb D5 [31] , but differs significantly in the contact sites , in the angle of approach and shows a role for somatic mutations in affinity maturation . These aspects influence both potency and breadth of neutralization , which are higher for HK20 compared to D5 and depend on somatically mutated residues . In addition , we show that in case of HK20 the scFv is at least 15-fold more potent in neutralization than IgG , consistent with a limited accessibility to the target site . The gp41 footprints of HK20 and D5 and the global structural principles employed by both antibodies are similar . CDR H2 and H3 loops contribute the main interactions in both cases whereas Phe 54 at the tip of CDR H2 points into a hydrophobic pocket that is occupied by gp41 HR2 residues in the post fusion conformation [15] , [16] , [31] . Notably , the same germline V gene family encodes the VH regions of HK20 and D5 . While CDR2 H2 from D5 did not carry somatic mutations , HK20 CDR H2 is mutated and contains four amino acid changes which are required for high affinity binding . CDR H3 from HK20 and D5 follow distinct structural principles to contact the HR1 epitope , consistent with their different D and J gene usage . While HK20 CDR H2 contacts two HR1 helices , its CDR H3 contacts only one HR1 helix . In contrast , CDR H3 from D5 contacts two HR1 helices . As far as light chains are concerned , D5 uses all three light chain CDRs to interact with HR1 and HR2 and D5 CDR L3 contacts are important since mutation of CDR L3 Y94A leads to the loss of neutralization [31] . In contrast HK20 does not bind directly to HR2 and HK20 CDR L3 contributes only two hydrophobic contacts ( Leu 94 and Asp 93 ) . The minor role of the HK20 light chain is further underlined by our data showing that the light chain can be replaced by the germline sequence without loss of binding activity . In conclusion , HK20 employs primarily two CDR loops to contact gp41 , while D5 utilizes all six CDRs , which might indicate that the interaction with gp41 is less rigid for HK20 as compared to D5 . This might permit HK20 to engage with a higher degree of conformational flexibility , as suggested by normal mode analyses , which permit to study the vibrational and thermal properties of the complex [38] . Interestingly , the crystal structure reveals that HK20 and D5 Fabs approach the same HR1 epitope with different angles; while D5 binds almost orthogonal towards HR1 , HK20 contacts 5-Helix in a ∼60° angle . It is conceivable that these different approaches may influence the accessibility to the target site . Previous studies reported variable effects of size on viral neutralization by HR1 targeting molecules . For instance , the size of fusion proteins coupled to T-20 affected neutralization efficiency [36] , while fusions to C34 had no effect [37] . D5 IgG and scFv showed comparable or slightly higher potency IC50 values [23] , [39] . In contrast , HK20 Fab and scFv showed , on a molar basis , at least 15-fold higher potency as compared to the corresponding IgG . The fact that HK20 Fab and scFv efficiently neutralize all pseudoviruses tested indicates that the HR1 epitope is easily accessible for proteins of 50 kDa or less and that in the intact IgG the presence of the Fc and/or the second Fab arm obstructs epitope access . It is also possible that temporal constraints during the fusion reaction play an important role in explaining the lower activity of IgG over Fab/scFv , since the diffusion rate of IgG ( 4 . 9×10−7 cm2/sec ) is slightly slower than that of Fab ( 7 . 4×10−7 cm2/sec ) [40] . Both D5 and HK20 show similar kinetics of binding . Our SPR measurements indicated a Kd of 3 . 1 nM for D5 , higher than the previously reported values of 0 . 26 nM [23] and 0 . 05 nM [31] , which , however , have been obtained with 5-Helix as analyte and thus include contributions of HR2 binding . Thus the small differences in affinity are not necessarily the best correlate of viral neutralization , corroborating that neutralization is most likely critically dependent on accessibility to the HR1 target epitope and possibly other factors . The conformational states of gp41 have been studied extensively and are attractive targets for fusion inhibitors and neutralizing antibodies due to the sequence conservation of gp41 [1] , [13] , [14] , [32] , [41] , [42] . However , it is not clear whether this conserved site is sufficiently immunogenic to trigger a neutralizing antibody response in vivo . In addition , since HR1 is only transiently exposed during the fusion reaction , it is itself an unfavorable target for induction of a B cell response . On the other hand , since many Env complexes found on virions are non-functional , it is possible that the prolonged exposure of the HK20 epitope on such complexes induces HK20-like antibodies and favors their affinity maturation . Unlike D5 , which was isolated from a phage library , HK20 was isolated from a memory B cell of an HIV-1 infected individual and was therefore selected in vivo in the course of HIV-1 infection . When reverted to the germline sequence HK20 still bound to the 5-Helix with 50-fold lower affinity but did not show neutralizing activity , indicating a critical role for somatic mutations to achieve high affinity binding and neutralization . Shuffling of Ig chains showed that mutations in the heavy but not in the light chain contributed to the increased binding affinity , a finding that is consistent with the structural data , which reveal only a minor role of CDR L3 for HR1 recognition . In this study we used HK20 as a probe to quantify serum antibodies directed against the same site . Using an HK20 blocking assay we found that some HIV-1 infected individuals ( 20 out of 33 ) have variable levels of HK20-like antibodies with titers ranging from 1∶21 to 1∶528 , which might include neutralizing and non-neutralizing antibodies . A recent study demonstrated that constructs containing HR1 and HR2 can be used to isolate by affinity chromatography from human immune sera antibodies with neutralizing activity in a PBMC-based assay [43] . However it remains to be determined which is the fraction of HR1-reactive antibodies endowed with neutralizing activity [24] . Different neutralization assays employing different target cells produce differences in neutralization breadth and potency with the result that no single assay is capable of detecting the entire spectrum of neutralizing activities [44] . Our findings demonstrate that antibodies that target HR1 are particularly sensitive to the target cell used . Indeed for both HK20 and D5 IgG we found much higher neutralization titers in the HOS-based as compared to the TZM-b1-based assay . It is possible that the high level of CD4 and CCR5 expression on TZM-b1 cells facilitates faster membrane fusion or might even cause a rapid internalization of the virus in the endosomal compartment thus limiting the time window available for antibodies to bind [45] , [46] . It is also worth noting that HK20 potency is generally higher against clade C as compared to clade B viruses , which might be due to differences in the kinetics of virus entry . The HR1 epitope recognized by HK20 is highly conserved ( Figure S3 in Supporting Information S1 ) and only two positions tolerate amino acid exchanges . Notably , the JR-FL isolate has an Arg at position 564 rather than His or Gln which are found in >99 . 5% of the isolates and is only poorly neutralized by HK20 ( IC50 174 nM on HOS cells ) . A similar finding was reported for mAb 8K8 [24] . Our HK20-gp41 structure demonstrates that His 564 is important to coordinate CDR H3 HR1 interaction , which can explain the poor neutralization of JR-FL . T-20 is a peptide inhibitor that targets the gp41 fusion machinery [14] . HK20 scFv shows a neutralization breadth comparable to that of T-20 but on average a ∼3 . 4 fold lower potency on a molar basis in the HOS-based assay . Interestingly , the HK20 epitope does not overlap with the T-20 binding site and consequently we predict that HK20 will be able to neutralize T-20 escape mutants affecting the 547-GIV-549 region [47] , [48] . Thus HK20 scFv represents a new tool to combat HIV-1 infection in general and specifically T-20-resistant viruses . Furthermore HK20 scFv or HK20 scFv mimetics might demonstrate an improved serum half life when compared to T-20 [49] or the next generation of peptide fusion inhibitors [50] . In addition , HK20 may synergize with HR2-specific antibodies or with small molecule entry inhibitors , as reported for mAb D5 in combination with mAb 2F5 [51] . Although HR1 is an attractive target for antibodies due to its high sequence conservation , an open question remains whether HK20-like antibodies are useful in the prevention of infection . While initial studies suggested that this might be a difficult goal [24] , a recent study suggested that immunization with HR1 mimetics can induce relevant titers of HR-1 specific neutralizing antibodies in a species-dependent fashion [29] . In summary our structural and functional data illustrate the general principle that the conserved fusion machinery of gp41 can be targeted by antibodies produced in the course of HIV-1 infection . However , as demonstrated by HK20 , the neutralizing activity may be severely limited by the accessibility of the epitope and by the size of the antibody . We propose that HK20 scFv , smaller engineered single domain HK20 versions or HK20 mimetics with oral bioavailability represent novel tools , complementary to T-20 , to combat HIV-1 infection .
The cDNA corresponding to 5-Helix [30] was synthesized ( Geneart ) and cloned into vector pETM-13 ( EMBL , Heidelberg ) and 5-Helix was expressed in E . coli strain BL21gold ( DE3 ) ( Invitrogen ) . Cells were grown to an OD600nm of 0 . 7 and induced with 1 mM IPTG at 37°C . After 5 hours cells were harvested by centrifugation and lysed in buffer A ( 0 . 02 M Tris pH 8 . 0 , 0 . 1 M NaCl ) . The soluble fraction was discarded and the pellet was resupended in buffer A supplemented with 0 . 2% octyl-β-D-glucopyranoside ( Sigma ) over night at 4°C . Solubilised proteins were separated on an anionic exchange column employing a gradient of buffer A and B ( 0 . 02 M Tris pH 8 . 0 , 0 . 5 mM NaCl ) . Further purification was carried out by gel filtration on a Superdex 200 column ( GE Healthcare ) in buffer A . Gp41-HR1 is a modified version of a circular permutated gp41 where the C-helix precedes the N-helix in sequence and HR2 is truncated to expose the HR1 epitope [52] . Gp41-HR1 is fused N-terminally to a 30 amino acid triple stranded coil ( pIQI ) [53] to increase solubility . Gp41-HR1 was cloned into pETM11 and expressed in E . coli strain BL21gold ( DE3 ) ( Invitrogen ) . Cells were grown to an OD600nm of 0 . 7 and induced with 1 mM IPTG at 37°C . After 3 hours cells were harvested by centrifugation and lysed in buffer A and purified on a Ni2+-NTA column . A final purification step included gel filtration on a S75 Superdex column in buffer A . HK20 IgG was proteolysed for 4h at 37°C with immobilized papain ( Roche ) in buffer C ( 0 . 05 M Bis-Tris pH 6 . 3 ) . Reaction was stopped with E-64 and filtering the solution through a 0 . 22um filter ( Millipore ) . Proteolysed HK20 was dialyzed overnight at 4°C in buffer D ( 0 . 025 M sodium acetate pH 5 . 0 ) and Fab fragments were separated from Fc fragments by cationic exchange chromatography in buffer D . Further purification was achieved by passing the Fabs over a mono P chromatofocusing column ( GE Healthcare ) and a final step included gel filtration on a Superdex 200 column ( GE Healthcare ) in buffer A . HK20 Fabs were mixed with 5-Helix using a 0 . 5 M excess of HK20 Fabs . The complex was separated on a Superdex 75 column in buffer A and complex formation was confirmed by separation of the complex on a 12% SDS-Tris-Tricine gel and on a 10% native PAGE . HK20-5-Helix complex crystals were obtained by the vapour diffusion method in hanging drops mixing equal volumes of complex and reservoir solution ( 0 . 1 M Hepes pH 7 . 5 , 0 . 2 M Ammonium sulphate , 25% PEG 3350 ( w/v ) ) . Crystal quality was improved and single crystals were obtained by microseeding of crystals into drops equilibrated with reservoir buffer ( 0 . 1 M Hepes pH 7 . 5 , 0 . 2 M Ammonium sulphate , 21% PEG 3350 ( w/v ) ) . 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 ) microfocus beamline ID23-2 . Data were processed and scaled with MOSFLM [54] , and SCALA [55] , [56] . The crystals belong to space group P21 with unit cell dimensions of a = 75 . 95Å , b = 62 . 71Å , c = 76 . 61 and β = 97 . 70° . The structure was solved by molecular replacement using PHASER [57] and the D5-5-Helix complex structure as a search model ( pdb ID 2CMR ) . The position of 5-Helix was first localized and the positions of heavy and light chains were successively determined . An initial model was build with ARP/WARP [58] and completed by several cycles of manual model building with COOT [59] and refinement with REFMAC [60] using data to 2 . 3 Å resolution ( Table 1 ) . The final model contains 5-Helix HR1 residues 543–584 ( chain A ) , 541–581 ( chain B ) , 543–582 ( chain C ) and HR2 residues 626–664 ( chain D ) , 624–664 ( chain E ) . The linker regions connecting HR1 and HR2 are disordered . HK20 residues include H chain residues 3–216 and L chain residues 1–214 . Molecular graphics figures were generated with PyMOL ( W . Delano; http://www . pymol . org ) . Co-ordinates and structure factures have been deposited in the Protein Data Bank with accession ID 2xra . BIAcore measurements were performed with the Biacore X instrument ( BIAcore . Inc . ) at 25°C in running buffer ( 10 mM Tris pH 8 . 0 , 100 mM NaCl , 0 . 005% surfactant P20 , 50 µM NiCl ) . Ni2+ NTA chips were coated with gp41-HR1 to a target of 2000 response units ( RU ) . The analyte mAbs D5 and HK20 ( dialyzed o/n in running buffer ) was passed over the chip surface at concentrations ranging from 1 nM to 50 nM for 160 seconds at a flow rate of 20 µl/min and dissociation was recorded during 5 minutes . The chip was regenerated with 20 µl of 35 mM EDTA at 50 µl/min . Binding kinetics were evaluated using the BiaEvaluation software package ( BIAcore , Inc . ) using a Langmuir model 1∶1 with no mass transfer . The clade B and C HIV-1 reference panels of Env clones [61] [62] and the SF162 clone were obtained from the NIH-AIDS Research and Reference Reagent Program ( ARRRP ) . Other non-clade B isolates were provided by the Comprehensive Antibody Vaccine Immune Monitoring Consortium ( CA-VIMC ) . HIV-1 subtype B clone JRFL was kindly provided by Dennis Burton ( Scripps Institute , La Jolla , US ) . Pseudoviruses were produced by co-transfecting HEK293T/17 cells with the env expressing plasmids and the complementing viral-genome reporter gene vector , pNL4-3 . Luc+ . E−R+ ( kindly provided by John R . Mascola , VRC , NIAID , NIH , US ) . TriMab was kindly provided by CFAR , NIBSC-HPA , UK . Virus supernatants Du174 , CM244 , 92UG024 , RW9209 , QH0692 , VI191 , 92BR025 and MN ( P ) were part of the NeutNet Virus Panel and distributed through the Programme EVA Centre for AIDS Reagents ( CFAR ) NIBSC-HPA , UK [44] , and J213 , a subtype B X4 virus is part of a pediatric virus isolation panel [63] . TZM-b1 and HOS . CD4-R5 cells were obtained from the NIH AIDS Research and Reference Reagent program ( ARRRP ) . A single cell infectivity assay was used to measure the neutralization of luciferase-encoding viruses pseudotyped with the HIV-1 Env proteins [64] . Both the HOS-CD4-R5 cell based assay and the TZM-b1 cell based assay were performed as described [34] . Virus titrations ( ID50 ) were performed in a PHA-activated PBMC culture , as previously described [65] . For the neutralization assay 10 to 50 Tissue Culture ID50 ( TCID50 ) of virus were used , though a virus titration was always performed simultaneously with the neutralization assay to calculate the precise TCID50 of each test run . Each reagent was tested in duplicate , in 4 steps of 4-fold dilutions , against two 2-fold dilutions of virus . The protocol is available on the EUROPRISE website www . europrise . org , with a slight modification: cells were washed at day 3 by exchanging the culture supernatant with fresh culture medium . The culture supernatant was harvested at day 7 and tested with an in house HIV p24 antigen ELISA ( Aalto Bio Reagents , Ireland ) . The heavy and light chain nucleotide sequences were analyzed using the IMGT database . Germline-like sequences were determined by reverting mutations to the germline sequence while retaining the original CDR3 junctions and terminal deoxy-nucleotidyl transferase ( TdT ) N nucleotides . IgG and scFv DNAs corresponding to mature and germline-like HK20 and D5 heavy and light nucleotide sequences were synthesized by Genescript ( Genescript , Piscatawy , NJ ) and their accuracies were confirmed by sequencing . D5 VH and VK sequences were obtained through the Protein Data Bank ( PDB accession code 2CMR ) . The scFv HK20 was designed to encode the VK gene followed by a ( GGGGS ) linker , the VH gene and a C-terminal His-tag . AgeI and HindIII restriction sites were added to 5′ and 3′ termini , respectively , during gene synthesis for cloning into the appropriate expression vectors . IgG1 expression vectors contained human IgG1 or Igκ constant regions ( kindly provided by Michel Nussenzweig , Rockefeller University , New York , US ) . The His-tag was used subsequently for scFv purification . Monoclonal antibodies were produced by transient transfection of suspension cultured 293 freestyle cells with PEI . Supernatants from transfected cells were collected after 7 days of culture . Recombinant IgG or scFv were affinity purified with Protein A or Ni2+ chromatography ( GE Healthcare ) , respectively , according to the manufacturer's instructions , and finally desalted against PBS using a HiTrap FastDesalting column . HK20 mAb was biotinylated using the EZ-Link NHS-PEO solid-phase biotinylation kit ( Pierce ) . The competition between polyclonal serum antibodies and biotinylated mAb for binding to immobilized 5-Helix was measured by ELISA . Briefly , plasma samples were added to 5-Helix-coated plates at different dilutions . After 1 hour , biotinylated mAb was added at a concentration corresponding to 80% of the maximal OD level , and the mixture was incubated at room temperature for 1 hour . Plates were then washed and bound biotinylated mAb was detected using AP-labeled streptavidin ( Jackson Immunoresearch ) . The percentage of inhibition was tested in duplicates and calculated as follows: ( 1−[ ( OD sample−OD neg ctr ) / ( OD pos ctr−OD neg ctr ) ] ) ×100 . BD80 value was calculated by interpolation of curves fitted with a 4-parameter nonlinear regression . The accession code of the env sequence of 5-Helix corresponds to the gp41 sequence of HXB2 ( AAA76685; 5-Helix , 2CMR ) . The D5 heavy chain sequence ( CH and VH ) are deposited under 2CMR and the D5 light chain ( CL and VL ) under 2CMR in the NCBI protein database and SwissProt . The sequences of the HK20 heavy and light chains have been deposited with PDB ID code 2xar .
|
The HIV-1 envelope glycoprotein composed of the receptor binding subunit gp120 and the fusion protein gp41 is the prime target for neutralizing antibodies . Receptor binding induces a conformational change in gp41 that transiently exposes the conserved heptad repeat 1 ( HR1 ) region . We have previously isolated the human HR1-specific mAb HK20 and provide now the structural basis for epitope recognition . HK20 employs mainly its CDR H2 and H3 for binding similar to HR1 binding of mAb D5 . We demonstrate that HK20 and D5 bind HR1 with similar affinities; however , HK20 has a broader neutralization breadth than D5 , which might be due to the differences in their approach angles of epitope recognition . Competition analyses of 33 sera from HIV-1 infected individuals reveal significant titers of HK20-inhibiting antibodies in 20 cases , confirming the immunogenicity of the epitope . We demonstrate further that HK20 IgG have limited neutralization breadth and potency while smaller HK20 Fabs and scFv reveal a broad cross clade neutralization breadth . This suggests that the accessibility of the HR1 epitope limits the value of HR1 mAbs for infection prevention , but highlights the importance of smaller versions such Fabs or scFv to combat infection alone or in synergistic approaches with other antivirals .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"infectious",
"diseases/hiv",
"infection",
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"aids",
"immunology/antigen",
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2010
|
Crystal Structure and Size-Dependent Neutralization Properties of HK20, a Human Monoclonal Antibody Binding to the Highly Conserved Heptad Repeat 1 of gp41
|
NK cells are important immune effectors for preventing microbial invasion and dissemination , through natural cytotoxicity and cytokine secretion . Bacillus anthracis spores can efficiently drive IFN-γ production by NK cells . The present study provides insights into the mechanisms of cytokine and cellular signaling that underlie the process of NK-cell activation by B . anthracis and the bacterial strategies to subvert and evade this response . Infection with non-toxigenic encapsulated B . anthracis induced recruitment of NK cells and macrophages into the mouse draining lymph node . Production of edema ( ET ) or lethal ( LT ) toxin during infection impaired this cellular recruitment . NK cell depletion led to accelerated systemic bacterial dissemination . IFN-γ production by NK cells in response to B . anthracis spores was: i ) contact-dependent through RAE-1-NKG2D interaction with macrophages; ii ) IL-12 , IL-18 , and IL-15-dependent , where IL-12 played a key role and regulated both NK cell and macrophage activation; and iii ) required IL-18 for only an initial short time window . B . anthracis toxins subverted both NK cell essential functions . ET and LT disrupted IFN-γ production through different mechanisms . LT acted both on macrophages and NK cells , whereas ET mainly affected macrophages and did not alter NK cell capacity of IFN-γ secretion . In contrast , ET and LT inhibited the natural cytotoxicity function of NK cells , both in vitro and in vivo . The subverting action of ET thus led to dissociation in NK cell function and blocked natural cytotoxicity without affecting IFN-γ secretion . The high efficiency of this process stresses the impact that this toxin may exert in anthrax pathogenesis , and highlights a potential usefulness for controlling excessive cytotoxic responses in immunopathological diseases . Our findings therefore exemplify the delicate balance between bacterial stimulation and evasion strategies . This highlights the potential implication of the crosstalk between host innate defences and B . anthracis in initial anthrax control mechanisms .
NK cells are immune cells that do not need prior exposure to antigen to exert their functions . Their receptors are germline encoded and do not require somatic gene rearrangements . These receptors recognise an array of self-molecules through highly specific mechanisms . The functions of NK cells are regulated through a delicate balance between activating and inhibitory receptors . Although NK cells are traditionally considered as belonging to the innate immune system , a number of recent reports have shown that NK cell education can occur , leading to an expansion of pathogen-specific cells and generation of ‘memory’ cells [1] . NK cells perform a surveillance task and react to transformed , stressed , and virally infected cells . They represent a first-line of defence against cancer and pathogen invasion . NK cells are important immune effectors for preventing microbial invasion and dissemination [1] . They are found in blood as well as in peripheral nonlymphoid tissues and secondary lymphoid organs [1] . In early host responses , NK cells exert two principal functions: secretion of a range of cytokines and natural cytotoxicity . Among secreted cytokines , IFN-γ plays a key role in enhancing immune responses , in particular by modulating macrophage activation [2] . NK-cell activation is readily induced during viral and bacterial infections and requires cytokine and receptor signals that are delivered by myeloid cells [3]–[5] , such as IFN-α/β [6] , IL-12 [7] , IL-15 [6] and IL-18 [8] . Apart from a potential role in polymicrobial sepsis [9] , NK-cell implication during bacterial infections has been studied in few models , mainly of intracellular bacteria ( Mycobacterium sp . , Listeria monocytogenes , Salmonella enterica serovar Typhimurium ) [5] . Among the extracellular bacteria , Staphylococcus aureus and the anaerobe Lactobacillus johnsonii have been reported to stimulate NK cells [5] . Spores of the extracellular bacterial pathogen Bacillus anthracis can efficiently drive IFN-γ production in large amounts by NK cells [10] . The spore is the infectious bacterial form that first interacts with the host , thereby eliciting the earliest host defences against infection . The innate immune response was originally considered as a non-specific response characterized by engulfment and digestion of microorganisms and foreign substances by phagocytic cells . However , innate immunity does show considerable specificity through the activation of different signaling pathways associated with different Toll-like receptors ( TLRs ) that recognise different pathogen-associated molecular patterns . Activation of TLRs thus results in different biological responses depending on the pathogen . NK cell activation induced by the B . anthracis spore is independent of TLR2 , TLR4 , and TLR7 [10] and is probably dependent on multiple receptor engagement due to the complex nature of the spore . The downstream signaling pathways nevertheless implicate the adaptor molecule MyD88 [10] . Among the primary effector cells of innate immunity to intervene at the portal of B . anthracis entry , macrophages have been implicated in two contrasting processes: ( i ) the initiation of infection by playing a role in spore germination [11] and spore dissemination [12] , and ( ii ) the control of infection through spore phagocytosis and destruction [11] , [13] , [14] . Neutrophils are recruited in the first hours of infection and are involved in the early control of infection [14] , [15] . Lung dendritic cells ( DCs ) play a pivotal role in spore uptake and promote dissemination of spores from the alveolar space into the draining lymph nodes [14] . Many of the symptoms of systemic anthrax can be attributed to the subversive effects of a poly-γ-D-glutamic capsule and toxins . Anthrax toxins suppress the immune defences of the host by targeting cells of innate and adaptive immunity . B . anthracis thus completely evades almost all of the key players responsible for efficient host protection . B . anthracis toxins are made up of three secreted proteins , protective antigen ( PA ) , edema factor ( EF; a calmodulin-dependent adenylate cyclase that increases intracellular concentrations of cAMP in host cells [16] ) and lethal factor ( LF; a zinc-dependent metalloprotease which cleaves the N-terminal region of mitogen-activated protein kinase kinases [MAPKKs] [17] ) . Functionally , the combination of PA with EF or LF forms edema toxin ( ET ) or lethal toxin ( LT ) respectively [18] , [19] . LT kills or inactivates monocytes , macrophages , and neutrophils [14] whereas ET disrupts cytokine networks in monocytes [14] , and reduces macrophage migration [14] . Both LT and ET impair neutrophil actin-based motility , resulting in paralysis of PMN chemotaxis [14] , [20] . Anthrax toxins impair activation and maturation of DCs , thus blocking the initiation of adaptive immunity [21] , [22] . LT disrupts TCR signaling in CD1d-restricted NKT cells , leading to functional unresponsiveness [23] . LT also severely reduces B-cell proliferation , impairs immunoglobulin production [24] , and blocks MAPKK-dependent cytokine production in CD4+ T cells [25] , [26] . Furthermore , ET reduces T-cell migration [14] and disrupts T-cell function [14] . Anthrax toxins are therefore involved in mediating immune evasion of the bacterium by interfering with the innate and adaptive immune responses . NK cells are pivotal in the first-line of innate defence , serving as a functional bridge between innate and acquired immunity . To our knowledge , their role during B . anthracis infection has yet to be addressed . The present study aimed to characterise the mechanisms of NK-cell activation by B . anthracis spores , the strategies employed by the bacteria to subvert and evade this response through toxin secretion . We also aimed to identify the relevance of these processes for controlling B . anthracis infection . Relatively few studies have linked specific cytokines with protection against B . anthracis . However , IFN-γ increases the ability of macrophages to resist destruction by B . anthracis and to kill the bacteria [27] and we have shown that sensitivity to B . anthracis infection was partially dependent on IFN-γ [28] . As NK cells are the main innate immune cells to produce IFN-γ , we focused our study on these novel cellular targets of anthrax toxins in the context of the complex heterocellular interactions induced by B . anthracis spores .
B . anthracis spores induce naive spleen cells to secrete large amounts of IFN-γ . This phenomenon depends upon the interactions between CD49b+ cells and accessory cells from the CD49b-negative fraction ( Figure 1A ) . Depletion of CD49b+ cells led to a significant 6 . 5-fold decrease of IFN-γ production ( Figure 1A ) . Direct stimulation of purified CD49b+ cells with spores did not induce IFN-γ secretion . However , co-culture of positively selected CD49b cells with CD49b-negative splenocytes restored IFN-γ production upon spore stimulation ( Figure 1A ) . As our study was focused on spore components as the initial stimuli of the innate immune system , spores needed to be inactivated and prevented from germinating and developing into vegetative bacilli . Different means of inactivation ( i . e . heat-inactivation , germination-deficient spores , and live spores in the presence of antibiotics ) did not modify the extent of IFN-γ induction when compared with formaldehyde inactivation ( Figure 1B ) . Spores of the phylogenetically distant B . subtilis gave rise only to a modest IFN-γ production , demonstrating that B . anthracis spore components are more efficient in inducing IFN-γ production than those of B . subtilis . Latex beads of comparable size as B . anthracis spores were ineffective in stimulating IFN-γ secretion by splenocytes , indicating that phagocytosis of inert particles alone was not sufficient and that B . anthracis spores were central for this response . Similarly , stimulation with spores of positively selected CD49b cells with bone-marrow derived macrophages ( BMDMs ) resulted in a strong induction of IFN-γ secretion ( Figure 1C ) and activation of CD49b+ cells ( Figure 1D ) ; i . e . a 7 . 3-fold upregulation of the leukocyte early activation marker CD69 and a 32-fold increase of intracellular IFN-γ positive cells ( Figure 1D and S1A ) . Stimulation of isolated CD49b+ cells or BMDMs by spores did not result in IFN-γ secretion ( Figure 1C ) , nor did it change basal expression of CD69 or intracellular IFN-γ ( Figure 1D ) . IFN-γ secretion by CD49b+ cells from NKT-deficient CD1d−/− mice [29] , [30] was similar to that by CD49b+ wild-type cells when co-cultured with spore-stimulated wild-type BMDMs ( Figure 1E ) . This strongly suggests that NK cells were the main source of IFN-γ production in this bacterial system . NK-cell activity is regulated by both contact-dependent and soluble signals . Direct contact between BMDMs and NK cells was necessary for NK cell activation as: ( i ) conditioned medium from spore-stimulated BMDMs did not elicit IFN-γ secretion ( Figure 1C ) ; and ( ii ) physical separation of purified NK cells and BMDMs in compartments of transwell plates did not lead to IFN-γ secretion , even if stimulation with the spores was effected in both compartments ( Figure 1C ) . The activating receptor , NKG2D , elicits cytokine production by NK cells [31] , [32] . The present study also implicated the NKG2D receptor in the interaction between NK cells and spore-stimulated BMDMs as addition of neutralizing antibody for the NKG2D receptor decreased IFN-γ production ( Figure 1C and 1F ) . Similarly , neutralizing antibody for RAE-1 —one of the NKG2D ligands— partially inhibited IFN-γ production and co-neutralization of NKG2D and RAE-1 led to a greater reduction ( Figure 1F ) . Finally , stimulation of BMDMs with B . anthracis spores resulted in an approximately 19-fold upregulation of RAE-1 expression compared with non-stimulated BMDMs ( Figure 1G ) . The activating receptor NKp46 was not involved as its neutralization did not modify IFN-γ secretion induced by spores ( Figure S1B ) . Taken together , these results indicate that NK cell activation and IFN-γ production in response to B . anthracis spores are dependent on an accessory cell , are contact-dependent , and occur through RAE-1-NKG2D interaction . Stimulation of BMDMs with B . anthracis spores induced secretion of IL-12 ( Figure 2A ) . Neutralization of IL-18 or blocking the IL-15Rα receptor did not alter IL-12 secretion . Macrophage-secreted IL-12 appeared to play a key role in NK-cell activation by B . anthracis spores as: ( i ) IL-12 neutralizing antibodies abolished IFN-γ secretion both in splenocytes and co-cultured BMDMs/purified NK cells ( Figure 2B ) , and ( ii ) stimulation of splenocytes from IL-12−/− and IL-12 R−/− mice did not elicit IFN-γ production ( Figure 2C ) . Furthermore , absence of IL-12 secretion by spore-stimulated BMDMs from IL-12−/− mice did not induce IFN-γ secretion by wild-type NK cells ( Figure 2D ) . However , NK cells from IL-12R−/− mice were weakly activated by spore-stimulated wild-type BMDMs ( Figure 2E ) , indicating that other signals besides IL-12 were able to activate NK cells . This low amount of IFN-γ production was completely abolished by the addition of neutralizing antibody for IL-18 ( Figure 2E ) . Thus , in the absence of IL-12 signaling , IL-18 becomes essential for the induction of IFN-γ . This was experimentally confirmed by the addition of IL-18 neutralizing antibodies , which significantly decreased spore-induced IFN-γ production by wild-type splenocytes or co-cultured BMDMs and purified NK cells ( Figure 2B ) . Expression of the IL-12 receptor by BMDMs was also important for full NK cell activation in response to spores; IFN-γ production by wild-type NK was significantly lower with spore-stimulated BMDMs from IL-12R−/− mice compared with their wild-type counterparts ( Figure 2D ) . This finding implies a positive feedback of IL-12 and its important role in influencing the stimulatory capacities of macrophages . Blocking the IL-15Rα receptor led to a partial but significant inhibition of IFN-γ production in both spore-stimulated splenocytes and purified NK cells co-cultured with BMDMs ( Figure 2B ) . Finally , simultaneous neutralization of IL-18 and IL-15 strongly decreased IFN-γ production ( Figure 2B ) . This indicates that IL-12-induced IFN-γ production depends upon IL-18 and IL-15 and suggests a synergistic mechanism of action of IL-12 with IL-18 and/or IL-15 . As mentioned above , inhibition of IFN-γ production by IL-18 or/and IL-15 neutralizing antibodies was not associated with a decrease in IL-12 secretion ( Figure 2A ) . The hypothesis that IL-18 or IL-15 are necessary for optimal IL-12 secretion by spore-stimulated BMDMs can therefore be excluded . Of the other cytokines tested , TNF-α , IFN-α , IFN-β , and IL-10 were not involved in the stimulation of IFN-γ secretion , as their neutralization did not interfere with IFN-γ secretion ( Figure S1B ) . IL-18 is stored in internal cellular compartments as a precursor and can rapidly be secreted through caspase-1 activation whereas IL-12 needs to be synthesized before secretion . To explore whether IL-18 is secreted earlier than IL-12 in spore-induced macrophage activation and NK cell IFN-γ production , we determined whether short-term priming with IL-18 or IL-12 was sufficient to achieve full activation of NK cells during stimulation with B . anthracis spores . A 4-h priming of splenocytes with IL-18 before spore stimulation followed by neutralization of IL-18 for the remaining incubation period with spores was sufficient to reach similar concentrations of IFN-γ to those obtained in the absence of IL-18 neutralization . In contrast , IL-18 neutralization for the entire length of spore-incubation significantly decreased IFN-γ production ( Figure 2F ) . However , similar short-term priming with IL-12 , followed by neutralization of IL-12 for the remaining incubation period with spores , only partially restored the IFN-γ response compared with spore-incubation without IL-12 neutralization ( Figure 2F ) . Thus B . anthracis spores induce IL-18 signaling as a primary event that probably synergizes with IL-12 signaling which needs to be effective for a longer period to activate IFN-γ secretion by NK cells . Both IL-12 and IL-18 are therefore essential for IFN-γ production by NK cells in response to B . anthracis spores . Spore recognition by macrophages triggers downstream signaling pathways that are dependent on MyD88 , an adapter protein essential not only for the induction of inflammatory cytokines triggered by TLRs , but also for signaling downstream to IL-18 and IL-1 receptors . Splenocytes from MyD88-deficient mice cannot produce IFN-γ in response to B . anthracis spores [10] . To identify the cells on which the MyD88 adapter protein is required , we performed mixed experiments co-culturing macrophages and NK cells from WT or MyD88-deficient mice . NK cells deficient in MyD88 did not produce IFN-γ; this is most likely due to the defect in IL-18 downstream signaling as IL-18 is central in this system of NK cell activation ( Figure 2G ) . Macrophages from MyD88-deficient mice were also impaired in their capacity to help NK cells to produce IFN-γ following spore stimulation . IL-12 production was strongly decreased ( Figure 2H , left panel ) , thus inhibiting IFN-γ production ( Figure 2H , right panel ) . These results demonstrate that MyD88 is implicated both in the macrophage —through recognition of B . anthracis spores by yet to be characterised pathogen pattern-based receptors— and in the NK cells —through activation of the IL-18 signaling pathway . As B . anthracis toxins secreted by the nascent bacilli upon spore germination could subvert the innate immune response to spores at the initial step of infection , the effects of toxins on spore-induced IFN-γ production were evaluated . The toxins interfered with contact-dependent signaling , as the spore-induced increase in RAE-1 expression on BMDMs was efficiently downregulated by ET or LT-treatment ( Figure 1G ) . The toxins also impaired cytokine-dependent signaling . LT disrupted the ability of splenocytes stimulated with B . anthracis spores to produce both IL-12 and IFN-γ , in a dose-dependent manner ( Figure 3A and 3B ) ; doses as low as 1 ng/ml and 0 . 1 ng/ml , respectively , significantly inhibited IL-12 and IFN-γ secretion . The addition of IL-12 or IL-18 did not restore LT-inhibited IFN-γ secretion ( Figure 3C ) . Cell viability was unaffected ( Figure S1C ) . These results suggest that LT blocks the NK cell response to IFN-γ inducing stimuli by interfering with IL-12 and IL-18 signaling . To explore the direct effects of LT on NK cells and the mechanisms of inhibition of IFN-γ production by LT , purified NK cells were co-stimulated with IL-12 and IL-18 in the absence of accessory cells . As expected [33] , this resulted in abundant IFN-γ production ( Figure 4A and 4B; PA only ) and rapid phosphorylation of p38 , ERK1/2 , and JNK MAPK ( Figure 4C ) . IFN-γ secretion by IL-12/IL-18-stimulated purified NK cells was totally inhibited with LT in a dose-dependent manner ( Figure 4A ) ; statistically significant inhibition was observed from doses as low as 1 ng/ml in these conditions of strong stimulation . LT inhibited p38 , ERK1/2 , and JNK MAPK phosphorylation ( Figure 4C ) . The viability of NK cells was unaffected by LT treatment , even at the highest dose of 100 ng/ml over the 18 h culture period ( Figure 4D ) . Assessment of metabolic activity using the MTS assay ( which measures the activity of mitochondrial deshydrogenases ) showed a reduction at 1 ng/ml and reached a plateau from 10 ng/ml with a circa 50% decrease ( Figure 4D ) . Metabolic activity was thus low in LT-treated NK cells , whereas viability was maintained . NK cells possess a dual function , cytokine secretion and natural cytotoxicity towards specific targets [1] . We determined whether LT could also alter the cytotoxic activity of NK cells . LT significantly reduced the ability of NK cells to kill the mouse YAC-1 lymphoma target cell in vitro ( Figure 5A ) . Thus , our results show that NK cells are highly sensitive to LT subverting both functions , leading to a strong inhibition of cytokine secretion and natural cytotoxicity . Similarly , ET disrupted the ability of splenocytes stimulated with B . anthracis spores to produce both IL-12 and IFN-γ in a dose-dependent manner ( Figure 3A and 3B ) . A dose of 10 pg/ml significantly inhibited both IL-12 and IFN-γ secretion , whereas the basal secretion IL-12 was not inhibited ( Figure 3A ) . The addition of IL-12 during spore stimulation did not restore the IFN-γ secretion ( Figure 3C ) . In contrast , the ET-mediated inhibition was reversed by addition of IL-18 ( Figure 3C ) . This was related to the persistence of a basal IL-12 secretion , as addition of neutralizing antibodies for IL-12 abolished restoration of IFN-γ production by recombinant IL-18 ( Figure 3C ) . These results strongly suggest that ET blocked IFN-γ production by acting mainly on macrophages , as NK cells were still functional and able to secrete IFN-γ . To explore in detail the direct effects of ET on IFN-γ secretion inhibition in NK cells , IL12/18-stimulated purified NK cells were exposed to graded doses of ET in the absence of accessory cells . ET did not inhibit IFN-γ production at doses up to 100 ng/ml , and only slightly at the high dose of 1 µg/ml ( Figure 4B ) , despite expression of adenylate cyclase activity , detected by a marked increase in intracellular cAMP ( 2 . 8-fold higher than in untreated IL-12/IL-18-stimulated purified NK cells ) ( Figure 4E ) . In contrast , ET significantly reduced the ability of NK cells to kill the YAC-1 target cells in vitro , exhibiting an even stronger inhibition capacity when compared with the effects of a similar dose of LT ( Figure 5A ) . The above results show that: ( i ) ET and LT similarly disrupt IFN-γ production by spore-stimulated splenocytes , but the mechanism of inhibition is different for each toxin; LT acts both on macrophages and NK cells , whereas ET acts on macrophages , and ( ii ) both ET and LT directly alter the innate ability of NK cells to exert their natural cytotoxicity function . During anthrax infection , B . anthracis toxins are present in the infected tissues and circulate in the host vascular system . To evaluate the functional consequences on the natural cytotoxic activity of NK cells in vivo , we exploited an in vivo model of NK cell cytototoxic activity relying on the capacity of NK cells to recognise and eliminate MHC class I-deficient cells . Equal ratios of MHC class I-deficient splenocytes ( from C57BL/6 β2m−/− , high CFSE labeling ) and wild-type C57BL/6 MHC class I-expressing splenocytes ( low CFSE labeling ) ( Figure 5B; “injected mix” ) were injected intravenously into syngeneic wild-type C57BL/6 mice . The elimination of the MHC class I-deficient β2m−/− cells was quantified in the spleen relative to the MHC class I-expressing cells [34] . In this in vivo model , MHC class I-deficient β2m−/− cells are rapidly eliminated 16–24 h following adoptive transfer , through the natural cytotoxicity function of NK cells ( Figure 5B and 5C; non-treated; cytotoxicity of 94% ) . As controls , in vivo activation of NK cells by injection of the classical NK cell cytotoxic activator poly: ( IC ) [35] led to elimination of the MHC class I-deficient cells ( CFSE high ) ( Figure 5B ) . In contrast , in vivo depletion of NK cells led to persistence of MHC class I-deficient cells ( Figure 5B ) . Pretreatment of mice with ET ( EF+PA ) by intravenous inoculation drastically inhibited clearance of the MHC class I-deficient cells ( CFSE high ) ( Figure 5B and 5C; remaining cytotoxicity of 27% ) . This effect was related to the adenylate cyclase activity of EF , as the Bordetella pertussis toxin CyaA , which has a similar enzymatic activity , also inhibited the elimination of the MHC class I-deficient cells ( Figure 5B ) . EF , LF or PA alone , and a mutated enzymatically inactive CyaA did not modify the capacity of NK cells to specifically eliminate the MHC class I-deficient cells ( Figure 5B and S1D ) . LT ( LF+PA ) also decreased MHC class I-deficient cell clearance , albeit to a lesser extent ( Figure 5B; remaining cytotoxicity of 51% ) . These results demonstrate the high efficiency of B . anthracis toxins , especially ET , in impairing the natural cytotoxicity of NK cells in vivo . NK cells patrol the host tissues to detect and react to any danger signal . No data are available on their involvement during B . anthracis infection . We therefore visualised in vivo NK cell recruitment into the initial infectious foci and the draining lymph node . Biphotonic imaging was performed on the infected ear of mice after inoculation with spores of an encapsulated toxin-deficient B . anthracis strain ( due to confinement restrictions , these experiments could not be performed with encapsulated toxin-secreting strains ) ; visualisation of the NK cells was carried out through intravenous adoptive transfer of CFSE-labeled purified NK cells in mice whose blood vascular system was labeled through injection of dextran-rhodamine ( Figure 6A–D ) . During the first 5 h of infection , NK cells were observed circulating in the ear capillaries ( #20 µm diameter ) in both infected and control ears ( Figure 6A ) . Occasionally , long-term ( more than 6 sec ) interaction of NK cells with the capillary endothelium could be observed in the infected ear ( Figure 6B ) . At 24 h of infection , NK cells were observed in the infected ear tissue , outside the vascular bed in the collagen-rich tissue ( as visualised in blue by the second harmonic ) , either close to the vascular lining ( Figure 6C , top ) , or at a distance ( Figure 6C , bottom ) . At this time of infection , NK cells began to be observed in the capsular sinus of the draining lymph nodes ( Figure 6D ) . Thus , infection with spores of B . anthracis triggers recruitment of NK cells into the local site of infection and further migration into the subcapsular sinus of the draining lymph node , where these cells could exert their functions . We then determine the early in vivo impact of toxin secretion by nascent bacilli on the local NK cell response . To this end , we characterised the recruitment of NK cells and macrophages into the draining lymph node at 24 h of infection with the encapsulated toxin-deficient B . anthracis strain , and its encapsulated derivatives expressing either ET or LT ( Figure 6E ) . The total number of lymphoid cells and the percentage of F4/80+ cells and CD49b+ cells was increased in the draining lymph node of the encapsulated toxin-less infected mice , compared with the lymph node from PBS-injected mice ( data not shown ) . Compared with an uninfected lymph node , the absolute number of CD49b+ cells ( Figure 6E , left panel ) and F4/80+ cells ( Figure 6E , right panel ) in the infected draining lymph nodes significantly increased by nine-fold and six-fold respectively; p<0 . 01 ) . Production of either ET or LT during infection with the encapsulated strains 9602L ( EF+PA+ ) or 9602C ( LF+PA+ ) led to a significant decrease in the number of CD49b+ and F4/80+ cells in the infected lymph node , as compared with lymph nodes from mice infected with the encapsulated toxin-deficient strain ( Figure 6E; p<0 . 01 ) . These results clearly indicate that infection with B . anthracis spores is recognized by the innate immune system , leading to early recruitment of NK cells into the cutaneous tissue , and recruitment of NK cells and macrophages into the draining lymph node . Through the action of its toxins , B . anthracis blocked recruitment of both NK cells and macrophages . To define the role of NK cells in B . anthracis infection , the effect of in vivo NK cell depletion was characterised . Accelerated systemic dissemination of the bacteria from the site of infection was observed , with early bacterial seeding of the spleen in NK-depleted mice versus control infected mice ( Figure 6F ) . This result demonstrates the in vivo role of NK cells in controlling early B . anthracis dissemination .
In the present study , we have provided insight into the mechanisms of cytokine and cellular signaling that enable B . anthracis spores to efficiently drive IFN-γ production by NK cells . We also showed how B . anthracis toxins may allow the bacteria to avoid immune clearance by altering cytokine production and natural cytotoxicity . The communication between NK cells and spore-activated macrophages was both cytokine- and contact-dependent and involved engagement of the NKG2D receptor . Our data indicate that macrophage-derived cytokines alone , or direct stimulation of NK cells by B . anthracis spores alone , are not sufficient to drive activation of NK cells , as assessed by CD69 and IFN-γ expression . Although IL-12 was secreted by spore-stimulated macrophages , NK cell activation did not occur when cellular interactions between NK cells and macrophages were prevented . Thus , cell-cell contact was a critical factor in macrophage/NK-cell interactions . Contact dependency involved at least engagement of the activating NKG2D receptor with one of its main ligands on the macrophage , RAE-1 [36] , whose expression was upregulated upon spore stimulation . Stimulation with various TLR agonists leads to surface expression of RAE-1 on macrophages [37] . RAE-1-NKG2D interactions contribute to IFN-γ production and provide a molecular mechanism by which NK cells and infected macrophages communicate directly during an innate immune response to infection [38] . The requirement for direct contact between NK cells and macrophages for full NK cell activation might in part also be a manifestation of the dependency of NK cells on cytokine-mediated signals delivered when contact is established . For example , delivery to NK cells of IL-15 and IL-18 produced by macrophages and dendritic cells occurs in a synaptic manner , so that as soon as the cytokines are secreted , they are captured by the secreting or target cell [39] , [40] . Secreted IL-15 , for example , is immediately bound by the IL-15 receptor-α expressed on the surface of the accessory cells and is presented to NK cells in a cell-contact dependent manner [41] . Similarly , IL-18 is delivered to NK cells through cell contact with dendritic cells [39] . The IL-12 receptor may also localise at the contact zone between NK cells and macrophages [4] , indicating that direct contact might be pivotal for efficient delivery of IL-12 to NK cells [42] . Our data are thus consistent with the strict contact-dependency of NK-cell activation , as these three cytokines are induced under spore stimulation and are strictly required for NK cell activation . We demonstrated a predominant implication of IL-12 and IL-18 in NK-cell activation by B . anthracis spores , and to a lesser degree of IL-15; these cytokines were secreted by the spore-stimulated macrophages . However , individually , IL-12 , IL-15 , and IL-18 failed to induce effector responses in purified NK cells . IFN-γ production was almost abolished when both signals from IL-15 and IL-18 were simultaneously absent . These observations strongly suggest a synergistic mechanism of action of IL-12 with IL-15 and/or IL-18 . Endogenous IL-15 produced by LPS-activated monocytes works in concert with IL-12 for optimal IFN-γ production by NK cells [43] . Several studies have shown that IL-18 responsiveness is dependent upon IL-12 and vice versa for T cells [44] , [45] . IL-12 and IL-18 are considered important mediators of IFN-γ production by NK cells and T lymphocytes [46] . The molecular mechanism underlying the synergy between IL-18 and IL-12 may be explained in part by reciprocal modulation of cytokine receptor expression . Specifically , IL-18 upregulates IL-12R expression [47] , whereas IL-12 upregulates expression of the IL-18R [48] . In our model , liberation of IL-18 was probably a primary event in response to B . anthracis spores . Short-term pretreatment with IL-18 prior to stimulation with spores fully restored IFN-γ production , even when IL-18 paracrine activity was later neutralized . In contrast , IL-12 was required for a longer time period to obtain full restoration of IFN-γ secretion . This may reflect differences in storage and delivery for these two cytokines . IL-18 is stored as a biologically inactive precursor in secretory organelles of the endolysosomal compartment and , upon stimulation , can be rapidly released into the extracellular milieu after cleavage by caspase-1 [49] , [50] , whereas IL-12 needs to be synthesised and secreted along the classical secretory pathway [51] . Finally , our data emphasise the key role of IL-12 which is produced by spore-activated macrophages , and demonstrate that it acts both on NK cells and macrophages . Absence of the IL-12 receptor on NK cells reveals the secondary key role of IL-18 . For macrophages , the absence of the IL-12 receptor greatly decreased their capacity to provide the necessary accessory signals to NK cells . Indeed , a general and critical role of IL-12 in potentiating the accessory function of myeloid antigen presenting cells has been suggested by Grohmann et al [52] . IL-12 is thus not only a connective element between accessory cells and lymphocytes , but also a key molecule for programming macrophage and dendritic-cell functions [53] , [54] . We demonstrated that the inflammatory response induced by B . anthracis spores requires MyD88-mediated signaling both on macrophages and NK cells . MyD88 is an adaptor protein which is essential for signaling downstream of many TLRs , and also of IL-18 and IL-1 receptors [55] . MyD88-implication in the NK cell response was thus expected , as IL-18 is crucial for NK cell activation by B . anthracis spores . On the other hand , MyD88-dependence of spore recognition by macrophages , leading to deficient IL-12 secretion and , by way of consequence , absence of NK cell activation , shows that it is pathogen-pattern based . However , the interactions between the components of the highly complex spore particle and TLRs or related receptors still need to be characterised in detail . Spore recognition most probably involve several receptors; knocking-out one or more TLRs has indeed been reported not to modify the biological response observed , either in vitro [10] or in vivo ( Tod Merkel , personnal communication ) . IL-18 is a proinflammatory cytokine that belongs to the IL-1 cytokine family . IL-18 and IL-1 are related in terms of structure , processing , receptor and signaling pathways [56] . Secretion of the active form of both cytokines is dependent on caspase-1 activation that is required for the processing of the IL-1 and IL-18 precursors . Release of mature IL-18 depends on concomitant activation of caspase-1 and TLR engagement by pathogen-derived agonists [56] . This suggests that spore recognition is able to deliver both activation signals . IL-18 amplifies the innate immune response by inducing the expression of cytokines and chemokines such as IL-1β , TNFα and IL-8 . The macrophage cytokine response could thus be triggered both directly and indirectly by the spores . B . anthracis toxins were highly efficient in subverting the innate immune response , triggered by B . anthracis spores through activation of macrophages and induction of IFN-γ secretion by NK cells . Even at very low doses , the toxins disrupted IFN-γ production by spore-stimulated splenocytes . They altered both contact-dependent and cytokine-dependent signaling . Both toxins reduced expression of RAE-1 on the surface of spore-stimulated macrophages , thus decreasing signaling through the activating receptor , NKG2D . Indeed , defects in the expression of RAE-1 molecules have been hypothesized to contribute to reduced NK cell function [57] . The mechanisms of inhibitory action for each toxin on cytokine secretion were different . LT targeted both macrophages and NK cells , whereas ET blocked the macrophage activating functions but did not affect the IFN-γ secretion capacity of NK cells . Impairment of IFN-γ production by ET depended mainly on inhibition of IL-18 production by macrophages , a primary event in NK cell stimulation . Basal IL-12 secretion by macrophages was not affected and was sufficient to drive normal amounts of IFN-γ secretion by NK cells when the external IL-18 concentration was restored . A number of studies have addressed the effects of LT and ET on cytokine secretion and their consequences on immunity [18] , [58] , [59] . While LT inactivation of MKK signaling pathways leads to almost invariable inhibition of the innate immune response , ET-induced cAMP increase results in a complex immunomodulatory effect . LT inhibits , whereas ET differentially regulates the release of pro-inflammatory cytokines in macrophages and DCs [22] , [60] , [61] . Our results on ET-induced IL-12 inhibition are consistent with previous reports of an inhibitory effect of ET on macrophages and DCs [22] . Of note is our observation that ET has a dominant effect on IL-18 secretion . Secretion of IL–18 by activated macrophages depends on the protease caspase-1 that converts the IL-18 precursor to the mature and biologically active cytokine [62] , [63] . Caspase-1 activation is induced by B . anthracis spores and has been suggested to play a critical role in host defences against B . anthracis infection in vivo [64] . LT has also been reported to induce caspase-1 activation which results in IL-1β and IL-18 intracellular processing [64] . However , the release of these cytokines occurs as a passive event , resulting from cell death and lysis [65] . NKT cells are a specialized subset of T lymphocytes sharing both T cell and NK cell markers with the capacity to recognise microbial glycolipid antigens [66] . However , their modes of recognition are distinct from NK cells and their functions are quite different [67] . The effect of LT on NKT cells has recently been explored after stimulation with their classical cognate ligand , α-galactosylceramide [23] . LT was reported to induce an anergy-like unresponsiveness in NKT cells following stimulation via their T cell receptor [23] . One of the best characterised functions of NK cells is their natural cytotoxic activity against virus-infected cells or cells undergoing tumor transformation [35] . The present study showed that both ET and LT directly altered the innate ability of NK cells to exert their natural cytotoxic function , both in vitro and , most importantly , in vivo . Furthermore , ET exerted a much stronger inhibitory effect than LT . EF -the enzymatic moiety of ET- is an adenylate cyclase leading to elevated concentrations of intracellular cAMP [16] . We demonstrated that similar in vivo inhibition of the NK cell cytotoxic activity was also induced by the CyaA toxin of Bordetella pertussis . Thus , as a bacterial adenylate cyclase , CyaA toxin produces the same end-effect i . e . increase of intracellular cAMP [68] . These data thus strongly suggest that inhibition of NK-cell cytotoxic activity by ET is mediated via the activation of cAMP downstream pathways . We thus provide the first demonstration that the ET subverting action leads to dissociation in NK-cell function , which strongly blocks natural cytotoxicity without affecting IFN-γ secreting capacity . Its high efficiency stresses the impact this toxin may exert on anthrax pathogenesis . NK cell-associated receptors have been implicated in certain autoimmune diseases [69] , [70] and NK cells have been suggested to play a role in modifying T cell-mediated autoimmunity . As the cytotoxic activity of cytotoxic CD8 T cells shares at least in part , common mechanisms with NK cells [71] , we believe these AMPc-elevating compounds could be of use to inhibit the deregulated or increased cytotoxic activities that underlie NK or CTL-dependent autoimmune pathology [72] . Our in vivo data show that NK cells are rapidly recruited into the cutaneous tissue infected by B . anthracis . Both NK cells and macrophages were detected early in the draining lymph node in the absence of toxin secretion . In contrast , production of either ET or LT during infection drastically inhibited this local immune response . The reduced inflammatory response could be related to the immunosuppressive activities of LT and ET [59] . By increasing the intracellular concentration of cAMP and cleaving MKKs , B . anthracis toxins have the potential to interfere with chemotactic signaling for neutrophils , T-cells and macrophages [14] , [20] , [73] . Considering that -in the context of anthrax infection- macrophages appear to afford protection to the host [14] , [74] , it is not surprising that B . anthracis has developed means of suppressing certain macrophage functions such as their migration to lymph nodes . We furthermore demonstrated that NK cells controlled the infectious process , as in vivo NK cell depletion resulted in an increased bacterial dissemination to the spleen . Conceptually , NK cell depletion could mimick the consequences of NK cell inactivation by the toxins . Clearly , further experiments are needed to address how NK cells control in vivo spreading of bacteria during anthrax infection . In a previous study , we showed the central impact of ET on bacterial control of dissemination in the draining lymph node , both after cutaneous and inhalational infections [75] . The draining lymph node thus appears to be the key organ for delaying bacterial systemic dissemination . We postulate that this control most probably occurs through direct interactions between NK cells and accessory cells , resulting in IFN-γ production and macrophage activation of their bactericidal activity . NK cells respond to pathogens through both cytokine secretion and natural cytotoxicity . The relative impact of each NK function on the development of an infectious process depends on the type of infection [5] . Natural cytotoxicity has mainly been characterised against tumoral or virally-infected targets cells [76] . Direct natural cytotoxicity to infected cells in bacterial infections has rarely been reported , primarily with intracellular pathogens ( Mycobacterium sp . , Listeria monocytogenes; [5] ) . As B . anthracis is an extracellular pathogen , we hypothesise that NK cell cytotoxic activity may potentially occur either at the initial infection step , when a proportion of the infecting spores are phagocytosed by accessory resident cells such as macrophages or dendritic cells - which could provide the necessary contact- and cytokine-dependent signaling to the NK cells , or , at a later stage of infection , through recognition by NK cells of cellular stress induced by the toxins . The potential role of NK cell cytotoxicity during B . anthracis infection remains to be explored in depth . The present study is the first to investigate the direct modulation of NK cell functions , IFN-γ producing capacity and natural cytotoxicity , by B . anthracis , and their subversion by ET and LT . Our findings exemplify the delicate balance between stimulation of the initial host control mechanisms by B . anthracis spores and the bacterial evasion strategies to overcome these innate host defences . NK cells are important immune effectors for preventing microbial invasion and dissemination [77] , performing their surveillance function and establishing intercellular communications at an early stage of infection . The model we propose hypothesises that interactions of macrophages and dendritic cells with the infecting spores and spore components would lead to NK cell activation and IFN-γ production , through a combination of signals derived from intercellular contacts with macrophages and from cytokines secreted by these cells . The accumulation of NK cells and macrophages in the appropriate cytokine environment of the infected lymph nodes will thereby amplify the inflammatory response . Such a positive feedback loop is likely to be important in the control and pathogenesis of anthrax . Furthermore , by secreting toxins , nascent B . anthracis bacilli will alter spore-induced contact-dependent signaling and cytokine production . This will prevent efficient immune-cell contacts and initiation of inflammation and inflammatory-cell recruitment into the infected draining lymph node , resulting in successful bacterial colonization and spreading of infection .
All the animal experiments described in the present study were conducted at the Institut Pasteur according to the European Union guidelines for the handling of laboratory animals ( http://ec . europa . eu/environment/chemicals/lab_animals/home_en . htm ) and were approved by the animal care and use committee at the Institut Pasteur . All efforts were made to minimize suffering . RPLC2 is a Sterne derivative that produces inactive lethal and edema factors mutated in their enzymatic site [78] . The 9602P ( delta-pagA ) , 9602C ( delta-cya ) and 9602L ( delta-lef ) strains [75] are derivatives of the highly virulent natural human isolate 9602 [79] . RPLC2 spores were produced , purified on Radioselectan ( Renografin 76% , Schering ) and formaldehyde-inactivated as previously described [80] , [81] . Formaldehyde-inactivated spores ( FIS ) were quantified using a Malassez counting chamber and inactivation was confirmed by plating on BHI agar . Heat inactivation was carried out as previously described [10] . B . subtilis strain SMY was a kind gift from Abraham L . Sonenshein ( Department of Molecular Biology and Microbiology , Tufts University School of Medicine , Boston , USA ) and spore inactivation was performed as for B . anthracis . The germination-deficient mutant on the 7702 background was constructed by inactivation of the cwlD , sleB and cwlJ1 genes through double crossing-over insertion of antibiotic cassettes ( spectinomycin , erythromycin and kanamycin respectively ) using techniques as previously described [82] ( Manuel Lopez-Vernara , Fabien Brossier and Michèle Mock , unpublished results ) . Germination was decreased by at least 6 log10 on BHI agar . The inoculum used in the in vitro cell infection assays ( 2×106 spores per well , as assessed by counting in a Malassez chamber ) did not give rise to any CFU upon numeration . Six- to 10-week-old C57BL/6 and FVB female mice were purchased from Charles River ( L'Arbresle , France ) . CD1d-deficient C57BL/6 mice lacking both CD1d1 and CD1d2 [83] were a kind gift from Dr Claire-Lise Forestier ( G5 Parasite Virulence , Institut Pasteur , Paris , France ) . β2m-deficient C57BL/6 mice , B6 . 129P2-B2mtm1Unc/J ( Jackson Laboratory ) , were a kind gift from Dr Matthew Albert ( Dendritic Cell Immunobiology , Institut Pasteur , Paris , France ) . The IL-12Rβ2-deficient and IL-12p35/p40-deficient C57BL/6 mice ( both from Jackson Laboratory ) were kindly provided by Dr Selina Keppler ( University Hospital Freiburg , Institute of Medical Microbiology and Hygiene , Freiburg ) . The MyD88-deficient mice obtained from the laboratory of Shizuo Akira were backcrossed eight times to the C57BL/6 background [55] and bred in the central animal facility of the Pasteur Institute . The animals were housed in the animal facilities of the Institut Pasteur licensed by the French Ministry of Agriculture and complying with the European regulations . The protocols were approved by the safety committee at the Institut Pasteur according to the standard procedures recommended by the animal care and use committee at the Institut Pasteur . Single spleen cell suspensions were prepared by mechanical disruption on a cell strainer ( 70 µm pore diameter , BD Biosciences , Bedford , USA ) in Dulbecco's D-PBS ( Invitrogen ) . Red blood cells were lysed using Hemolytic Gey's Solution as previously described [10] . NK cells were purified using the EasySep Mouse panNK ( CD49b ) Positive Selection Kit ( StemCell Technologies , Vancouver , British Columbia , Canada ) according to the manufacturer's protocol . Flow cytometry analysis showed the purity of NK cells to be more than 90% . Further purification ( >98% ) was performed using a MoFlo cell sorter ( Beckman-Coulter ) . Bone marrow-derived macrophages ( BMDMs ) were obtained from the femur of mice after differentiation for 8 to 10 days in RPMI complete medium supplemented with 20 ng/ml M-CSF ( PeproTech , Levallois-Perret , France ) on bacterial petri dishes . BMDMs were mature as assessed by expression of the F4/80 surface marker ( consistently over 90% F4/80 positive ) . Cell enumeration and viability ( >90% ) was routinely assessed by acridine orange/propidium iodide staining . All cell cultures were carried out in RPMI 1640 medium+GlutaMAXTM I ( Invitrogen , Cergy-Pontoise , France ) supplemented with 10% fetal calf serum ( FCS; BioWest , Nuaillé , France ) , 100 µg/ml penicillin/streptomycin ( Invitrogen ) , and 50 µM 2-mercaptoethanol ( Invitrogen ) . All reactivation conditions were performed in triplicate . Cell activation was performed by incubating either 2×105 splenocytes , or 2×105 purified NK cells with 2×105 BMDM , with 2×106 spores ( or polystyrene latex beads −1 . 1 µm mean particle size- , Sigma , Aldrich ) in a final volume of 200 µl in 96-well tissue culture plates ( TPP , Trasadingen , Switzerland ) for 1 to 3 days . Direct stimulation of purified NK cells was performed by adding to the culture medium endotoxin-free mouse rIL-12 ( 10 ng/ml , BD Pharmingen ) and endotoxin-free mouse rIL-18 ( 20 ng/ml , MBL International ) for 18 h . In some experiments , NK cell activation was performed in 96-well Transwell plates ( 0 . 4 µm pore diameter; Corning Costar , NY ) ; BMDMs ( 3 . 1×105 cells in 235 µl ) mixed with FIS ( 3 . 1×106 ) were seeded into the outer chamber; CD49b+ NK cells ( 3 . 1×105 cells in 75 µl ) were added either directly to the outer chamber , or placed into the inner chamber along with or without 3 . 1×106 FIS . Cytokine and receptor function were blocked by addition of the following azide-free , low-endotoxin anti-mouse cytokine or receptor-specific mAbs: rat anti-mouse IL-12p40/p70 ( 5 µg/ml , clone C17 . 8 , BD Pharmingen ) and its isotype control ( clone R35-95 , BD Pharmingen ) , rat anti-mouse IL-18 ( 10 µg/ml , clone 93-10C , MBL International Corporation ) and its isotype control ( clone eBRG1 , eBioscience ) , goat anti-mouse IL-15Rα polyclonal ( 10 µg/ml , R&D Systems ) , armenian hamster anti-mouse NKG2D ( 10 µg/ml , clone C7 , eBioscience ) and its isotype control ( clone eBio299Arm , eBioscience ) , rat anti-mouse RAE-1 ( 5 µg/ml , clone 199205 , R&D Systems ) and its isotype control ( clone R35-95 , BD Pharmingen ) , rat anti-mouse IFN-α ( 5 µg/ml , clone RMMA-1 , PBL Biomedical Laboratories ) , rat anti-mouse IFN-β ( 5 µg/ml , clone RMMB-1 , PBL Biomedical Laboratories ) , goat anti-mouse NKp46 polyclonal ( 10 µg/ml , R&D Systems ) , rat anti-mouse IL-10 ( 10 µg/ml , clone JES5-2A5 , BD Pharmingen ) , armenian hamster anti-mouse TNF-α ( 10 µg/ml , clone TN3-19 . 12 , Sigma ) . For analysis of the effects of the toxins , splenocytes were pre-treated before stimulation , for 1 h with rPA ( 250 ng/ml ) and/or either rLF ( 0 . 01–100 ng/ml ) , -both a kind gift from Dr Bassam Hallis , HPA , Porton Down , UK- , or rEF ( 0 . 01–100 ng/ml ) -a kind gift from Pr Wei-Jen Tang , University of Chicago , Chicago , USA . Purified CD49b+ NK cells were pre-treated before stimulation for 1 h with PA ( 250 ng/ml ) and/or either LF ( 0 . 5–100 ng/ml ) or EF ( 10–100 ng/ml ) , or PA ( 2500 ng/ml ) and EF ( 500–1000 ng/ml ) . NK cell viability after toxin treatment was analyzed by using Live/Dead Cell Staining Kit ( Invitrogen ) and their metabolic activity was assessed by the CellTiter 96 AQueous One Solution Cell Proliferation Assay ( MTS ) ( Promega ) . In all cases , cell-free supernatants were removed at specific time points and frozen at −20°C before subsequent ELISA analysis for IFN-γ or IL-12 . When mentioned , conditioned supernatants from FIS-activated BMDM were collected after a 24-h stimulation , transferred on purified CD49b+ NK cells and incubated for a further 48 h . ELISA was performed as previously described [10] using the following antibody pairs and protein standards ( all from BD Pharmingen , Le-Pont-de-Claix , France ) : for IL-12p40/p70 , capture with mAb clone C15 . 6 , detection with mAb clone C17 . 8 , standard recombinant mouse IL-12 ( p70 ) ; for IFN-γ , capture with mAb clone R4-6A2 , detection with mAb clone XMG1 . 2 , standard recombinant mouse IFN-γ . Cell staining was performed with the following mAbs: anti-mouse CD69-fluorescein isothiocyanate ( FITC ) ( clone H1 . 2F3 ) ( BioLegend , San Diego , USA ) and its FITC-conjugated isotype control ( Caltag laboratories ) , CD49b- phycoerythrin ( PE ) ( clone DX5 ) ( BD Bioscience , San Diego , CA ) , IFN-γ-FITC ( clone XMG1 . 2 ) ( BD Bioscience , San Diego , CA ) and its FITC-conjugated isotype control ( BioLegend ) , F4/80-allophycocyanine ( APC ) ( clone BM8 ) ( BioLegend ) , pan-RAE-1-PE ( clone 186107 ) ( R&D Systems ) and its PE-conjugated isotype control ( Caltag Laboratories ) . To obtain a single-cell suspension , collected cells were pre-treated in 5 mM EDTA/PBS to dissociate cell aggregates . Cells were subsequently washed and blocked for 10 min with anti-CD16/CD32 mAb ( BioLegend ) in FACS buffer ( Dulbecco's PBS , 2% heat-inactivated FBS , 10 mM sodium azide ) and then labeled with the appropriate antibodies . Dead cells were excluded during acquisition through staining with LIVE/DEAD Fixable Dead Cell Stain Kit according to the manufactor's procedure ( Invitrogen ) . For intracellular cytokine staining [84] , cells were incubated with brefeldin A ( 5 µg/ml , Sigma , Aldrich ) for the last 4 h of activation with the spores . After labeling with DX5-PE mAb , fixation in 2% paraformaldehyde and permeabilisation in 0 . 5% saponine , the cells were stained with anti-IFN-γ-FITC mAb for 30 min . Cell acquisition was performed using a FACSCalibur flow cytometer ( BD Bioscience ) . NK cells were gated by their light scattering properties -forward ( FSC ) and side ( SSC ) scatter- that distinguished them from the macrophages ( Figure S1A ) . Isotype controls Abs were used for each staining combination . A minimum of 10 , 000 events was acquired for analysis . Data were acquired and analyzed using CELLQUEST software ( BD Bioscience ) . Figures were derived by free WinMDI Software ( Version 2 . 8 , Bio-Soft Net; WinMDI software [http://en . bio-soft . net/other/WinMDI . html] ) . Natural cytotoxicity assay was performed as previously described [85] . Briefly , the mouse lymphoma YAC-1 target cells were labeled with 2 . 5 µM CFSE ( Sigma , Aldrich ) at 2×106 cells/ml for 8 min at room temperature . After dilution by 1∶5 in complete medium and 2 washing steps , the CFSE-labeled target cells were resuspended in complete medium ( 4×104 cells/ml , 100 µl/tube ) and mixed with positively selected CD49b+ cells at an effector/target cell ratio ( E/T ratio ) of 50∶1 ( final volume: 200 µl ) in 5 ml Falcon round-bottom tubes ( BD Bioscience ) . When necessary , CD49b+ cells were treated for 4 h with PA ( 250 ng/ml ) and/or either LF ( 100 ng/ml ) or EF ( 100 ng/ml ) , and then washed prior to mixing . The cells were then incubated in the presence of 10 ng murine rIL-2 ( BioLegend ) in a humidified atmosphere of 5% CO2 at 37°C for 18 h . Cytotoxicity was then assessed by flow cytometry analysis after propidium iodide labeling . Negative controls were CFSE-labeled target cells without NK cells . For data analysis , the CFSE-stained target cells were gated ( R1 ) on SSC/FL1 ( CFSE ) parameters and analyzed on FL1 ( CFSE ) /FL3 ( Propidium iodide ) . For each sample 4000 events of R1 were collected . The percentage of specific target cell death was calculated as follows: [ ( dead CFSE-positive targets in the sample ( % ) −spontaneously dead CFSE-positive targets ( % ) ) / ( 100−spontaneously dead CFSE-positive targets ) ]×100 . Spleen cells from C57BL/6 and β2m−/− C57BL/6 mice were labeled with 2 µM ( C57BL/6 ) or 5 µM ( β2m−/− C57BL/6 ) CFSE , and equal cell numbers ( 106; “injected mix” ) were co-injected intravenously into C57BL/6 mice recipients , either untreated or injected intravenously 8 h prior to cell inoculation with ( i ) LF ( 7 . 5 µg ) or EF ( 7 . 5 µg ) and PA ( 20 µg ) or ( ii ) CyaA or the inactive mutant CyaE5 ( 15 µg ) of Bordetella pertussis ( a kind gift from Dr Daniel Ladant , Biochimie des Interactions Macromoléculaires , Institut Pasteur , Paris , France ) . Control mice received either EF or PA alone . In vivo NK cell depletion was performed by intravenous injection of 200 µg anti-NK1 . 1 mAb ( clone PK136 , Serotec , Oxford , United Kingdom ) . In vivo NK cell activation was performed by intravenous injection of 100 µg poly: ( IC ) ( Invivogen ) . Single spleen cell suspensions were prepared 16 h later and the CFSE positive cell population was acquired by FACS as described above . The ratio of CFSE high versus CFSE low cells was determined and specific lysis was calculated as described [86]: 100×[1− ( ratio of injected mix/recovery ratio ) ] , where ratio = % CFSE low/% CFSE high . Purified NK cells ( 106 cells ) were incubated with PA ( 250 ng/ml ) , and/or LF ( 100 ng/ml ) for 2 h at 37°C . Recombinant IL-12 ( 50 ng/ml ) and IL-18 ( 100 ng/ml ) were then added for 10 min , cells were washed twice with cold PBS and lysed on ice for 30 min with a total protein extraction buffer ( 20 mM Tris-HCl , pH 7 . 5 , 150 mM NaCl , 1 mM sodium EDTA , 1 mM EGTA , 1% Nonidet P-40 , 1% sodium deoxycholate , 2 . 5 mM sodium pyrophosphate , 1 mM β-glycerophosphate , 1 mM Na3VO4 , 1 µg/ml leupeptin ) containing Complete protease and phosphatase inhibitor tablets as specified by the manufacturer ( Roche Diagnostics ) . Total protein ( 50 µg as determined by the Bradford assay , Bio-Rad Laboratories ) was resolved on 10% SDS-PAGE gels and transferred to nitrocellulose membranes . Membranes were blocked in TBS containing 5% non-fat dried milk and 0 . 1% Tween 20 . Protein detection was performed with polyclonal Abs directed against phospho-p38 MAPK , phospho-p42/44 ( ERK 1/2 ) MAPK , phospho-JNK or total p42/44 ( ERK 1/2 ) MAPK ( all from Cell Signaling Technology ) . Total p42/44 ( ERK 1/2 ) MAPK was used as loading control as previously described [87] . Bands were visualized with appropriate secondary HRP-conjugated Abs and SuperSignal West Pico chemiluminescent substrate ( Pierce ) . When reprobed , membranes were first stripped by incubating in a stripping buffer ( Gene Bio-Application Ltd . ) . To determine the cAMP response generated by edema toxin , purified CD49b+ NK cells ( 106 cells ) were incubated with PA ( 0 . 25 µg/ml ) and EF ( 0 . 05–0 . 1 µg/ml ) , or PA ( 2 . 5 µg/ml ) and EF ( 0 . 5–1 µg/ml ) at 37°C for 1 h and then stimulated with rIL-12 ( 10 ng/ml ) and rIL-18 ( 20 ng/ml ) . After 2 h of incubation , the culture medium was discarded and the cells were lysed with 0 . 1 M HCl . After centrifugation , the supernatants were collected and immediately stored at −80°C before analysis . Intracellular cAMP concentration was determined using a commercial cAMP EIA kit ( Cayman Chemical , Ann Arbor , MI , USA ) according to the manufacturer's instructions . FVB mice were infected into the ear dermis with spores of the non-toxinogenic encapsulated 9602P ( EF+LF+ ) strain , or the 9602L ( EF+PA+ ) or 9602C ( LF+PA+ ) strains . Cutaneous infections were performed under light anesthesia by injecting 2 . 91±0 . 03 log10 spores per mouse in 10 µl of PBS into the dermis of the left ear as previously described [88] . 24 h later the cervical lymph nodes draining the infected site were excised , placed into ice-cold saline solution and mechanically dissociated to obtain single-cell suspensions . Control lymph nodes were obtained from PBS-inoculated ears . Cell labeling was performed with APC-conjugated anti-F4/80 ( BioLegend ) , anti-CD4-FITC ( BD PharMingen ) , anti-CD8-PE ( BD PharMingen ) , and PE-conjugated DX5 ( eBioscience ) . Flow cytometry acquisition was performed as described above . NK-cell depletion was performed in C57BL/6 mice by intraperitoneal injection of 200 µg of anti-NK1 . 1 antibody ( clone PK136 , Serotec , Oxford , United Kingdom ) at 2 days and 1 day before infection . Depletion of NK cells ( >95% ) was verified in the spleen by flow cytometry . Control mice received an injection of 200 µg isotype-matched antibody ( Sigma ) . Cutaneous infection in the ear was performed by injection of 3 . 05±0 . 29 log10 spores of the 9602P strain . Spleens were removed aseptically 18 h post-inoculation and homogenized in 5 ml of PBS . The bacterial load in the resulting suspensions was determined by plating 100 µl of 10-fold dilutions onto BHI agar plates and is expressed as log10 CFU per spleen . C57BL/6 mice were subcutaneously infected with 2×103 spores of the non-toxigenic encapsulated B . anthracis strain in 10 µl of PBS into the external face of the right ear , while the same volume of PBS was injected in the left ear . Prior to imaging , 100 µg of rhodamine B 10 kD-dextran ( Sigma ) were administrated by intravenous injection . Negatively selected NK cells ( Negative selection mouse NK cell enrichment kit , StemCell Technologies ) were labeled with 5 µM of CFSE and were injected intravenously ( 106 per mice ) 4 h after infection . Ketamine-xylazine anesthetized mice were placed under the microscope with their ears maintained between cover glasses . The cervical lymph nodes draining the infected and the PBS-injected control ears were harvested from the recipient mice 24 h after infection , fixed with tissue glue to the plastic chamber containing PBS , and sequentially imaged . Two-photon excitation fluorescence ( TPEF ) imaging was performed using a LSM 710 Zeiss microscope . The excitation wavelength is 854 nm , allowing epicollection of second-harmonic generated signal ( SHG ) at 427 nm quasi specific of collagen . Other epicollected signals were intravenous rhodamine B and CFSE-stained NK cells fluorescence . Image acquisition and analysis were performed by using ZEN 2008 software ( Zeiss ) . Statistical analysis was performed using Graphpad Prism software . Unless otherwise noted , results are expressed as mean values ± standard deviation . The student's t-test was used to determine significance ( P<0 . 05 ) .
|
NK cells are important immune effectors that perform a surveillance task and react to transformed , stressed , and virally infected cells . They represent a first-line defence against cancer and pathogen invasion . Different pathogens trigger distinct NK-cell activation pathways . The Bacillus anthracis spore is the highly resistant form that enters the host and provokes anthrax . This microbe kills through a combination of acute bacterial infection and devastating toxemia . In the present study , we characterise the crosstalk between NK cells and spores , as well as the strategies used by B . anthracis to evade initial control mechanisms and impact anthrax pathogenesis . Our findings exemplify the spores' property to efficiently drive a high production of IFN-γ by NK cells , as well as the complex pathways used for activation which require both cytokine and cellular signaling . B . anthracis subverts this response through its toxins by paralysing essential NK cell functions . Furthermore , edema toxin from B . anthracis blocks natural cytotoxicity without affecting IFN-γ secretion . The CyaA toxin of Bordetella pertussis possesses the same enzymatic activity and has a similar effect . The high efficiency of these toxins in blocking cytotoxicity in vivo implies possible exploitation of their subverting activity to modulate excessive cytotoxic responses in immunopathological diseases .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"immune",
"cells",
"cytokines",
"immune",
"activation",
"immunity",
"to",
"infections",
"immunology",
"microbiology",
"host-pathogen",
"interaction",
"bacterial",
"pathogens",
"biology",
"immune",
"response",
"gram",
"positive",
"immune",
"system",
"nk",
"cells",
"immunity",
"innate",
"immunity"
] |
2012
|
Mechanisms of NK Cell-Macrophage Bacillus anthracis Crosstalk: A Balance between Stimulation by Spores and Differential Disruption by Toxins
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Naïve anti-viral CD8+ T cells ( TCD8+ ) are activated by the presence of peptide-MHC Class I complexes ( pMHC-I ) on the surface of professional antigen presenting cells ( pAPC ) . Increasing the number of pMHC-I in vivo can increase the number of responding TCD8+ . Antigen can be presented directly or indirectly ( cross presentation ) from virus-infected and uninfected cells , respectively . Here we determined the relative importance of these two antigen presenting pathways in mousepox , a natural disease of the mouse caused by the poxvirus , ectromelia ( ECTV ) . We demonstrated that ECTV infected several pAPC types ( macrophages , B cells , and dendritic cells ( DC ) , including DC subsets ) , which directly presented pMHC-I to naïve TCD8+ with similar efficiencies in vitro . We also provided evidence that these same cell-types presented antigen in vivo , as they form contacts with antigen-specific TCD8+ . Importantly , the number of pMHC-I on infected pAPC ( direct presentation ) vastly outnumbered those on uninfected cells ( cross presentation ) , where presentation only occurred in a specialized subset of DC . In addition , prior maturation of DC failed to enhance antigen presentation , but markedly inhibited ECTV infection of DC . These results suggest that direct antigen presentation is the dominant pathway in mice during mousepox . In a broader context , these findings indicate that if a virus infects a pAPC then the presentation by that cell is likely to dominate over cross presentation as the most effective mode of generating large quantities of pMHC-I is on the surface of pAPC that endogenously express antigens . Recent trends in vaccine design have focused upon the introduction of exogenous antigens into the MHC Class I processing pathway ( cross presentation ) in specific pAPC populations . However , use of a pantropic viral vector that targets pAPC to express antigen endogenously likely represents a more effective vaccine strategy than the targeting of exogenous antigen to a limiting pAPC subpopulation .
In the fight against virus invasion , TCD8+ play an essential role by killing virus-infected cells . Activation of these cells by professional antigen presenting cells ( pAPC ) is a vital step in generation of an effective adaptive immune response . pAPC are a heterogeneous population comprised of B cells , macrophages and dendritic cells ( DC ) , and specialized subsets of each of those populations . Numerous studies have examined the abilities of these populations and subpopulations to present pMHC-I following virus infection or immunization [1–6] . These studies have concluded that certain pAPC populations are specialized for particular functions , leading to multiple strategies targeting particular pAPC populations in vaccine design [7] . However , the extent to which pAPC populations provide sufficient pMHC-I for maximal generation of TCD8+ depends on factors such as viral tropism for pAPC populations [8] , interference with pMHC-I processing pathways [9] , or lysis of infected pAPC populations [10] . To date , previous studies have relied upon the semi-quantitative activation of T cells , measured either by initiation of proliferation or acquisition of effector functions such as cytokine production or lytic activity . Each measure of T cell activity is quantitative only in the sense that each T cell has undergone proliferation or displayed effector activity , but these activities are affected by many other factors , including the expression of costimulatory and adhesion molecules by TCD8+ or pAPC , the cytokine milieu and/or modulation of each of these factors by virus infection or pre-activation of the pAPC by other inflammatory stimuli [11] . Here we have quantitatively examined antigen presentation following infection with a poxvirus , the natural mouse pathogen ectromelia virus ( ECTV ) , which is pantropic for all pAPC populations examined . Our system allowed us to differentiate between presentation of endogenously synthesized antigen by multiple populations of infected pAPC ( direct presentation ) and presentation of antigen acquired by uninfected pAPC populations ( cross presentation ) . We have demonstrated that presentation of endogenously synthesized antigen results in much higher pMHC-I levels than acquisition of exogenous antigen and that , on a per cell basis , each infected pAPC population produces equivalent pMHC-I levels , irrespective of activation or maturation status . These data have important ramifications for rational vaccine design in that they indicate that a vaccine in which endogenous synthesis of the targeted antigen occurs within multiple pAPC populations is the most effective way to generate the greatest number of effective pMHC-I complexes which , in turn , results in an optimal antigen specific TCD8+ response .
To quantify ECTV infection and subsequent antigen presentation , we utilized a recombinant ECTV virus that encodes a fusion protein ( NP-S-EGFP ) consisting of the influenza nucleoprotein ( NP ) , an enhanced green fluorescent protein ( EGFP ) , and ovalbumin ( OVA ) residues 257–264 ( SIINFEKL ) [12] . This system allows us to identify ECTV-infected and uninfected cells based on the presence and absence of EGFP expression . Following injection with NP-S-EGFP i . d . , draining lymph nodes ( D-LN ) were harvested at 12 h . p . i . from naïve or ECTV-infected mice . A distinct EGFP+ cell population was observed ( Fig 1A ) . ECTV-infected cells were resident in the periphery of the D-LN , just below the sub-capsular sinus by 6 h . p . i ( Fig 1B ) . To assess whether the EGFP+ cells were infected by ECTV and were not uninfected cells that had engulfed dead or dying EGFP+ cells or EGFP+ cellular material , we conducted the following experiment . Splenocytes from C57BL/6 . SJL ( CD45 . 1+ ) mice were infected in vitro with ECTV NP-S-EGFP or wild type ( wt ) ECTV to allow expression of viral antigen and then treated with psoralen and UV-C-crosslinking to abolish further virus replication [13] ( S1A Fig ) . The infected and psoralen/UV treated cells were injected i . v into C57BL/6 ( CD45 . 2+ ) mice , and spleens subsequently assessed for the presence of recipient-derived EGFP+ cells . As a positive control , mice were directly infected i . v with a dose of NP-S-EGFP that was 30-fold lower than the number of infected splenocytes injected . We found EGFP+ cells in mice directly infected with ECTV NP-S-EGFP but not in naïve mice or mice immunized with either WT ECTV or a large excess of NP-S-EGFP-infected cells ( Fig 1C ) . Notably , infection of cells by ECTV in vivo was dependent on virus replication ( S1A Fig ) . These results demonstrate that EGFP+ cells resulted from ECTV infection , and not from internalization of EGFP+ material by uninfected cells . We isolated cells from the D-LN of mice infected with ECTV NP-S-EGFP or NP-EGFP ( which lacks the OVA257-264 SIINFEKL determinant ) 12 h . p . i . and stained with an antibody specific for Kb-SIINFEKL complexes [14] . Cells from mice inoculated with ECTV NP-EGFP did not show staining above background . Infected cells from ECTV NP-S-EGFP-infected mice expressed measurable levels of Kb-SIINFEKL complexes ( Fig 1D ) but none of the uninfected GFP- cells from mice infected with ECTV NP-S-EGFP displayed antibody staining ( Fig 1D ) . To ensure that antigen presentation in infected cells occurred via the conventional endogenous processing pathway , we measured antigen presentation following infection of mice lacking TAP1 , a vital component of this pathway . Mice lacking TAP1 did not display staining for Kb-SIINFEKL complexes above background levels ( Fig 1E ) . Collectively , these results indicate that this infection allows differentiation between virus-infected and uninfected cells in vivo and accurate quantification of specific peptide-MHC complexes on infected cells . To examine the pAPC ( DC , B cells and macrophages ) infected by ECTV , we injected vehicle , NP-EGFP , or NP-S-EGFP i . d . , and harvested D-LN at 24 h . p . i . We stained with a panel of antibodies to identify DC ( CD11c+ CD169- CD19- ) , B cells ( CD19+ CD11c- CD169- B220+ ) , and macrophages ( CD11b+ CD11c- CD19- CD169+ ) ( S1B Fig ) . A kinetic analysis indicated that CD169+ macrophages were the first pAPC to be infected , while CD19+ B cells and CD11c+ DC were infected by 12 h . p . i . ( S2A Fig ) . Therefore , at 24 h . p . i all major populations of pAPC were infected ( S2A Fig ) , allowing us to compare the efficiency of antigen presentation by each pAPC population . We compared the fluorescence produced from antigen-conjugated GFP in each pAPC population ( Fig 2B ) . B cells and macrophages expressed equivalent levels of antigen , but DC expressed significantly more ECTV-encoded antigen on a per cell basis ( Fig 2C , top panel ) . As above , we found that only infected pAPC stained for Kb-SIINFEKL . Staining of uninfected B cells , macrophages and DC was indistinguishable from cells isolated from mice infected with control ECTV-NP-GFP . We found higher levels of Kb-SIINFEKL complexes on the surface of DC than on the surface of B cells , and each was significantly higher than the levels observed on the surface of macrophages ( Fig 2C middle panel ) . The levels of Kb-SIINFEKL complexes increased with time after infection with NP-S-EGFP ( S2B Fig ) . Because DC express more ECTV antigen than B cells or macrophages ( Fig 2C , top panel ) we sought to ascertain the efficiency of antigen presentation in each pAPC population by equalizing for protein expression . Therefore , we calculated the efficiency of direct presentation as a ratio of Kb-SIINFEKL complexes per EGFP protein , which represents the formation of pMHC-I complexes as a function of the levels of the protein antigen from which the complexes were derived . DC and B cells were equally efficient at producing Kb-SIINFEKL complexes while macrophages were significantly less efficient ( Fig 2C , bottom panel ) . Although Kb-SIINFEKL complexes were only detected on the surface of infected pAPC populations , levels below the threshold of detection with the 25 . D1 . 16 antibody might still be capable of TCD8+ stimulation [14] . Therefore , we analyzed the ability of ECTV-infected and uninfected pAPC populations to activate naïve SIINFEKL-specific OT-I TCD8+ [15] . Mice were injected in the footpads with either NP-EGFP or NP-S-EGFP , and EGFP+ or uninfected EGFP- B cells , DC and macrophages were sorted from D-LN cell suspensions . Each cell population was co-cultured separately with naïve OT-I TCD8+ and TCD8+ proliferation was determined at 60 h post-culture . None of the pAPC populations purified from mice infected with control NP-EGFP , activated OT-I TCD8 above background ( Fig 2D ) . Only NP-S-EGFP-infected B cells and macrophages robustly activated naive OT-I TCD8+ , whereas uninfected B cells and macrophages did not stimulate naive OT-I TCD8+ ( Fig 2D ) . Notably , both ECTV-infected and uninfected DC were capable of activating naïve OT-I TCD8+ ( Fig 2D bottom panel ) . Thus , although Kb-SIINFEKL complexes were undetectable with antibody staining on EGFP- DC ( Fig 1D and 2B ) , these uninfected DC appear specialized ( compared to B cells and macrophages ) to express sufficient Kb-SIINFEKL complexes to stimulate the high affinity TCR on OT-I TCD8+ . Although we demonstrated antigen presentation by all infected pAPC populations , it was not clear whether all infected pAPC populations are located at sites at which naïve TCD8+ are activated . Therefore , we visualized the interaction of labeled naïve OT-I TCD8+ with virus-infected pAPC expressing cognate antigen . Recipient mice were injected with either NP-EGFP or NP-S-EGFP i . d . , and at 12 h . p . i ( Figs 3A and 3B ) or 24 h . p . i . ( Figs 3C–3J ) , D-LN were harvested for microscopic analysis . ECTV-infected cells were predominantly located at the periphery of the D-LN just below the sub-capsular sinus at early time points , with a few cells observable in the cortical region ( Figs 3A and 3B ) , as we [16] and others [17] have previously described following infection with the related poxvirus vaccinia virus ( VACV ) . However , in contrast to short–lived VACV infection , where the number of GFP+ cells is reduced over time [16] , following ECTV infection , EGFP+ cells were visualized 300 m from the periphery at 24 h . p . i , and the number of infected cells had increased significantly ( Figs 3C , 3E , 3G and 3I ) , mirroring our flow cytometry analyses ( S2A Fig ) . Notably , in D-LN infected with SIINFEKL-expressing virus ( NP-S-EGFP ) , the OT-I TCD8+ relocated into the peripheral regions of the D-LN ( Fig 3A ) , presumably , to interact with virus-infected cells . However , in D-LN infected with NP-EGFP ( Fig 3B ) , OT-I TCD8+ were restricted to the T cell zone . To determine the interaction of individual pAPC populations with naïve TCD8+ , cryosections were stained with anti-B220 ( B cells ) , anti-CD169 ( macrophages ) , anti-CD11c ( DC ) , or anti-CD103 ( migrating DC ) antibodies and visualized by fluorescence microscopy . As expected , we primarily observed B220+ cells in the B cell follicle region ( Fig 3C ) , CD169+ in the sub-capsular region ( Fig 3E ) , and CD11c+ or CD103+ cells in the cortical region of the D-LN ( Figs 3G and 3I ) . To visualize direct interaction between OT-I TCD8+ and ECTV-infected pAPC , we acquired and analyzed 3-dimensional high power images . When analyzing the images produced we considered that there would not be direct co-localization of cell surface stain with GFP , which is localized within the nucleus as it is attached to NP . In D-LN from mice infected with NP-S-EGFP , we visualized OT-I TCD8+ interacting with EGFP+CD169+ macrophages ( Fig 3F ) , EGFP+CD11c+ DC ( Fig 3H ) , EGFP+CD103+ DC ( Fig 3J ) and , surprisingly , EGFP+B220+ B cells ( Fig 3D ) within 24 h of infection . Therefore , the antigen presentation that we measured in vitro by each pAPC population in Fig 2 has the potential in vivo to induce the activation of naïve TCD8+ . DC are composed of different subpopulations that are proposed to be specialized to perform differing tasks during antigen presentation [5] . Several studies have reported a role for individual DC subsets in MHC class I mediated TCD8+ activation [1–3 , 5 , 6] . However , during a virus infection it is vital to account for viral tropism for individual DC subsets versus functional specialization of DC presenting viral antigen . We focused on the three major DC subsets in lymph node and spleen characterized as: CD8α+ CD11b- B220- ( hereafter CD8α+ DC ) , CD11b+ CD8α - B220- ( hereafter CD11b+ DC ) , and plasmacytoid B220+ CD11b- ( hereafter pDC ) . To determine whether there is specialization in MHC class I presentation by infected DC subsets , mice were injected with NP-EGFP or NP-S-EGFP i . d . , and D-LN were harvested at 24 h . p . i . Cells were stained to identify DC subsets and analyzed by flow cytometry . As NK cells , T cells and B cells share some DC markers and may alter antigen presentation [6] we stained with antibodies to identify NK cells , T cells and B cells , to exclude these lymphoid populations from our analysis . We found that GFP+ cells contained all DC subsets ( Fig 4A ) . We did not observe staining for Kb-SIINFEKL complexes on any uninfected cell population . The number of Kb-SIINFEKL complexes on the surface ( Fig 4B ) and efficiency with which these Kb-SIINFEKL complexes were generated from GFP-tagged antigen ( Fig 4C ) were , surprisingly , equivalent in each DC subset ( Figs 4B and 4C ) . This suggests that all DC subsets are equally capable of presenting endogenous antigen when infected . Because uninfected DC stimulated TCD8+ ( Fig 2D bottom panel ) we asked whether specific uninfected DC subsets were specialized to present antigen . We compared the ability of uninfected and ECTV-infected DC subsets to activate naive OT-I TCD8+ following a footpad injection with NP-S-EGFP . Twenty-four h . p . i . , D-LN cells were FACS-sorted for EGFP+ and EGFP- DC subsets . Isolated DC sub-populations were co-cultured with naïve OT-I TCD8+ , and 60 h later TCD8+ proliferation was determined . Infection with NP-EGFP did not induce proliferation of OT-I TCD8+ ( Fig 4D ) . Infected CD11b+ DC and pDC from mice infected with ECTV-NP-S-EGFP were highly efficient in stimulating naïve OT-I TCD8+ , but uninfected CD11b+ DC and pDC did not significantly prime TCD8+ ( Fig 4D ) . However , both ECTV-infected and uninfected CD8α + DC activated OT-I TCD8+ ( Fig 4D ) , indicating that the TCD8+ activation by uninfected DC in Fig 2D was mediated by cross presentation by CD8α + DC . The inflammatory milieu and expression of costimulatory molecules can also affect the efficiency of TCD8+ stimulation . Therefore , the inability of EGFP- CD11b+ DC and pDC isolated from NP-S-EGFP-infected D-LN to prime naïve OT-I TCD8+ could be attributed to the lack of or lower expression of co-stimulatory molecules , such as CD80 ( B7 . 1 ) and CD86 ( B7 . 2 ) , compared to EGFP+ DC . However , there was no significant difference in expression of CD86 between ECTV-infected and uninfected DC in any of the subsets examined and only minor changes in CD80 expression in the CD11b+ population ( Fig 4E ) . Therefore , ECTV infection of DC does not inhibit maturation and changes in costimulatory molecule expression induced by infection are unlikely to account for the differential ability of uninfected DC subsets to present antigen . Maturation of DC has been reported to enhance antigen presentation , and systemic in vivo activation of DC by TLR agonists such as LPS , CpG-B , and Poly I:C is reported to block cross presentation of viral antigen by uninfected cells [18] . However , TLR ligation inhibited influenza virus infection of DC in vitro [19] and markedly reduced in vivo viral loads following infection with the poxvirus VACV [20] , potentially reducing antigen presentation by infected cells . We asked whether TLR ligation and maturation of pAPC altered infection , antigen production or presentation . As expected , TLR treatment stimulated maturation of DC , following 12 hr CpG-B ( not shown ) or LPS treatment in vivo , as assessed by upregulation of MHC class II ( I-Ab ) , CD40 , CD80 and CD86 ( Fig 5A ) . This 12 hr pre-treatment with TLR ligands also inhibited proliferation of adoptively transferred CFDA-SE labeled OT-I TCD8+ following immunization with presentation incompetent β2m-/- cells that were infected in vitro for 6 hours with either NP-EGFP , NP-S-EGFP or were left uninfected . As β2m-/- cells lack MHC class I and therefore cannot present antigen , this indicates that TCD8+ priming in this system via cross-presentation is inhibited by systemic TLR ligation ( Fig 5B ) . In contrast , the majority of OT-I TCD8+ in untreated mice that received β2m-/- cells infected with NP-S-EGFP proliferated ( Fig 5B ) . Therefore , presentation of ECTV-derived antigen by uninfected pAPC was inhibited by TLR agonist treatment in vivo . We next assessed whether TLR agonists affected ECTV infection of pAPC or direct antigen presentation by infected pAPC . Mice were injected with CpG-B , Poly I:C , or LPS , and then infected with either NP-EGFP or NP-S-EGFP . Presentation of antigen by infected pAPC was quantified 12 h . p . i . by flow cytometry . In vivo treatment with TLR agonists resulted in an approximate 70% reduction in the numbers of ECTV-infected DC ( Fig 5C ) and other pAPC ( not shown ) , indicating that DC maturation dramatically reduces virus infection . This inhibition of ECTV infection of DC extended across all the sub-populations examined ( Fig 5C , top panels ) , but GFP fluorescence in the infected population was not altered by TLR ligation ( not shown ) . Examination of antigen presentation by the remaining 30% of infected DC revealed that infected mature DC were able to directly present antigen with the same efficiency as DC that were not exposed to TLR agonists ( Fig 5C , lower panels ) , suggesting that DC maturation did not enhance direct presentation in vivo . To reconcile our findings with those describing a role for DC maturation in enhanced antigen presentation [21 , 22] , and no effect of TLR ligation upon virus infection in vitro [18] , we isolated DC from mice , treated with LPS or CpG-B for 12 h , and infected with NP-S-EGFP . TLR ligation prior to virus infection did not inhibit ECTV infectivity of DC or DC subsets in vitro ( Fig 5D , top panel ) , regardless of MOI ( Fig 5E , top panel ) . TLR ligation also did not enhance direct antigen presentation ( Fig 5D , bottom panel ) even when DC were infected at various MOI ( Fig 5E , bottom panel ) . However , at the highest MOI , overall direct presentation was significantly lower , presumably due to ECTV-induced cell death ( Fig 5E , bottom panel ) . Our data above indicate that during ECTV infection only CD8α + DC can present antigen when uninfected . To test the importance of this pathway for induction of antigen-specific TCD8+ we infected wild-type or Batf3-/- mice with NP-S-EGFP . Batf3-/- mice lack CD8α + DC and have a significant defect in cross presentation [23] . At 2 d . p . i . no proliferation of adoptively transferred OT-1 TCD8+ was observed in the spleen following infection with either NP-SEGFP or control NP-EGFP ( not shown ) . We did observe proliferation of OT-1 in the D-LN after infection with NP-S-EGFP ( Fig 6A ) , but the proliferation observed was equivalent in wild-type and Batf3-/- mice , indicating that CD8α + DC are dispensable for initiation of an OVA-specific TCD8+ response . To extend our observation beyond an OVA-specific response and beyond the use of the highly sensitive OT-1 TCR TCD8+ we examined the functional activation of TCD8+ specific for native ECTV encoded epitopes within the B8R , M1L , A3L , A8R , and E7R viral proteins . Seven days after infection , the frequency ( Fig 6B ) and numbers ( Fig 6C ) of TCD8+ producing IFN-γ in response to the B8R and A8R epitopes were equivalent in wild-type and Batf3-/- mice . However , responses to the M1L , A3L , and E7R epitopes were reduced in Batf3-/- mice ( Figs 6B and6C ) , indicating that presentation by CD8α + DC may be required for maximal presentation of some determinants .
Vaccines aimed at inducing protective TCD8+ responses have the promise of targeting invariant intracellular proteins that can be used to clear the pathogens encoding the antigens when antibody responses are ineffective . Recent studies have indicated that pAPC , and particularly DC , subpopulations are specialized to induce T cell responses via different antigen presentation pathways . Recent vaccine strategies have specifically targeted exogenous antigen to particular DC populations , often along with ligands known to induce DC maturation , in an attempt to increase the efficacy of TCD8+ priming [7] . However , our work reveals that for vaccines aimed at inducing protective TCD8+ , targeting only individual pAPC populations , particularly with exogenous antigens , may drastically reduce the presentation of peptide-MHC complexes in vivo , irrespective of DC maturation . In particular , our results indicate that the number of peptide MHC complexes generated from endogenous sources dramatically outnumbers those produced from exogenous sources . Indeed , peptide-MHC complexes produced from exogenous sources were below the level of detection using our specific antibody ( >100 complexes per cell [14] ) even when mice were immunized with 3 x 107 infected cells expressing large quantities of viral protein . Therefore it is clear that if a virus infects a pAPC more peptide-MHC complexes are likely to be produced than if these cells remain uninfected , even if targeted exogenously . This finding may have been hidden by the experimental use of mismatched human virus/murine target combinations where virus tropism is diverted away from pAPC , which are often a Trojan Horse when infected that allow transmission of numerous viruses . The use of the ECTV system reveals that during a fulminant natural infection , direct presentation likely predominates during induction of protective TCD8+ . Increasing the number of pMHC-I on the surface of an APC in vitro causes activation induced cell death and allows survival of only low affinity TCD8+ [24] . In contrast , increasing the number of pMHC-I in vivo can increase the number of TCD8+ primed [25 , 26] up to a certain point [27] , and does not reduce the affinity of the responding TCD8+ . Therefore , a vaccine vector that produces a larger number of cell surface pMHC-I will produce more effective TCD8+ . The TCD8+ response to the poxvirus VACV is initiated following antigen presentation by infected APC [16 , 20 , 28] . Here we demonstrate that the number of pMHC-I presented by infected pAPC vastly outnumbers the number of complexes presented by uninfected pAPC , even when the antigen is readily available for presentation by both infected and uninfected cells . Therefore , our findings show that the most efficient way to induce a strong TCD8+ response is to utilize a vaccine in which endogenous expression of antigen within pAPC is optimized . Here we found that uninfected CD8α+ DC were able to present exogenously derived viral antigen . Previous studies have implicated CD8 α+ DC in the presentation of all viral antigen [1] , but these studies may reflect preferential infection of certain DC subpopulations by viruses [29] , or exclusive presentation of exogenous antigen as pAPC are not infected [2 , 3] . In addition , it has been proposed that some pAPC populations are specialized to present peptides on MHC Class I while other populations are specialized to present on MHC Class II [5] . Support for this hypothesis comes from gene array analysis describing a paucity of expression of components of the MHC Class I processing pathway in DC populations that did not present exogenous antigen [5] . Importantly , these studies only examined presentation of exogenous antigen . Virtually all nucleated cells express both MHC Class I and the machinery required to present peptide-MHC complexes derived from endogenous antigens . Specialization of pAPC populations to avoid such presentation would furnish viruses and intracellular bacteria with a location in which they could replicate with relative indifference to the action of the adaptive immune system . Therefore , it is logical that all infected pAPC will present pMHC-I derived from endogenous antigens , and this is indeed what we observe . We examined the relative efficiency of presentation of endogenous antigens to reveal that DC do not appear to be more efficient at presenting endogenous antigens than B cells , although both appear to be better than macrophages ( Fig 2C ) . There is no specialization within DC subpopulations , a pronounced difference from the presentation of exogenous antigens , which CD8α+ DCs are substantially superior at presenting [2] . This lack of specialization by DC populations is at odds with the gene array data indicating differential expression of MHC Class I processing machinery [5 , 30] . However , the supply of antigenic peptide , rather than the expression of any processing components , is limiting in MHC Class I presentation [31] . Therefore , the rate of antigen production and degradation controls the efficiency and amplitude of antigen presentation in infected cells . In the system examined here , DC ( of all subsets ) produce more fluorescent antigen than other pAPC , and so present a higher number of peptide-MHC complexes per cell . Peptides are generated from endogenous short-lived proteins , termed Defective Ribosomal Products ( DRiP ) or Rapidly Degraded Proteins ( RDPs ) [22 , 32] much more efficiently than from long-lived proteins , which are the substrates for cross presentation [33] . DRiP/RDP are unlikely to be correctly folded and therefore may not be fluorescent in our system . Our calculations of the relative efficiency of antigen presentation are made with the assumption that the proportion of newly synthesized protein within the RDP fraction is equal between pAPC populations . There are no publications that indicate the contrary . DC that were ECTV-infected following TLR agonist treatment directly presented antigen at equivalent levels to untreated DC , demonstrating that DC maturation does not enhance antigen presentation and so likely does not affect the supply of antigenic peptide . Systemic TLR ligation did block cross presentation , as previously published [18] , but it also reduced ECTV infection by around 70% demonstrating that , as with VACV infection , TLR ligation fails to differentiate between antigen presentation by infected and uninfected pAPC [20] . Pre-treatment of DC with TLR ligands rendered DC resistant to influenza virus infection in vitro [19] . However , we did not observe a decrease in virus infectivity when DC were treated with TLR agonists in vitro , regardless of MOI . It is possible that this may reflect an overall reduction in DC infectability that is a byproduct of the DC isolation procedure upon infectability with ECTV , but this is unavoidable . Nonetheless , we did not observe an inhibition of infection by TLR treatment in vitro . Thus , systemic TLR ligation may reduce the infectability of pAPC populations via an indirect mechanism , such as the relocalization of DC populations , alteration in virus drainage to reduce cellular exposure to virus , or inhibition of virus replication through induction of innate antiviral pathways . Using current methodology , it has not been possible to differentiate between infection of DC in the periphery or in the D-LN . However , at early time points following ECTV infection i . d , the ECTV-infected cells in the D-LN were found predominantly below the sub-capsular sinus , and phenotypic analysis showed that these infected cells were CD169+ macrophages . Infection of macrophages found within or below the sub-capsular sinus has been previously reported with VACV and vesicular stomatitis virus infection [16 , 17 , 34] . Our kinetic studies of ECTV infection revealed that macrophages were probably the first pAPC to be infected by 6 h . p . i . , while B cells and DC were infected by 12 hours post-ECTV infection ( S2 Fig ) . These findings suggest that virus drained from the site of infection into the D-LN and subsequently infected DC , although we cannot exclude the possibility that ECTV-infected DC migrated from the site of infection into the D-LN at later time points [35] . Although it was expected that only certain infected pAPC populations interact with naïve TCD8+ we readily identified naïve TCD8+ interacting with all of the pAPC populations that are presenting antigen . The interaction of macrophages and DC with TCD8+ during a poxvirus infection has been previously described [16 , 17] . Previous reports also showed that recently triggered antigen-specific TCD8+ relocated to the peripheral regions in an area termed the “peripheral inter-follicular region” [17] . This region was just below the LN sub-capsular sinus , and TCD8+ were shown to interact with DC found in this macrophage-rich region of the LN . Interaction with infected macrophages may induce an intermediate activation phenotype [17] . The rapid decline in GFP+ cells following VACV infection indicates that this non-native virus infection rapidly kills the cells that it infects and inefficiently infects other cells in the D-LN , which contributes to our inability to purify significant numbers of VACV-infected cells [16 , 36] . All ECTV-infected pAPC populations ( including infected B cells ) purified from infected mice were able to trigger in vitro proliferation of naïve TCD8+ , and interact with naïve TCD8+ in vivo . The interaction of infected B cells and naïve TCD8+ observed is surprising , the separation between the T cell zone and the B cell follicle within secondary lymphoid organs is carefully regulated by tightly controlled chemokine gradients . However , poxviruses , including ECTV , encode chemokine-binding proteins [37] that likely alter the balance of local chemokines in infected LN . Such an alteration in local chemokine gradient could allow interaction of TCD8+ with infected B cells . Notably , very few TCD8+ were visualized in the B cell follicles but were mainly distributed in the cortical region and marginal zones of the LN . This suggests that ECTV-infected B cells may have migrated to the inter-follicular regions where they interacted with antigen-specific TCD8+ . Our ongoing efforts seek to understand the impact of ECTV-mediated changes in local chemokine gradients on the role of B cells in induction of ECTV-specific TCD8+ and TCD4+ . Overall , our results are of importance for both vaccine design and to appreciate the basic mechanisms responsible for induction of a TCD8+ response to a fulminant widespread virus infection . In a vaccine the most effective way to induce large numbers of antigen-specific TCD8+ appears to be expression of antigen endogenously within pAPC populations , as the number of peptide-MHC complexes generated from endogenous antigens far exceeds those produced from exogenous sources . Specific DC populations did not display enhanced presentation capabilities , and prior induction of a TCD8+ response did not enhance antigen presentation on a cellular level . Our data indicate that a viral vector that effectively infects multiple pAPC populations and induces an inflammatory state via expression of natural pattern recognition receptor ligands may induce an optimal protective TCD8+ response . In terms of the basic mechanisms responsible for induction of a TCD8+ response it appears that a widespread natural infection may primarily use direct presentation by infected pAPC to prime naïve TCD8+ . The predominance of the use of cross presentation in the literature may be a byproduct of the study of human viruses in the mouse or of viruses that specifically avoid infection , even if unproductive , of pAPC populations .
C57BL/6 mice were purchased from Charles River Laboratories . Beta 2-microglobulin ( β2m-/- ) [38] , OT-I [15] , TAP1-/- [39] were from Jackson and were bred and housed in the specific-pathogen-free animal facility at the Hershey Medical Center . The Penn State College of Medicine Institutional Animal Care and Use Committee approved all studies . Recombinant ECTV ( Moscow strain ) encoded a fusion protein consisting of the influenza virus A/NT60 nucleoprotein ( NP ) affixed to the NH2-terminus of enhanced green fluorescent protein ( EGFP ) [12] . Ovalbumin ( OVA ) residues 257–264 ( SIINFEKL ) were inserted between the NP and EGFP to produce NP-S-EGFP . A control virus that lacks SIINFEKL peptide is denoted as NP-EGFP . Replication of each recombinant virus in vitro and in vivo is similar to wild-type ECTV . Mice were immunized with 106 plaque-forming units ( PFU ) of rECTV intravenously ( i . v . ) , intraperitoneally ( i . p . ) , intradermally ( i . d ) in the ear pinnae , or footpad injection . For in vitro studies , cells were infected with ECTV at a multiplicity of infection ( MOI ) of 0 . 1 , 1 or 10 , depending on the experiment . In vivo , mice were injected i . v . , and in vitro , splenocytes were treated with 15 μg/ml of Escherichia coli 055:B5 lipopolysaccharide ( LPS ) ( Sigma-Aldrich ) , 20 μg/ml of CpG-B oligonucleotides 1826 ( Invivogen ) and 20 μg/ml of Polyinosinic:polycytidylic acid ( Poly I:C ) ( Sigma-Aldrich ) dissolved in phosphate buffered saline ( PBS ) . Spleens and lymph nodes were harvested from OT-I . SJL mice and cells incubated with anti-CD8α beads , and TCD8+ were positively selected using an AutoMACS sorter ( Miltenyi Biotech ) . To assess TCD8+ proliferation , Carboxyfluorescein diacetate , succinimidyl ester ( CFDA-SE ) ( Invitrogen ) labeled OTI . SJL TCD8α+ cells were adoptively transferred into mice on day minus 3 by i . v . injection into the tail vein . On day 3 , TCD8+ cell proliferation was determined by dilution of CFDA-SE fluorescence using flow cytometry . For visualization , TCD8+ were labeled with 5 μM CellTracker Orange CMTMR ( 5- ( and-6 ) - ( 4-chloromethyl ) benzoyl ) amino ) tetramethylrhodamine ( Invitrogen ) and adoptively transferred into mice . Twenty four hours later , the mice were infected with rECTV , and the draining lymph nodes ( D-LN ) were harvested and frozen . Cryostat sections ( 30 μm ) were cut and fixed in 4% paraformaldehyde . Cryostat sections were incubated with Fab donkey anti-mouse IgG ( Jackson ImmunoResearch ) then stained with directly labeled APC-conjugated anti-CD11c ( N418 ) ( eBiosciences ) or Alexa-647 conjugated anti-B220/CD45R ( RA3-6B2 ) ( eBiosciences ) antibodies . Staining with the unlabeled primary antibodies anti-CD103 ( BioLegend ) or anti-CD169 ( 3D6 . 112 ) ( Serotec ) was revealed by staining with Cy-5 conjugated F ( ab ) 2 donkey anti-rat IgG ( Jackson ImmunoResearch ) . Staining was visualized using an Olympus 1X81 deconvolution microscope and Slidebook 5 . 0 digital microscope . Antibodies to the following molecules were purchased from eBioscience unless otherwise stated: MHC class II ( I-Ab ) ( 25-9-17 ) , CD11c ( N418 ) , CD45 . 1 ( A20 ) , CD80 ( 16-10A1 ) , CD45R/B220 ( RA3-6B2 ) , CD19 ( ID3 ) , NK1 . 1 ( PK136 ) , CD90 . 2 ( 53-2 . 1 ) , CD11b ( M1/70 ) , CD8α ( 53-6 . 7 ) , Streptavidin , CD86 ( GL1 ) ( BD Biosciences ) , CD40 ( 3/23 ) ( BD Biosciences ) , CD169 ( 3D6 . 112 ) ( Serotec ) , and 25-D1 . 16 ( grown , purified and labeled in house ) . β2m-/- , STBKM-1 fibroblast cells or C57BL/6 . SJL lymphoid cells were infected with ECTV at an MOI = 10 for 6 hours , then treated with psoralen and UV-C light ( 254 nm ) for 1 hour , as previously described [13] . The mice were then administered LPS i . v . on day 0 , then 12 hours later injected i . p . with UV-treated/gamma-irradiated β2m-/- cells that were infected with NP-EGFP or NP-S-EGFP . Using a MoFlo XDP cell sorter , popliteal lymph node cells were sorted for EGFP+ or EGFP- pAPC: Macrophages ( CD11c-CD19-B220-CD11b+CD169+ ) , B cells ( CD11c-CD11b-CD169-CD19+B220+ ) , DC ( CD19-NK1 . 1-CD90-CD11c+ ) , and DC subsets ( CD8α+B220-CD11b- , CD11b+CD8α-B220- , B220+CD11b-CD8α- ) . Cells were co-cultured with OTI . SJL TCD8+ at 1:8 DC:T cell ratio for 60 hours , then proliferation of OTI . SJL TCD8+ measured by flow cytometry . To prevent T cell infection by ECTV 50 μM Vistide/Cidofovir ( Gilead ) was added . Spleens were harvested from B6 and Batf3-/- mice at 7 days post infection ( d . p . i . ) with ECTV , and cells stimulated for 5 hrs with 1 μg/mL of ECTV-specific peptide ( B8R20-27 ( TSYKFESV ) , M1L424-438 ( KSIIIPFIAYFVLMH ) , A3L270-277 ( KSYNYMLL ) , A8R189-196 ( ITYRFYLI ) and E7R130-137 ( STLNFNNL ) ) or no peptide in the presence of 10 μg/mL of brefeldin A . After stimulation , cells were washed , fixed in 2% paraformaldehyde and permeabilized prior to staining intracellularly for IFN-γ . Net frequencies and numbers of epitope-specific TCD8+ were calculated by subtracting the no peptide background response .
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To induce a protective cell type ( CD8+ T cells ) following virus infection , it is necessary to present degraded fragments of viral protein in complex with self molecules on the surface of so-called antigen presenting cells ( APC ) . This process can occur in infected or uninfected APC and has been studied and quantified extensively in experimental setups in the lab . However , the extent to which presentation by infected or uninfected cells contribute to the induction of a protective CD8+ T cell response has not been studied extensively during a natural infection in a mouse model . Here we use a natural mouse virus to examine importantly , quantify , the contribution of presentation of the fragments of viral protein by infected or uninfected cells . We find that the presentation by infected cells dwarfs that seen by uninfected cells . The importance of this work lies in the fact that , if infected cells present way more antigen than uninfected cells , successful vaccine design should utilize this observation to make a vaccine where infected cells expressing virus proteins are the prevalent mode of induction of CD8+ T cells .
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[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
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Peptide-MHC-I from Endogenous Antigen Outnumber Those from Exogenous Antigen, Irrespective of APC Phenotype or Activation
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G protein–coupled receptors ( GPCRs ) play an important role in physiology and disease and represent the most productive drug targets . Orphan GPCRs , with their endogenous ligands unknown , were considered a source of drug targets and consequently attract great interest to identify their endogenous cognate ligands for deorphanization . However , a contrary view to the ubiquitous existence of endogenous ligands for every GPCR is that there might be a significant overlooked fraction of orphan GPCRs that function constitutively in a ligand-independent manner only . Here , we investigated the evolution of the bombesin receptor–ligand family in vertebrates in which one member—bombesin receptor subtype-3 ( BRS3 ) —is a potential orphan GPCR . With analysis of 17 vertebrate BRS3 structures and 10 vertebrate BRS3 functional data , our results demonstrated that nonplacental vertebrate BRS3 still connects to the original ligands—neuromedin B ( NMB ) and gastrin-releasing peptide ( GRP ) —because of adaptive evolution , with significantly changed protein structure , especially in three altered key residues ( Q127R , P205S , and R294H ) originally involved in ligand binding/activation , whereas the placental mammalian BRS3 lost the binding affinity to NMB/GRP and constitutively activates Gs/Gq/G12 signaling in a ligand-independent manner . Moreover , the N terminus of placental mammalian BRS3 underwent positive selection , exhibiting significant structural differences compared to nonplacental vertebrate BRS3 , and this domain plays an important role in constitutive activity of placental mammalian BRS3 . In conclusion , constitutively active BRS3 is a genuinely orphan GPCR in placental mammals , including human . To our knowledge , this study identified the first example that might represent a new group of genuinely orphan GPCRs that will never be deorphanized by the discovery of a natural ligand and provided new perspectives in addition to the current ligand-driven GPCR deorphanization .
G protein–coupled receptors ( GPCRs ) , also known as seven-transmembrane ( 7TM ) receptors , represent the largest superfamily of more than 800 vertebrate transmembrane proteins , and the main characteristic feature of these proteins is that they share a common 7TM configuration [1 , 2] . GPCRs have attracted great interest owing to their numerous physiological and pathological roles in transducing extracellular signals into intracellular effector pathways through the activation of heterotrimeric G protein ( e . g . , Gs , Gi , Gq , and G12 ) by binding to a broad range of ligands , including proteins , peptides , eicosanoids , and small organic compounds [1 , 3] . Moreover , in humans , GPCRs have been proven to be the most successful class of drug targets , and 30%–50% of marketed drugs are estimated to exert their clinical effects via GPCRs [4] . Therefore , more than 140 GPCRs—named orphan GPCRs , which were considered to have endogenous ligands—are potential therapeutic targets , and they attracted a great deal of interest in the search for their endogenous ligands for deorphanization [3–8] . Bombesin ( also called BBS or BN ) is a tetradecapeptide originally isolated from the skin of the European fire-bellied toad Bombina bombina and only found in amphibians [9] . Two bombesin-like peptides ( BLPs ) , gastrin-releasing peptide ( GRP ) and neuromedin B ( NMB ) , were found to be conserved across vertebrates . The biological actions of NMB and GRP are mediated via their specific receptors , namely NMB receptor ( NMBR , BB1 ) and GRP receptor ( GRPR , BB2 ) , respectively [10] . NMBR/GRPR stimulation of intracellular calcium and extracellular signal–regulated kinase ( ERK ) require their cognate peptide ligands via Gq signaling [1 , 11] . But neither NMB nor GRP is the cognate ligand for the third bombesin receptor , named bombesin receptor subtype-3 ( BRS3 , BB3 ) , although it shows high sequence identity to GRPR ( approximately 47% ) and NMBR ( approximately 44% ) . BLPs exhibit a large degree of sequence conservation across most vertebrates [12] . Therefore , BRS3 was considered as an orphan Gq-coupled GPCR , and some studies proposed the existence of a BRS3 cognate peptide hormone , which should be a natural and endogenous BLP [11–13] . Also , many synthetic ligands were designed for BRS3 , even with high binding affinity [14] . However , thus far , the natural peptide ligand for BRS3 has not been identified , and BRS3 is still considered as an orphan GPCR [15] . Almost every GPCR is presumed to interact with endogenous cognate ligand ( s ) in our body , and therefore orphan GPCRs attract a great deal of interest in search for endogenous ligands , eventually leading to deorphanization [7 , 16] . On the other hand , some studies demonstrated that orphan GPCRs can function in a ligand-independent manner—e . g . , constitutively activating the G protein signaling—by heterodimerizing with other GPCRs and functioning as a co-receptor [17–20] . Instead of postulating a natural ligand for each GPCR , namely constitutive functions for some orphan GPCRs have not been much illuminated [21 , 22] . Here , with analysis of 17 vertebrate BRS3 structures and 10 vertebrate BRS3 functional data , we showed that BRS3 , as a classic orphan GPCR originating from an ancestor of NMBR and GRPR , still connects to its original ligands NMB and GRP in nonplacental vertebrates but not in placental mammals . However , placental mammalian BRS3 underwent positive selection , and in comparison to nonplacental vertebrate BRS3 , its protein structure is altered significantly . With three key residues ( R127Q , S205P , and H294R ) that regulate ligand binding/activation altered , placental mammalian BRS3 lost binding affinity to NMB/GRP and , because of additional changes in the N-terminal domain and G protein selectivity barcodes , constitutively activates Gs , Gq , and G12 signaling in a ligand-independent manner . Our study identified the first example , to our knowledge , that constitutively active BRS3 is a genuinely orphan GPCR in placental mammals , including humans .
To explore the origin of BRS3 , the genetic relationship among BRS3 receptors , GRPRs , and NMBRs ( the latter two having >44% sequence identity to BRS3 ) , as well as CCHamide-1 receptor ( CCHaR-1 ) and CCHamide-2 receptor ( CCHaR-2 ) , which also belong to the BRS3 phylogenetic subgroup , was included . The endothelin receptor type A ( EDNRA ) was used as an out-group , since it has low amino acid sequence identity ( above 25% ) relative to BRS3 . The corresponding vertebrate amino acid sequences ( n = 96 ) were downloaded from NCBI and Ensemble databases to reveal the evolutionary processes within the bombesin receptor family ( S1 Table ) . A consensus tree was then built using MEGA 7 . 0 . 26 ( JTT+G+I , bootstrap = 500 , cutoff for condensed tree = 20% ) ( Fig 1A ) . As shown in Fig 1A , BRS3 , GRPR , and NMBR were present in vertebrates such as fishes , amphibians , reptiles , birds , and mammals . However , NMBR-like members were also detected in a few nonvertebrate deuterostomes ( such as Hemichordata and Echinodermata ) . Nonvertebrate deuterostome receptors may belong to prevertebrate NMBR/GRPR/BRS3 members , and vertebrate receptors are just NMBR but not NMBR-like members . In contrast , CCHaR-1 and CCHaR-2 receptors were only detectable in protostomes , such as Arthropoda , Brachiopoda , and Mollusca ( Fig 1A ) . Two lineages were shown in Fig 1A , one representing protostome receptors ( Lineage 1 ) and the other representing deuterostome receptors ( Lineage 2 ) . Prior to the two rounds of whole-genome duplications ( 2R ) [23] , mainly deuterostome NMBR-like was present in lineage 2 , indicating that bombesin receptors of vertebrates originated from the deuterostome NMBR-like genes . After the 2R event during the origin of vertebrates , a NMBR/GRPR/BRS3 progenitor expanded to three receptor subtypes: NMBR pairs with its endogenous peptide , NMB; GRPR pairs with its endogenous peptide , GRP; BRS3 has been considered an orphan GPCR since its 1980 identification [11 , 24 , 25] . The protein products of two paralogues , CCHaR-1 and CCHaR-2 , share high amino acid similarity ( above 80% ) in amino acid sequences , binding two highly similar ( >80% ) endogenous peptides CCHamide-1 and CCHamide-2 , respectively . This indicates an independent duplication of both receptor and ligand genes in protostomes ( Fig 1A ) [26] . The phylogenetic tree also suggested that BRS3 originated from a common ancestor of NMBR and GRPR and might still bind the endogenous ligands NMB and/or GRP in early stages of vertebrate origination . To assess whether the bombesin receptors underwent positive selection after the 2R event , a branch-site model was utilized . As shown in Fig 1B , the result showed that only in placental mammal lineage , BRS3 sequences have a large nonsynonymous ( dN ) /synonymous ( dS ) substitution rate ratio ( branch-site dN/dS of ω >> 1; Fig 1A ) that is highly significant ( likelihood ratio tests [LRTs] , P < 0 . 05; Fig 1B ) . In contrast , all other vertebrates do not exhibit this ratio , suggesting that positive Darwinian selection occurred specifically in placental mammalian BRS3 but not in nonplacental vertebrates , including Marsupialia and Monotremata ( Fig 1A and 1B ) . In contrast , no positive selection was detected in GRPR and NMBR ( Fig 1B ) . In conclusion , BRS3 originated from the NMBR-like gene after the 2R event and underwent adaptive selection in placental mammals . Since BRS3 of placental mammals underwent positive selection ( Fig 1B ) , 17 vertebrate species ( 8 placental mammals: Homo and Pan represent Euarchonta , Mus and Rattus represent Glires , Canis and Felis represent Laurasiatheria , and Orycteropus and Loxodonta represent Atlantogenata; 3 nonplacental mammals: Phascolarctos and Monodelphis represent Marsupialia , and Ornithorhynchus represents Monotremata; 6 nonmammalian vertebrates: Gallus and Corvus represent bird , Chrysemys and Anolis represent reptile , Xenopus represents amphibian , and Lepisosteus represents fish ) , representing diverse types of vertebrates , were used for exploring potential structural changes underlying functional differences of BRS3 between placental mammals and nonplacental vertebrates . As shown in S4 Fig and S1 Fig , the average sequence identities and similarities of BRS3 ranged from 0 . 45 to 0 . 5 and 0 . 62 to 0 . 65 when each of the BRS3 receptors was compared to NMBRs/GRPRs . There was no significant difference between placental mammalian and nonplacental vertebrate BRS3 receptors ( P = 0 . 164 ) when their amino acid sequences were compared to those of GRPR and NMBR on average ( S4 Fig and S1A Fig ) . This result indicated that phylogenetic relationships did not reflect the pharmacological differences of BRS3 receptors [16] . Instead , examining the GPCR structures could provide important insights into their pharmacological features [1 , 16] . Consequently , we predicted the 17 BRS3 structures using the Iterative Threading Assembly Refinement ( I-TASSER ) web server [27] ( S1B Fig ) . Each of 17 BRS3 structures was compared with each structure of the NMBRs and GRPRs , respectively ( S1B and S1C Fig ) . Two representative structures were shown in Fig 2 ( human represents placental mammals , and turtle represents nonplacental vertebrates ) , and the other eight species were shown in S4 Fig . As shown in S1A Fig and S4 Fig , the average structural similarities between nonplacental vertebrate BRS3 and NMBR/GRPR were high with root-mean-square deviations ( RMSDs ) and ranged from 9 . 22 Å to 10 . 77 Å . In contrast , the placental mammalian BRS3 receptors had significantly ( P ≈ 0 . 00 << 0 . 01 ) higher RMSDs ( ranging from 13 . 82 Å to 14 . 38Å ) compared to those of nonplacental vertebrate BRS3 ( S1A Fig and S4 Fig ) . In contrast , all 17 vertebrate NMBRs showed no significant difference in structural similarities , with RMSDs ranging from 5 . 55 Å to 7 . 70 Å and 4 . 65 Å to 7 . 93 Å when compared to each of GRPRs , respectively ( S2 Fig ) . Also , there is no significant difference when placental mammalian and nonplacental vertebrate NMBR structures were compared with each structure of GRPRs ( S2 Fig ) . Furthermore , as shown in Fig 2 , S1 Fig , and S4 Fig , there is a significant difference between the predicted N-terminal structures of placental mammalian BRS3 versus nonplacental vertebrate BRS3 . The N-terminal structures of placental mammalian BRS3 consisted of a fragment of alpha helix and coils ( Fig 2 , S1 Fig and S4 Fig ) . In contrast , N termini of nonplacental vertebrate BRS3 show the coils structure , which is same with GRPR and NMBR in all vertebrates ( Fig 2 , S1 Fig , and S4 Fig ) . Taken together , our results indicated that after positive selection , the protein structure of placental mammalian BRS3 differed in comparison to nonplacental vertebrate BRS3 , especially with respect to the N termini , possibly resulting in different pharmacological properties . To verify the functional differences of BRS3 between placental mammals and nonplacental vertebrates , 10 vertebrate species ( 4 placental mammals: human/Homo represents Euarchonta , mouse/Mus represents Glires , dog/Canis represents Laurasiatheria , and aardvark/Orycteropus represents Atlantogenata; 2 nonplacental mammals: koala/Phascolarctos represents Marsupialia , and platypus/Ornithorhynchus represents Monotremata; 4 nonmammalian vertebrates: chicken/Gallus represents bird , turtle/Chrysemys represents reptile , frog/Xenopus represents amphibian , and spotted gar/Lepisosteus represents fish ) were selected to test the biological experimental research ( Fig 2 and S4 Fig ) . Meanwhile , since ERK phosphorylation and mobilization of calcium ions are common indicators of GPCR activation [28] , stimulation of intracellular calcium and ERK by bombesin receptors requires ligand binding to stimulate Gq signaling [1 , 11] . Two conserved consensus peptides , representing conserved mature peptides of GRP and NMB in vertebrates , were synthesized for receptor–ligand function assays [1 , 11] ( S3 Fig ) . These GRP and NMB peptides were utilized to activate the empty vector plasmid pcDNA3 . 1-V5-His ( negative control group ) and each of 10 BRS3 receptors , respectively . All 6 BRS3 receptors in nonplacental vertebrates ( koala , platypus , chicken , turtle , frog , and spotted gar ) could be activated by GRP and NMB , BRS3 receptors stimulated an increase in the levels of phosphorylated ERK ( pERK ) ( Fig 2 and S4 Fig ) , and pERK levels were similar to those when GRPR was stimulated by GRP or NMBR stimulated by NMB ( S4 Fig bottom ) . In contrast , all 4 BRS3 receptors in placental mammals ( human , mouse , dog , and aardvark ) could not be activated by GRP or NMB for ERK phosphorylation increase ( Fig 2 and S4 Fig ) . Furthermore , there was a remarkable increase of Ca2+ ions in cells only when all 6 BRS3 receptors of nonplacental vertebrates were stimulated with GRP or NMB ( Fig 2 and S4 Fig ) , and Ca2+ ion levels were similar to those when GRPR was stimulated by GRP or NMBR was stimulated by NMB ( S4 Fig bottom ) . In contrast , no stimulation was observed in all four placental mammalian BRS3 ( Fig 2 and S4 Fig ) . Also , we quantitated the ERK and calcium assays by statistical analysis in S5 Fig . Moreover , when we tested them in luciferase reporters , they showed results similar to the nuclear factor of activated T cells response element ( NFAT-RE ) luciferase reporter ( S6B Fig ) [29] . On the other hand , it is well known that many GPCRs are coupled to multiple G proteins , which lead to regulation of respective downstream signaling pathways [1] . The existence of ligand-independent ( i . e . , constitutive ) activity of GPCRs was first described in the 1980s , and numerous additional constitutively active GPCRs have been reported to this day [17] . BRS3 was reported to be a Gq-coupled GPCR receptor in vertebrate systems [15] . Since BRS3 of placental mammals lost the connection with GRP and NMB , we investigated its potential constitutive activity with G protein luciferase functional assays , and cAMP response element ( CRE ) , NFAT-RE , serum response factor response element ( SRF-RE ) , and serum response element ( SRE ) luciferase reporters were utilized for testing Gs , Gq , G12 , and potential Gi signaling in human embryonic kidney 293 ( HEK293 ) cells , respectively [29] . In luciferase functional assays , BRS3 plasmids representing 10 classic species were cotransfected with four luciferase reporter plasmids , respectively . CRE , NFAT-RE , and SRF-RE luciferase units for placental mammalian BRS3 ( human , mouse , dog , and aardvark ) showed a significant increase in a dose-dependent manner ( Fig 2 , S4 Fig , and S6A Fig ) . Especially in the case of mouse BRS3 ( mBRS3 ) , the degree of activity increased with the increase of transfection concentration up to 19 . 0-fold , 18 . 6-fold , and 20 . 1-fold for CRE , NFAT-RE , and SRF-RE luciferase units , respectively ( S4 and S5A Figs ) . Moreover , with a GTPgamma35S incorporation assay [30–32] , we further confirmed that placental mammalian BRS3 coupled with Gs , Gq , and G12 and that nonplacental BRS3 coupled with Gq ( Fig 2A and Fig 2B ) . Our data show that BRS3 expression in mouse brain tissue is even higher than that in mBRS3 highest dose–transfected cells ( S7B Fig ) . In contrast , stimulation of luciferase units in a receptor in a dose-dependent manner could not be detected for BRS3 in all six nonplacental vertebrates ( koala , platypus , chicken , turtle , frog , and spotted gar ) , including two nonplacental mammals ( koala and platypus ) ( Fig 2 , S4 Fig , and S5A Fig ) . Also , since Gs signaling mediates the cAMP stimulation , we tested the intracellular cAMP level , and the result was similar with luciferase reporter assay ( S8 Fig ) . Moreover , when four luciferase reporters were cotransfected with BRS3 receptors of placental mammals , respectively , stimulation of luciferase units cannot be detected in a potential ligand ( GRP/NMB ) in a dose-dependent manner ( S6B Fig ) . Taken together , our results revealed that nonplacental vertebrates and placental mammals show a different structure and different function probably because of adaptive evolution in placental mammals . Our results showed NMB and GRP are the endogenous cognate ligands for BRS3 in nonplacental vertebrates , whereas BRS3 of placental mammals constitutively activates Gs , Gq , and G12 signaling . To further verify our hypothesis , three classic BRS3 receptors in nonplacental vertebrates ( chicken BRS3 [cBRS3] , turtle BRS3 [tBRS3] , and spotted gar BRS3 [sBRS3] ) and three classic placental mammalian BRS3 receptors ( human BRS3 [hBRS3] , mBRS3 , and aardvark BRS3 [aBRS3] ) were selected for binding experimental research . As shown in Fig 3A and 3B , binding experiments also showed the direct binding of GRP to BRS3 in nonplacental vertebrates ( cBRS3 , tBRS3 , and sBRS3 ) with high affinity ( IC50 = 0 . 15 nM for cBRS3 , IC50 = 0 . 14 nM for tBRS3 , and IC50 = 0 . 17 nM for sBRS3 ) , but no binding was detected in placental mammals ( hBRS3 , mBRS3 , and aBRS3 ) . Furthermore , we showed direct binding of NMB to BRS3 in nonplacental vertebrates ( cBRS3 , tBRS3 , and sBRS3 ) with high affinity ( IC50 = 2 . 26 nM for cBRS3 , IC50 = 2 . 82 nM for tBRS3 , and IC50 = 2 . 65 nM for sBRS3 ) but again no binding to placental mammalian BRS3 ( hBRS3 , mBRS3 , and aBRS3 ) ( Fig 3A and 3B ) . The binding affinities for GRP and NMB to BRS3 in nonplacental vertebrates were consistent with the GRP–GRPR and NMB–NMBR pairs ( Fig 3A and 3B ) [33] . We also confirmed that NMB inhibits GRPR–GRP binding with very low affinity and that GRP cannot inhibit NMBR–NMB binding ( Fig 3A , Fig 3B , and S9 Fig ) [33] . High affinity was observed when NMB inhibits BRS3–GRP binding in nonplacental vertebrates ( IC50 = 1 . 35 nM for cBRS3 , IC50 = 1 . 14 nM for tBRS3 , and IC50 = 1 . 74 nM for sBRS3 ) and when GRP inhibits BRS3–NMB binding in nonplacental vertebrates ( IC50 = 0 . 18 nM for cBRS3 , IC50 = 0 . 28 nM for tBRS3 , and IC50 = 0 . 54 nM for sBRS3 ) ( Fig 3A and 3B ) . In summary , our results showed that BRS3 in nonplacental vertebrates can still bind to the original ligands , NMB and GRP , with high binding affinity and be activated to stimulate Gq signaling ( Fig 2 , Fig 3A and 3B , and S4 Fig ) . The BRS3s in placental mammals have apparently lost this activity because of their inability to bind those ligands ( Ki > 1 , 000 nM ) . We further verify that BRS3 in placental mammals constitutively activates Gs , Gq , and G12 signaling . Since Gs and G12 signaling show independent signaling pathways but Gi and Gq signaling showed some similar downstream activation ( e . g . , ERK phosphorylation ) , a specific Gq signaling inhibitor YM 254890 was used to confirm Gq signaling of BRS3 receptors [34–36] . We obtained additional confirmation that ERK phosphorylation and calcium ion mobilization were inhibited in the case of BRS3 in three nonplacental vertebrates ( cBRS3 , tBRS3 , and sBRS3 ) and GRP/NMB peptide ligands ( Fig 3C and 3D ) . Furthermore , YM 254890 inhibition confirmed the constitutive activity of BRS3 receptors in three placental mammals ( hBRS3 , mBRS3 , and aBRS3 ) via Gq signaling ( Fig 3E ) . Also , when we apply another Gq downstream signaling inhibitor , sotrastaurin , to these experiments , similar results are shown ( S10 Fig ) . BRS3s in both placental mammals and nonplacental vertebrates were coupled with Gq signaling ( Fig 3 ) , but only BRS3 of placental mammals was coupled with Gs and G12 signaling ( Fig 2 and S4 Fig ) . The Gs and G12 selectivity barcodes were recognized by different key residues of GPCR to trigger a specific downstream pathway [1] . The largest possible binding pocket of Gs , G12 , and GPCR was predicted by Discovery Studio 3 . 0 [37] . These pockets were utilized to construct an initial coarse model of the protein–protein complex , and the model with the lowest energy was then obtained using RosettaDock [38 , 39] . Then , binding sites were obtained by the Residue Interaction Network Generator ( RING ) [40] , and the results were shown in S2 Table . Thus , four amino acids ( 157P , 371V , 269I , and 338Q ) were predicted as key residues of placental mammalian BRS3 to trigger a specific downstream pathway , since they exhibited a large degree of conservation in mammals , and they differ from the corresponding amino acids in nonplacental vertebrate species ( S11 and S12 Figs ) . As shown in Fig 3F , these placental mammalian amino acids change back to the nonplacental vertebrate ( turtle/chicken ) residues and trigger Gs and G12 signaling , as manifested in a decreased stimulation in the CRE and SRF-RE luciferase assay . GPCR expression patterns in cells were examined after plasmid transfection ( S7 Fig ) . The residue 338Q is under positive selection ( Fig 1B ) , and the other three residues ( 157P , 371V , and 269I ) are consistently changed and conserved in placental mammals ( S11 and S12 Figs ) . We also tested eight neighboring residues to check specificity of the amino acids we predicted , and the neighboring residues have no effect or little effect except for reduced expression on the receptor activity ( S13 Fig ) . Taken together , our results revealed that BRS3 in placental mammals constitutively activates two novel GPCR signaling pathways—Gs and G12—because of positive selection . In order to explore the key binding/activating sites of BRS3 with its endogenous ligands , GRP and NMB , in nonplacental vertebrates , receptor–ligand docking was completed by a combination of the Discover Studio package and the FelxPepDock module of Rosetta [41] . Discovery Studio 3 . 0 was utilized for predicting the potential binding pockets of BRS3 receptors , the largest possible binding pocket was utilized to construct an initial coarse model of the peptide-protein complex , and the initial model was obtained using RosettaDock [37] . Since the peptide was considered as a rigid body in RosettaDock , the peptide–protein complex with the lowest energy was then refined by utilizing the FelxPepDock module of Rosetta . The output of 2 , 000 models was then ranked based on their energy score . The first 10 low-energy-score models for each of the three types of receptor–ligand complexes were selected to further analyze the binding sites . Three residues of nonplacental vertebrate BRS3—namely , 127Q , 205P , and 294R—were frequently found to be the binding sites for each of nonplacental vertebrate receptor–ligand complexes ( BRS3–GRP and BRS3–NMB ) ( Figs 4A and 5A and S3 Table ) . In contrast , the three corresponding residues in placental mammalian BRS3 ( 127R , 205S , and 294H ) were found to have moved away from the ligands ( Figs 4B and 5B ) . Furthermore , according to sequence logos between nonplacental vertebrate BRS3 and placental mammalian BRS3 , the three residues were conserved in nonplacental vertebrate BRS3 and altered in placental mammalian BRS3 sequences ( Fig 4C , S11 Fig and S12 Fig ) . Therefore , combined with other results previously reported [42–44] , our results indicated the three residues 127Q , 205P , and 294R as the potential key binding sites of nonplacental vertebrate BRS3 for binding GRP and NMB peptides . To further investigate the function of the three residues ( 127Q , 205P , and 294R ) in the receptor–ligand pocket , the three residues in BRS3 of three classic nonplacental vertebrates ( cBRS3 , tBRS3 , and sBRS3 ) were mutated to the corresponding residues in the placental mammalian orthologs , respectively . In contrast , the three residues ( 127R , 205S , and 294H ) in placental mammalian BRS3 ( hBRS3 , mBRS3 , and aBRS3 ) were reversely mutated to the corresponding residues in nonplacental vertebrate BRS3 , respectively . The mutants ( Q127R , P205S , and R294H ) for cBRS3 , tBRS3 , and sBRS3 and the reverse mutants ( R127Q , S205P , and H294R ) for hBRS3 , mBRS3 , and aBRS3 were constructed to further confirm the important receptor–ligand binding/activating sites of BRS3 . GPCR expression patterns in cells were examined after plasmid transfection ( S7A Fig ) . As shown in Fig 4D , GRP and NMB stimulation of ERK phosphorylation almost could not be detected in the Q127R and R294H mutants in the tBRS3 background; except for the P205S mutant , the effect is not obvious . Simultaneously , the GRP/NMB-induced ERK phosphorylation signal could be detected in the R127Q and H294R mutants , and the effect is not obvious in the S205P mutant for the corresponding hBRS3 background ( Fig 4D ) . Furthermore , as shown in Fig 4E , there was a remarkable decrease of cellular Ca2+ ions for each of three mutants ( Q127R , P205S , and R294H ) of tBRS3 receptor compared with that of wild-type tBRS3 , when GRP and NMB were utilized to stimulate ( Fig 4E ) . In contrast , a remarkable increase of cellular Ca2+ ions for each of three mutants ( R127Q , S205P , and H294R ) in the hBRS3 receptor background was found when compared to Ca2+ levels in wild-type hBRS3 , when GRP and NMB were utilized to stimulate each of the three mutants ( Fig 4E ) . Similar results were obtained when we tested the R127Q , S205P , and H294R mutants in the genes of other placental mammalian species ( mBRS3 and aBRS3 ) and the Q127R , P205S , and R294H mutations in the corresponding genes of nonplacental vertebrates ( cBRS3 and sBRS3 ) ( Fig 5C ) . In contrast , all three mutants , including S205P in mBRS3 and aBRS3 , lead to ERK phosphorylation signal and a significant increase in cellular Ca2+ ions levels ( Fig 5D ) . Also , the significant effects for both ERK phosphorylation and cellular Ca2+ ions levels can be detected in three mutants ( only excepting P205S of cBRS3 for ERK phosphorylation activated by NMB ) of cBRS3 and sBRS3 ( Fig 5C and 5D ) . When we apply these to luciferase reporters , results were similar to those in ERK and Ca2+ assays ( S14 Fig ) . These results indicated that each of the three residues ( i . e . , 127Q , 205P , and 294R ) might play a critical role in the process of BRS3 with GRP/NMB interactions and that the 205th residue differs in function in different species . The impact of point mutations for each of the three residues with respect to placental mammalian BRS3 constitutive activity was also investigated . As shown in Fig 4F , CRE , NFAT-RE , and SRF-RE luciferase units for each of R127Q , S205P , and H294R mutants of hBRS3 were significantly decreased in comparison to those of wild-type hBRS3 . Especially , the mutant R127Q leads to the most significant reduction of the three signal pathways ( Fig 4F ) . The three mutants of tBRS3 were also tested . In contrast , almost three mutants ( Q127R , P205S , and R294H ) of tBRS3 had a significant increase when compared to those of wild-type tBRS3 ( Fig 4F ) . Simultaneously , the three corresponding mutants of the other two placental mammals ( mBRS3 and aBRS3 ) and the two nonplacental vertebrates ( cBRS3 and sBRS3 ) were tested , and the results were consistent with the aforementioned hBRS3 and tBRS3 mutants , respectively ( Fig 5E ) . Our results revealed that 127Q , 205P , and 294R were the key residues for BRS3 in nonplacental vertebrate recognition of GRP or NMB peptides ( Figs 4A and 5A ) . As for the three key residues , 127R and 294H are under positive selection ( Fig 1B ) , and 205S is consistently changed and conserved in placental mammals ( Fig 4C ) . Taken together , our results suggested that positive selection in placental mammalian BRS3 leads to the disconnection between BRS3 and its original ligands , GRP/NMB . Furthermore , each of the three residues ( i . e . , 127R , 205S , and 294H ) had an impact on the process of placental mammalian BRS3 constitutive activation via the attached G protein , resulting in activation of three signal transduction pathways ( Gs , Gq , and G12 ) . However , when we mutated all three residues ( R127Q , S205P , and H294R ) , the results of triple mutants were similar to those of single mutants , and we cannot interpret it appropriately ( S15 Fig ) . Therefore , the key binding/activating sites of BRS3 in nonplacental vertebrates for recognition of GRP and NMB are altered to key residues that regulate constitutive activity in placental mammalian BRS3 . Our results suggested placental mammalian BRS3 underwent positive selection to lose the connection to their original peptide ligands and eventually to evolve into a constitutively active GPCR . To explore the role of the N terminus in the placental mammalian BRS3 , the phylogenetic analysis by maximum likelihood ( PAML ) method was applied to test for positive selection of the N terminus ( 41 amino acids ) , which consists of a fragment of alpha helix and coils , and showed a significant difference between placental mammals and nonplacental vertebrates ( Fig 2 and S4 Fig ) [45] . Truncated receptor expression patterns in cells were examined after plasmid transfection ( S7A Fig ) . The N-terminal domain of placental mammalian BRS3 exhibit a large nonsynonymous ( dN ) /synonymous ( dS ) substitution rate ratio ( branch-site dN/dS of ω >> 1 ) that is highly significant ( LRT , P < 0 . 05; Fig 6A ) but not in nonplacental vertebrate orthologs . Three residues ( 14I , 23S , and 40N ) were found to have undergone positive selection ( Fig 6A ) . Constitutive activation of mBRS3 is the most significant compared with the other two placental mammalian species ( S4 Fig ) ; therefore , a synthetic N terminus peptide of mBRS3 and luciferase functional assays for placental mammalian BRS3 with truncated N termini ( N-hBRS3 , N-mBRS3 , and N-aBRS3 ) were investigated to further verify the role of this domain in for constitutive activity . When these N-BRS3 receptors were stimulated by an N-terminal peptide of mBRS3 , all CRE/NFAT-RE/SRF-RE signaling pathways could be activated ( Fig 6B ) . In contrast , N-cBRS3 , N-sBRS3 , and N-tBRS3 receptors cannot be stimulated by an N-terminal peptide , unlike placental mammalian BRS3 ( S16 Fig ) . The stimulation by the N-terminal peptide is not as high as the one achieved with the entire wild-type hBRS3 , presumably because the synthetic exogenous peptide does not form the necessary structures with the remainder of the receptor for optimal functionality [46] . Besides , we test the truncated BRS3 receptors’ constitutive activation level with luciferase reporter in HEK293 cells . The result showed that truncated receptors also have significant but lower stimulations , compared with intact BRS3 ( S17 Fig ) . These positive selection sites ( 14I , 23S , and 40N ) were conserved amino acids in placental mammalian BRS3 ( S11 and S12 Figs ) . Therefore , the three residues were considered as key residues for constitutive activity of BRS3 in placental mammals . To test these key residues , we mutated these residues in hBRS3 , mBRS3 , and aBRS3 to residues found in the nonplacental vertebrate ( turtle/chicken 14C , 23L , and 40S ) . After transfection in cultured HEK293 cells , luciferase functional assays were utilized to test signaling . As shown in Fig 6C , all mutants of both hBRS3 and aBRS3 exhibited a significant decrease compared with wild-type hBRS3 and aBRS3 , respectively . Especially when S23L was tested for the mouse gene , all CRE/NFAT-RE/SRF-RE luciferase units were sharply reduced to background levels ( Fig 6C ) . To further exam the positively selected sites in the N terminus of BRS3 , we also predicted N-terminal triple-mutated structures of BRS3 , and these mutations do modify the secondary structure of the N terminus of BRS3 to non-alpha helix ( S18 Fig ) . These results revealed that the three N-terminal residues ( 14I , 23S , and 40N ) were key residues for the constitutive activity of placental mammalian BRS3 . Taken together , our results suggested that the N terminus plays a critical role in the conversion of the ancestral BRS3 receptor to constitutive activity in placental mammals .
BRS3 belongs to a classic polypeptide family , originated from the 2R event during vertebrate evolution sharing a common ancestry with NMBR and GRPR ( Fig 1A ) . The latter two GPCRs and their peptide ligands coevolved and are conserved in vertebrates . It is well known that GPCRs connected with peptide ligands over the entire range of vertebrate evolution . Therefore , BRS3 was always considered as a classic orphan receptor since 1980s [7 , 24] . Many synthetic agonists and antagonists are designed for pairing with BRS3 , but up to now , no endogenous BLP as a cognate ligand of BRS3 has been identified [14 , 15] . In this study , we showed that after the common ancestor of NMBR/GRPR/BRS3 expanded to three GPCRs in vertebrates , while NMBR paired with its endogenous peptide ( NMB ) in vertebrates and GRPR paired with its endogenous peptide ( GRP ) in vertebrates , BRS3 in nonplacental vertebrates is still connected to GRP and NMB with high binding affinity stimulating Gq signaling ( Figs 1–3 ) . In contrast , placental mammalian BRS3 lost the original connection with GRP and NMB ( Fig 2 , Fig 3 and S4 Fig ) . Moreover , positive selection of the BRS3 occurred in the placental mammalian lineage , and certain structures changed significantly compared with BRS3 of nonplacental vertebrates ( Fig 1B , Fig 2 and S4 Fig ) . Under positive selection , with three altered key residues ( R127Q , S205P , and H294R ) affecting ligand binding and activation , placental mammalian BRS3 lost connection with its original ligands and became constitutively regulated by its altered G protein selectivity barcodes and its altered N terminus , which also underwent positive selection in placental mammals ( Figs 3–6 ) . Therefore , our results showed that the cognate ligand-BLPs for BRS3 actually are NMB and GRP , but only in nonplacental vertebrates , including nonplacental mammals ( Marsupialia and Monotremata ) ( Fig 2 , Fig 3 and S4 Fig ) , which is consistent with previous study [47] . In contrast , placental mammalian BRS3 underwent positive selection to become a constitutive active GPCR in a ligand-independent manner ( Fig 2 , Fig 3 and S4 Fig ) . Many studies have reported that the mutations from almost every part of GPCR can influence its constitutive activity [48 , 49] , and here our results consistently showed adaptive evolution of both original ligand-binding/activating sites and that the N-terminal domain drove the constitutive activity of placental mammalian BRS3 ( Figs 5 and 6 ) . In phylogenetic analysis and experiment data , platypus ( representing Monotremata ) and koala ( representing Marsupialia ) still harbor a nonplacental vertebrate-type BRS3 ortholog , but the aardvark ( representing Atlantogenata ) features a placental mammalian-type BRS3 ortholog ( Fig 2 , Fig 3 and S4 Fig ) . Also , evolution analysis for both whole gene and the N terminus of BRS3 found positive selection occurred after Marsupialia split from placental mammals ( Figs 1 and 6A ) . Taken together , the endogenous BLP for placental mammalian BRS3 actually does not exist , and the constitutively active BRS3 is a genuinely orphan GPCR in placental mammals , including humans ( Fig 6D ) . At the organ level , NMBR and GRPR , members of the bombesin receptor family , are widely expressed , especially in the gastrointestinal tract and central nervous system ( CNS ) , and have similar functions in regulating smooth muscle contraction and CNS effects [50] . In contrast , BRS3 expression appears to be highly species-dependent [51] . Most BRS3 acts in the brain , including regulating sympathetic outflow and affecting food intake , metabolic rate , body temperature , heart rate , blood pressure , and insulin secretion [50 , 52 , 53] . The best-established role of BRS3 is in the regulation of food intake , energy expenditure , and body weight [50 , 53] . Consequently , BRS3 gene inactivation in mice causes obesity , whereas synthetic agonists produce weight loss [50 , 53] . Here , we propose that placental mammalian BRS3 underwent positive selection in the placental mammal lineage to lose the connection with NMB/GRP and may carry out some physiological role . Further studies on BRS3 are necessary to unveil its different functions between nonplacental vertebrates and placental mammals ( Fig 6D ) . However , one possibility that should not be ruled out is that after positive selection , placental mammalian BRS3 now binds a completely different endogenous ligand by chance . We cannot exclude this remote possibility , as it is virtually impossible to experimentally exclude all possible endogenous ligands for placental mammalian BRS3 . GPCR–ligand pairs coevolved and maintained a large degree of conservation concerning their physiological function and G protein signaling across vertebrates , with even more conservation in mammals [1 , 28] . On the other hand , subtle differences of the same GPCR–ligand pairs were reported between human and mouse [54] . Our results also showed differences in signaling of the same point mutations generated in the mouse or human BRS3 gene ( Figs 4 and 5 ) . The difference was distributed not only in the stimulation of G protein signaling but also in key residues of the N terminus and other domains ( Figs 4–6 ) , which is consistent with significant pharmacological differences between rat and human that were reported previously [55] . Our results indicated diverse evolution between primates and rodents after origination of placental mammalian BRS3 , and further studies are necessary to investigate the different function of BRS3 between human and mouse . Coevolution of GPCR–ligand pairs appears more pronounced in the polypeptide family , and there are approximately 30 orphan GPCRs from the polypeptide family , in which endogenous peptide ligands are predicted to bind to and activate these orphan GPCRs [56] . Since GPCRs from the polypeptide family were considered to be paired with natural polypeptide ligands , many studies aim to identify endogenous peptides to deorphanize these orphan GPCRs in the polypeptide family [7 , 16] . According to the evolution of GPCR-peptide pairs , after the 2R event during the origin of vertebrates , most orphan GPCRs arose via gene duplication and belong to specific subfamilies—e . g . , GPR37L and GPR37 belong to the endothelin/cholecystokinin subfamily , BRS3 belongs to the NMB/GRP subfamily , and GPR39 belongs to the ghrelin/motilin subfamily [57 , 58] . Although these orphan GPCRs were considered to be activated by a naturally occurring peptides , which should be similar to peptides in the same subfamily , they still have not been deorphanized during a long time period . Even though GPR39 has been reported to be activated by obestatin [59] , a contrary view remains that GPR39 cannot be activated by obestatin , and it actually has been proposed to be a constitutively active GPCR in a ligand-independent manner [60 , 61] . Similarly , GPR37 and GPR37L1 were deorphanized by neuropeptides head peptide [62] and prosaptide/prosaposin [63] , but a number of questions remain regarding the pairing of these peptide ligands with GPR37 and GPR37L1 [64] . Thus far , GRP37 still is considered an orphan GPCR and has been reported to regulate cellular protein quality control during Wnt-signaling in a ligand-independent manner [64 , 65] . GPR37 also exhibits N-terminal structural differences in comparison to its homologous GPCR , EDNRA/B , whereby the N terminus plays an important role in regulating its constitutive activity [66 , 67] . Similarly , the N terminus of placental mammalian BRS3 and the N-terminal peptide of most adhesion GPCRs show a similar situation concerning constitutive activity ( Fig 6 ) [46] . Taken together , those orphan GPCRs that cannot be deorphanized often showed constitutive activity . Thus , the contrary view that there exist endogenous ligands for these orphan GPCRs or that they only function constitutively in a ligand-independent manner has not been illuminated [21] . More than 140 orphan receptors that were considered to have endogenous ligands attracted a great deal of interests for deorphanization [3 , 68] . However , the rate of GPCR deorphanization decreased drastically at the turn of this century , suggesting some gaps exist . The reason was mostly considered to be the lack of signal transduction assays and positive control experiments for these orphan GPCRs [69] . Some orphan GPCRs , which function in a ligand-independent manner , often cannot be paired with any of the possible endogenous ligands [19 , 20 , 60 , 61 , 66 , 67] . Under these circumstances , we propose a new point of view that perhaps a large fraction of orphan GPCRs do not have endogenous ligands . Therefore , our results demonstrated that the BRS3 lost its endogenous ligand because of positive selection in placental mammals and finally functions constitutively to become a genuinely orphan GPCR in placental mammals , including humans . This should strengthen the view that at least some of the remaining orphan GPCRs will never be deorphanized by discovery of a natural ligand and will remain genuinely orphan GPCRs . Taken together , our study identified the first example that might represent a new group of GPCRs that will never be deorphanized by the discovery of a natural ligand and will remain genuinely orphan GPCRs that function constitutively in a ligand-independent manner , and it provided new perspectives in addition to the current ligand-driven GPCR deorphanization .
The exons of bombesin receptor family members and two paralogous genes ( CCHaR-1 and CCHaR-2 ) and an out-group gene ( EDNRA ) were translated into the amino acid sequence and aligned with ClustalX version 1 . 8 , using default settings [70] . The corresponding species and genes were shown in S1 Table . Unrooted tree topology based on multiple alignments of amino acid sequences was obtained using the Maximum Likelihood method in MEGA 6 . 06 [71] . We used the branch-site model of PAML version 4 . 4 to test for positive selections on interested . The species for phylogenetic analysis were as follows: Homo: H . sapiens; Pan: P . troglodytes; Mus: M . musculus; Rattus: R . norvegicus; Sus: S . scrofa; Capra: C . hircus; Ovis: O . aries; Canis: C . lupus familiaris; Felis: F . catus; Orycteropus: O . afer; Loxodonta: L . africana; Phascolarctos: P . cinereus; Monodelphis: M . domestica; Ornithorhynchus: O . anatinus; Gallus: G . gallus; Anolis: A . carolinensis; Chrysemys: C . picta; Xenopus: X . tropicalis; Lepisosteus: L . oculatus; Danio: D . rerio; Saccoglossus: S . kowalevskii; Acanthaster: A . planci; Strongylocentrotus: S . purpuratus; Apis: A . mellifera; Nasonia: N . vitripennis; Drosophila: D . melanogaster; Aedes: A . aegypti; Tribolium: T . castaneum; Camponotus: C . floridanus; Parasteatoda: P . tepidariorum; Myzus: M . persicae; Lingula: L . anatina; Crassostrea: C . virginica; and Mizuhopecten: M . yessoensis . WebLogo was developed to generate sequence logos that are graphical representations of the patterns within a multiple sequence alignment and to assist in discovering and analyzing those patterns [72 , 73] . We implemented WebLogo to find conserved sites/areas in GPCRs . The sequence similarity and identity of GPCRs were computed by Sequence Manipulation Suite . The I-TASSER algorithm has been developed for protein conformation prediction [74 , 75] . The structures of GPCRs were predicted using I-TASSER . The RMSD between structures of GPCRs was calculated by Rosetta software . Discovery Studio is a suite of packages for predicting the potential binding pockets of receptors . RosettaDock is a Monte Carlo–based multiscale docking algorithm that optimizes both rigid-body orientation and side-chain conformation [41] . According to the predicted largest possible binding pocket , an initial conformation of the protein complex was constructed for utilizing in RosettaDock . The FelxPepDock module of Rosetta is designed to create high-resolution models of complexes between flexible peptides and globular proteins [76] . Therefore , the peptide–protein complex with the lowest energy was then optimized by using the FelxPepDock module of Rosetta . The binding sites were analyzed based on the first 10 low-energy-score-optimized models of peptide–protein complexes . PyMOL is a molecular graphics system for the visualization of three-dimensional ( 3D ) structures of GPCRs [77] . As for ligand peptides , two conserved and consensus peptides ( GRP-GSHWAVGHLM-NH2 and NMB-GNLWATGHFM-NH2 ) and the N-terminal peptides of mBRS3 ( MSQRQSQSPNQTLISITNDTETSSSVVSNDTTHKGWTGDNS-NH2 ) were synthesized from Biotech Bioengineering ( Shanghai , China ) for receptor–ligand function assays , except radioligand binding assays . BRS3 mutants were generated by introducing point mutations using a QuikChange II site-directed mutagenesis kit ( Agilent Technologies ) . Briefly , overlapping primers with the desired point mutations were used to amplify wild-type BRS3 . The parental plasmid was digested using the DpnI enzyme , and the newly synthesized plasmid was used as a template for PCR amplification of BRS3 mutants before subcloning into the pcDNA3 . 1-V5-His plasmid . To detect constitutive activity of BRS3 , HEK293 cells were seeded in 24-well plates and cotransfected with the CRE/NFAT-RE/SRF-RE/SRE luciferase reporter plasmids ( 50 ng ) , the BRS3 from different species , or BRS3 mutants with different doses ( 10 ng/50 ng/150 ng ) ; after 24 h , cells were further incubated in serum-free media for another 12 h before luciferase assay . As for peptide supplementation treatment , HEK293 cells were cotransfected with the CRE/NFAT-RE/SRF-RE/SRE luciferase reporter plasmids ( 50 ng ) and various genes ( 300 ng ) of the bombesin receptor family . After 24 h , cells were further incubated in serum-free media and GRP/NMB peptides ( 0/10/100/1 , 000 nM ) or the N-terminal peptides of mBRS3 ( 1 , 000 nM ) with different concentrations for another 12 h . Luciferase activities were determined using luciferase assay kits ( Beyotime , Shanghai , China ) and normalized to β-galactosidase activity . All experiments were performed at least three times in triplicates . HEK293 cells transfected with pcDNA were used as a blank control in all luciferase experiments , and the fold was calculated compared to blank control . For ERK1/2 phosphorylation , HEK293 cells were seeded in 24-well plates and transfected with GRPR , NMBR , BRS3 , mutants of different species’ BRS3 , or an empty vector plasmid , pcDNA3 . 1-V5-His plasmid ( 300 ng ) . After 36 h , cells were incubated in serum-free media for another 8 h; stimulated with 1 , 000 nm GRP or NMB for 0 , 2 , and 5 min or 0 , 2 , 5 , and 10 min; homogenized in lysis buffer containing 50 mM Tris-HCl ( pH 6 . 8 ) and 2% sodium dodecyl sulphate ( SDS ) with freshly added protease/phosphatase inhibitor cocktail ( Cell Signaling Technology , Indianapolis , IN , USA ) ; and subjected to western blot using specific antibodies for ERK1/2 ( Cell Signaling Technology , Cat . #9102 , 1:2 , 000 ) and phosphor-ERK1/2 ( Cell Signaling Technology , Cat . #9101 , 1:1 , 000 ) . Each sample with an equal amount of protein was mixed with 6×SDS sample buffer , boiled for 5 min , and separated on 10% SDS-polyacrylamide gel electrophoresis ( PAGE ) before transferring the proteins onto a PVDF membrane . The membranes were then blocked at room temperature for 1 h with 5% milk powder in Tris-buffered saline-Tween ( TBST ) , followed by subsequent incubation at 4°C overnight in TBST containing the different primary antibodies ( 1:1 , 000 dilution ) . After washing three times ( 10 min each time ) with TBST , the membranes were incubated for 1 h in TBST containing the secondary antibodies ( 1:2 , 000 dilution ) , followed by washing three times ( 10 min each time ) with TBST , prior to detection by chemiluminescence . HEK293 cells were seeded in 24-well plates , and the expression vector pcDNA3 . 1-V5-His containing the different BRS3 orthologs or relevant mutants was transiently transfected into HEK 293 cells using Lipofectamine 2000 ( Invitrogen ) with 300 ng of different species’ BRS3 receptor or mutants . After 48 h , supernatant was removed and cells rinsed twice with PBS , homogenized in lysis buffer , and subjected to western blot analysis using specific Bombesin Receptor Polyclonal Antibody ( Thermo Fisher , Cat . #PA5-26484/Lot . #A81B02N , 1:1 , 000 ) and actin as a control ( Sigma-Aldrich , A5441 , 1:5 , 000 ) . Intracellular calcium was measured using the non-wash calcium assay Fluo8 kit ( ab112129 , Abcam ) according to the manufacturer’s instructions . Briefly , HEK293 cells were seeded in 24-well plates and were transfected with GRPR , NMBR , BRS3 , or the empty vector plasmid pcDNA3 . 1-V5-His ( 300 ng ) and incubated overnight at 37°C with 5% CO2 . The next day , the cells were replated into a 96-well assay plate ( black plate and clear bottom ) . After 12 h , growth media were aspirated , and calcium dye was added . Following incubation for 30 min at 37°C and 10 min at room temperature , GRP ( 10 nM ) or NMB ( 10 nM ) was added , and assay plates were placed into a fluorescence kinetic plate reader ( Hamamatsu ) immediately . The basal fluorescence intensity was recorded 15 times at 1 Hz for 20 s . The results were normalized to the average basal fluorescence intensity in ratio , and the peak response was used for the result calculation . Calcium fold was calculated using no stimulation data as standard value . The unlabeled ligand peptides GRP ( APLQPGGSPALTKIYPRGSHWAVGHLM ) and NMB ( YKIKVNPRGNLWATGHFM ) were synthesized by [125I]-Tyr42-GRP and [125I]-Tyr-NMB ( Anhui Guoping Pharmaceutical ) at a specific activity of 870 Ci/mmol and 690 Ci/mmol , respectively , and were prepared by the Beijing North Institute of Biotechnology . HEK293 cells were seeded in a 10-cm tissue culture dish at a density of 106 cells per dish and grown overnight at 37°C in growth medium . The following morning , 5 μg of different species’ BRS3 plasmids were transfected . After 6 h , the medium was replaced with growth medium . Cells were maintained at 37°C in a 5% CO2 atmosphere and used 48 h later for binding assays . The positive control group included transfected GRPR and NMBR; the test group consisted of transfected BRS3 from different species . Then , [125I]-Tyr42-GRP and [125I]-Tyr-NMB were used as the ligands to assess affinity to the various receptors . Briefly , 48 h after transient transfection with Lipofectamine , disaggregated transfected cells were incubated for 1 h at 21°C in 250 μl of binding buffer containing 24 . 5 mM HEPES ( pH 7 . 4 ) , 98 mM NaCl , 6 mM KCl , 2 . 5 mM KH2PO4 , 5 mM sodium pyruvate , 5 mM sodium fumarate , 5 mM sodium glutamate , 2 mM glutamine , 11 . 5 mM glucose , 0 . 5 mM CaCl2 , 1 mM MgCl2 , 0 . 01% ( w/v ) soybean trypsin inhibitor , 0 . 2% ( v/v ) amino acid mixture , 0 . 2% ( w/v ) BSA , and 0 . 05% ( w/v ) bacitracin with 50 pM of [125I]-Tyr42-GRP ( 870 Ci/mmol ) or 50 pM of [125I]-Tyr-NMB ( 690 Ci/mmol ) in the presence of the indicated concentration of unlabeled peptides . The cell concentration was adjusted to between 0 . 05 and 4 × 106 cells/ml for each receptor such that less than 20% of the total added radioactive ligand was bound during the incubation , and the results were compared to cells transfected with GRPR or NMBR adjusted in concentration to bind a similar amount of ligand . After the incubation , 100 μl aliquots were added to 1 . 5-ml microfuge tubes , which contained 100 μl of binding buffer to determine the total radioactivity . The bound tracer was separated from unbound tracer by pelleting the cells through the binding buffer by centrifugation at 10 , 000g in a Microfuge E ( Beckman ) for 3 min . The supernatant was aspirated , and the pelleted cells were rinsed twice with a washing buffer that contained 1% ( w/v ) BSA in PBS . The amount of radioactivity bound to the cells was measured in a Cobra II Gamma counter ( Packard Instruments ) . Binding was expressed as the percentage of total radioactivity that was associated with the cell pellet . All binding values represented saturable binding; nonsaturable binding was <15% of the total binding in all experiments . Each point was measured in duplicate , and each experiment was replicated at least four times . Calculation of affinity was performed by determining the IC50 ( the GRP or NMB concentration causing half-maximum inhibition of binding ) , using the curve-fitting program KaleidaGraph ( Synergy Software ) , and the Hill coefficient ( nH ) was calculated from the displacement curve by using GraphPad Prism [78] . All the experiment data were calculated using 1 pM ligand-treated as 100% control . Discovery Studio is a suite of packages for simulating macromolecule systems [37] . The largest possible binding pocket of Gs , G12 , and GPCR was then predicted by Discovery Studio 3 . 0 [37] . These predicted pockets were utilized to construct an initial coarse model of the G protein–GPCR complex . The molecular model of GPCR receptors and G protein were predicted by the I-TASSER algorithm . I-TASSER was designed for protein structure modeling by iterative threading assembly simulations [27] . Starting from an amino acid sequence , I-TASSER first generates 3D atomic models from multiple threading alignments and iterative structural assembly simulations . The function of the protein is then inferred by structurally matching the 3D models with other known proteins . The output from a typical server run contains full-length secondary and tertiary structure predictions and functional annotations on ligand-binding sites , Enzyme Commission numbers , and Gene Ontology terms . An estimate of accuracy of the predictions is provided based on the confidence score of the modeling . Previous study revealed the existence of a selectivity barcode ( that is , patterns of amino acids ) on each of the 16 human G proteins , which is recognized by distinct regions on the approximately 800 human receptors [1] . Therefore , the docking between GPCRs and G proteins was explored based on the known R-G protein co-crystal structures and homology modeling . The docking process between G protein and GPCRs was the same as the docking process between BRS3 receptors and GPR/NMB . The model with the lowest energy was then obtained using Rosetta software ( RosettaDock and FelxPepDock module ) [38] . All types of noncovalent interactions at the atomic level in a protein structure could be identified by the RING ( http://protein . bio . unipd . it/ring/ ) [40] . Binding sites between proteins in complex were then obtained by RING . HEK293 cells were cultured as a monolayer on culture plates to 80%–90% confluency . Cells were harvested and centrifuged twice at 1 , 000 rpm for 5 min . The amount of cAMP produced was determined with the cAMP ELISA Detection Kit ( GenScript ) . Three thousand cells per well were preincubated for 45 min at 37°C and subsequently at room temperature for 3 h with a range of agonist concentrations . The incubation was stopped by adding detection mix and antibody solution , according to the instructions of the manufacturer . The generated fluorescence intensity was then quantified finally with Synergy H1 ( BioTek ) . Assays were run in 20 mM HEPES , 100 mM NaCl , 8 mM MgCl2 , and 10 μg·mL–1 at pH 7 . 4 in a final volume of 200 μl in 1 . 5-ml tubes at 25°C . One hundred microliters of membrane preparation ( 20 μg protein per well ) containing 5 μM GDP was added , followed by the addition of 10 μl of buffer agonists being tested and 10 μl of GTPgamma35S to provide a final concentration in the assay of 400 pM . Cell membranes were incubated for 30 min at 25°C with agonists , followed by addition of GTPgamma35S and incubation for an additional 60 min . Preincubation was employed to ensure that agonists were at equilibrium during the labeling period . Then , 35S-labeled membranes were solubilized for 30 min with 0 . 27% Nonidet P-40 , followed by the addition of the desired antibody ( 10 μl/well ) to provide a final dilution of 1/200 and incubation for an additional 60 min . Fifty microliters of suspended anti-IgG-coated SPA beads was added per tube . Tubes were incubated for 3 h and then were centrifuged , and their radioactivity was determined using a Aloka LSC-8000 counter . Experiments were repeated independently at least three times . Results were analyzed using GraphPad Prism 5 . Differences between two groups were compared using two-tailed Student t test . One-way ANOVA was followed by a Fisher’s LSD post hoc test to evaluate the differences among multiple groups . Data are expressed as mean ± SEM . Calculations were done with a standard statistical package ( SPSS for Windows , version 21 ) . Statistical significance was defined as a P value < 0 . 05 ( * ) or P value < 0 . 01 ( ** ) [79] .
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The current model on G protein–coupled receptors ( GPCRs ) is that they have at least one endogenous ligand for the activation of the heterotrimeric G proteins that controls lots of physiological functions . Currently , 140 out of approximately 800 GPCRs are referred to as orphan because of a lack of knowledge about their endogenous ligands , and they attract great interest in major medical institutes and pharmaceutical companies . Can a “ligand-receptor” model apply to all the GPCRs ? Our study shows that it probably cannot . By studying the evolution of a classic orphan GPCR–bombesin receptor subtype-3 ( BRS3 ) , we show that placental mammalian BRS3 is constitutively active , in contrast to nonplacental vertebrate BRS3 , which actually connects to its original ligands—neuromedin B ( NMB ) and gastrin-releasing peptide ( GRP ) . Protein structure analysis and experiment data also suggest that placental mammalian BRS3 , including human BRS3 , lost connection with its original ligands during placental mammalian evolution . Therefore , we propose a new point of view that a considerable number of orphan GPCRs do not have endogenous ligands and might represent a new group of GPCRs that are genuinely orphan GPCRs and will never be deorphanized by discovery of a natural ligand . This new finding will provide new perspectives in current ligand-driven GPCR deorphanization .
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2019
|
Constitutively active BRS3 is a genuinely orphan GPCR in placental mammals
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Hsp90 from Giardia lamblia is expressed by splicing of two independently transcribed RNA molecules , coded by genes named HspN and HspC located 777 kb apart . The reasons underlying such unique trans-splicing based generation of GlHsp90 remain unclear . In this study using mass-spectrometry we identify the sequence of the unique , junctional peptide contributed by the 5′ UTR of HspC ORF . This peptide is critical for the catalytic function of Hsp90 as it harbours an essential “Arg” in its sequence . We also show that full length GlHsp90 possesses all the functional hall marks of a canonical Hsp90 including its ability to bind and hydrolyze ATP . Using qRT-PCR as well as western blotting approach we find the reconstructed Hsp90 to be induced in response to heat shock . On the contrary we find GlHsp90 to be down regulated during transition from proliferative trophozoites to environmentally resistant cysts . This down regulation of GlHsp90 appears to be mechanistically linked to the encystation process as we find pharmacological inhibition of GlHsp90 function to specifically induce encystation . Our results implicate the trans-spliced GlHsp90 from Giardia lamblia to regulate an essential stage transition in the life cycle of this important human parasite .
Heat Shock Protein 90 ( Hsp90 ) , is a versatile molecular chaperone , involved in diverse cellular processes . It is an essential and evolutionarily conserved chaperone present in both prokaryotes and eukaryotes . Hsp90 have selective set of proteins to chaperone called as clients , which majorly includes transcription factors and protein kinases . Through its interaction with its clients it modulates cell cycle , signal transduction , differentiation , development and evolution [1] , [2] . In recent past many new roles have been attributed to Hsp90; like stabilization of genetic variations and transposon mediated mutagenesis [3] . In protists like Dictyostelium , Leishmania , Plasmodium , Toxoplasma and Trypanosoma Hsp90 has been shown to play an important role in growth and development [4] , [5] , [6] , [7] , [8] , [9] . In Plasmodium , Hsp90 has been established to modulate transition from ring to trophozoite stage [6] . Similarly , in Leishmania Hsp90 inhibition results in stage differentiation [5] whereas in Trypanosoma cruzi challenging Hsp90 function by using specific inhibitor abrogates the growth of the parasite in vitro [8] . In parallel , Hsp90 in Candida has been shown to be involved in morphogenesis . Inhibition of Hsp90 mimics the temperature dependent morphogenesis of yeast forms to filamentous forms of Candida [10] , [11] . All together , these studies highlight the varying function of Hsp90 in the diverse biological systems . Due to its involvement in the range of biological processes , Hsp90 has been proposed as a drug target and its inhibitors as candidate drugs for Malaria and Trypanosomiasis [12] , [13] , [14] . Hsp90 has a characteristic domain organization which is; amino terminal domain , catalytical middle domain and a dimerizing carboxy terminal domain . The amino terminal domain possesses a binding pocket for ATP/Geldanamycin , it also binds to some co-chaperones . The middle domain houses the catalytic residue ‘Arg’ and also some reports suggest that clients bind through this region [15] . Bridging the amino terminal and middle domain is a variable charged linker; truncation studies report its importance in co-chaperone binding and client maturation [16] , [17] , [18] . Carboxy terminal domain is responsible for the dimerization of Hsp90 and interaction with TPR domain containing proteins to form a molecular chaperone complex . Giardia is a minimalistic protozoan which is a common cause of diarrhea worldwide . The infection in the mammalian hosts is established upon ingestion of the environmentally resistant , latent cysts [19] , [20] . The ingested cysts upon encountering the harsh acidic conditions of the host stomach undergo excystation to form actively dividing trophozoites . These trophozoites colonize the colon of the intestine where they adhere to the epithelial cells and are thus well nourished in a nutrient rich milieu [21] . Some of these trophozoites undergo encystation upon environmental cues that are only partially understood [22] . The precise molecular triggers and mechanism underlining these transitions remain unclear [23] . Previously we have shown Hsp90 from G . lamblia to be represented as two separate genes ( HspN and HspC ) together accounting for N- and C-terminal halves of a canonical Hsp90 . We found that the pre-mRNA's corresponding to HspN and HspC are brought together by a trans-splicing mechanism that is assisted by complimentary , positional sequences within the individual pre-mRNAs [24] , [25] . Based on the cDNA sequence of the full length Hsp90 we proposed that removal of a part of HspN and addition of a segment adjoining to HspC are required to generate GlHsp90 , during the splicing mechanism . The evidence for such a reconstruction mechanism at proteomic level was however missing in this study . In the present study , we provide proteomic evidence for the above mentioned trans-splicing mechanism by mass spectrometry based sequencing of the critical junctional peptide from GlHsp90 expressed in trophozoites of G . lamblia . In addition , we have biochemically characterized the trans-spliced full length product with respect to its ability to bind and hydrolyze ATP . As described above , Giardia has two alternative life stages , namely trophozoites and cysts . The mechanism of encystation and the molecular players involved in this transition have not been identified so far . We find Hsp90 to play an important role in stage transition from trophozoite to cyst in Giardia . We find decrease in the levels of GlHsp90 from trophozoites to cysts by more than 50% . In agreement , pharmacological inhibition of GlHsp90 appears to promote the formation of cysts . All together , in addition to proteomic and biochemical characterization of trans-spliced GlHsp90 our results provide important functional insights into its role in regulating the life cycle stages of Giardia lamblia .
G . lamblia parasites were cultured in TYI – S33 supplemented with 10% Adult Bovine Serum and sub-cultured with 5×104 cells per tube from log phase parasites [24] . 5×104 cells were seeded and harvested at log phase for the experiment . Healthy adherent cells were subjected to 40°C or 37°C , for 30 mins in water bath , followed by 1 hour of recovery at 37°C . Following the recovery cells were harvested by chilling on ice for 20 mins and spun down at 700× g at 4°C . For western blot analysis cells were lysed as described previously and clarified . Total protein in the supernatant was estimated by Bradford method of protein estimation . Equal protein was resolved on SDS PAGE , transferred on to nitrocellulose membrane and probed with HspN specific antibody [24] . For real time PCR harvested cells were washed with chilled PBS and total RNA was extracted by TRI-reagent as described by manufacturer's protocol . A narrow slice corresponding to a GlHsp90 band was cut from the stained SDS-PAGE gel and further sliced into smaller gel plugs . Each samples were processed and analysed by automated nanoflow LC-MS/MS as described previously [26] . The spectra were acquired on a Q-STAR Elite mass spectrometer equipped with Applied Biosystems NanoSpray II ion source . The data was acquired in a data dependent mode , one MS spectrum followed by 3 MS/MS spectra . Data analysis was performed in Analyst QS 2 . 0 software . For identification of proteins the processed data was searched against Giardia database ( www . giardiadb . org ) [27] using the ProteinPilot 2 . 0 with precursor and fragment mass tolerances of 0 . 15 Da , cysteine carbamidomethylation as fixed modification and methionine oxidation , lysine acetylation , glutamine and asparagine deamidation as variable modifications . The resulting MS/MS based peptide identifications were manually verified . The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium ( http://www . proteomexchange . org ) via the PRIDE partner repository [28] with the dataset identifier PXD000795 . Total RNA was isolated using TRIZOL ( Bioline ) as per the manufacturer's protocol . The concentration of RNA was estimated at A260 and the purity checked at A260/280 . Quality and integrity of RNA was assessed on 1% formaldehyde-agarose gel . RNA ( 2–5 µg ) was treated with DNAse I , and then reverse transcribed , using OligodT primers . Prior to quantitative real time PCR , semi-quantitative PCR was performed . The sequence of the primers used for amplification were 5′CGCCCTTCGACATGTGGGAC′3 ( forward ) and 5′CCGCAGCACGACGCCGC′3 ( reverse ) for HspN , 5′CGCCCTTCGACATGTGGGAC′3 ( forward ) and 5′GAACGTGAGCCAGTCGGGAAC′3 ( reverse ) for Full length GlHsp90 , 5′-CCCTATAACTAACACGCAGG-3′ ( forward ) and 5′-CGATGCGATTCTTCTGGAGC-3 ( reverse ) for HspC and 5′-GCCCGAGGAGATCCCATGG-3′ ( forward ) and 5′-CTTGCAGCCGCCGGTGATATG-3′ ( reverse ) for GAPDH . Each primer concentration and the respective annealing temperatures were standardized . Real-Time PCR reactions were performed in a 25 µl mixture containing 1 µl of cDNA ( diluted 1∶2 ) 1× SYBR Green buffer ( Bioline ) , 400 nM primers , 4 mM MgCl2 , 0 . 2 mM dNTPs mix and 0 . 025 Unit Taq thermostable DNA polymerase . The annealing temperatures used for N-terminus , full-length , C-terminus GlHsp90 and GAPDH amplifications were 57°C , 61°C , 56°C and 60°C respectively . Real-Time quantitations were performed using the BIO-RAD iCycler iQ5 system . Appropriate no-RT and non-template controls were included in each 96-well PCR reaction ( in all experiments n = 3 , error bars represents S . D ) . Primers used for the full length Hsp90 cloning; sense 5′ – CCCCCGGATCCATGCCCGCTGAAGTCTTCGAGTTC -3′ and antisense 5′-CCCCCGAATTCTCAGTCAACTTCGTCAACGTCCTC-3′ were used . For confirmation of gDNA contamination HspN internal sense primer 5′- GTGTfCGCACTTCCGTGTCG -3′ was used in combination of antisense HspN primer used for qRT-PCR . The amplicons were ligated to pRSET series vector with N terminus 6× His-tag . The positive clone was confirmed by restriction digestion and PCR using respective specific primers . The positive clone was transformed to BL21 . PlysS E . coli strain , induced by IPTG and purified using Nickel NTA agarose beads as described in manufacturer's instructions . Fluorescence measurements were carried out in a Perkin Elmer fluorescence spectrophotometer using 25 µg of purified HspN in 20 mM Tris ( pH 7 . 4 ) and 1 mM EDTA . The sample was excited with a wavelength of 280 nm and the emission was scanned between 300–400 nm , at 120 nm/min . The filter width was adjusted to 3 . 5 and 4 . 5 nm for excitation and emission respectively . Fluorescence emission at 340 nm was taken for all the calculations . The concentrations of ATP used were varied from 50 µM to 100 µM . The data points were fitted with a curve by a non-linear regression analysis using a single site specific binding to get dissociation constant ( Kd ) [12] . ATPase assay was carried out in 40 mM HEPES-KOH , 5 mM MgCl2 , pH 7 . 5 . The final concentration of purified HspN used was 0 . 125 µg and the concentration of ATP was varied from 150 to 5000 µM in a final reaction volume of 10 µl . The reaction was incubated at 37°C for 1 hour and then quenched by adding 1 µL of 0 . 5 M EDTA . Thin layer chromatography was carried out on polyethyleneimine-cellulose F ( Merck ) sheets , using mobile phase 0 . 5 M LiCl , 0 . 5 mM EDTA and 2N formic acid . The polyethyleneimine-cellulose sheets were dried and analyzed by phosphor imaging . The spots corresponding to phosphate and ATP were quantitated using Image Quant software ( Fujifilm ) . To determine the Hsp90-specific ATPase activity , 150 µM of 17-AAG was used . All values are averages of three separate measurements [12] . Two step encystation protocol was followed [29] . Concentration of Ox bile ( Hi-media ) was standardized for the optimum number of viable cysts formation in vitro . Log phase grown adherent cells in growth medium was harvested . Number of viable cells were counted using trypan blue in haemocytometer . 5×103 live trophozoites/mL were seeded in Pre-encystation medium . Pre-encystation medium differs from growth medium in the bile and pH . Pre-encystation is completely devoid of bile and pH was set at 7 . 2 . Faetal bovine serum was used in this condition . Cells were incubated at 37°C for 3 days ( till confluency ) . At the end of 3 days , motile cells were removed along with the medium and replaced with pre-warmed encystation medium . Encystation medium contain 10 time excess bile in comparison to growth medium with pH of 7 . 8 . Adult bovine serum was maintained in the encystation medium . In addition , Lactic acid was used in the medium . The cells were maintained in this condition for 2 days . At the end of two days cysts formed was counted by haemocytometer and harvested [29] , [30] ( in all experiments n = 3 , error bars represents S . D ) . Cysts are enriched by water lysis of contaminating trophozoites . Cysts were harvested in sterile conditions , tubes were chilled on ice for 20 mins , and cells were pelleted down at 150× g for 10 mins . The cell pellet obtained was re-suspended in sterile chilled water and incubated in 4°C on end on rotor for 20 mins . After the incubation the cysts were pelleted at 150× g for 10 mins and water lysis was carried out repeatedly for 3 times . The cysts obtained were allowed to stand in 4°C over night . The cysts were pelleted down and viability was counted using trypan blue . The purified cysts were either harvested in trizol or in Tris 20 mM with 1% Triton ×100 buffer for total RNA or protein extraction . Hsp90 antisense was cloned in “Oct-vector” described previously [31] . The insert was amplified using sense primer 5′- ATGCCCGCTGAAGTCTTCGAGTTC -3′ and antisense primer 5′- TCAGTCAACTTCGTCAACGTCCTC -3′ from total cDNA of the Trophozoites . The constructs were prepared in oct-vector by replacing PPAc Antisense using XhoI and ApaI . The overhangs were blunted using Phusion enzyme . The inserts were ligated ( blunt end ) and transformed to E . coli DH5α and screened for negative orientation clones . The orientation was confirmed by restriction digest pattern using BamHI .
Based on the cDNA sequence of full length Hsp90 mRNA we have previously proposed that a trans-splicing event results in generation of a full length Hsp90 mRNA from two independently expressed HspN ( Gene ID , GL50803_98054 ) and HspC ( Gene ID , GL50803_13864 ) pre-mRNAs . Such a trans-splicing event results in removal of a segment of predicted HspN ORF and addition of a junctional peptide from the 5′ untranslated region of predicted HspC ORF . This 5′ region happens to be a non-coding strand of a predicted hypothetical gene in GiardiaDB ( Gene ID , GL50803_31692 ) . While we did provide proteomic evidence for the presence of such a full length protein , the precise amino acid sequence of the unique , junctional peptide remained to be shown at the protein level . Towards this , we prepared total cell extracts from G . lamblia trophozoites and fractionated the protein mixture on SDS-PAGE ( Figure 1A ) . The position of the full length GlHsp90 protein was known to us from our previous analysis [24] . We thereby performed in-gel trypsin digestion of the GlHsp90 band as described under “materials and methods” . Since the GiardiaDB does not contain the full-length GlHsp90 sequence , we manually included the protein sequence in our search list by translating the cDNA sequence obtained previously [24] . As expected and shown previously , mass-spectrometric analysis of the tryptic digest revealed the presence of peptides from both the predicted HspN and HspC ORFs ( Figure 1C ) ( other proteins identified from the sample are tabulated in Table S1 ) . In addition , the analysis also revealed the junctional peptide ( yellow shaded region on the sequence of Figure 1D ) sequence corresponding to the 5′ UTR of HspC ORF , which is also a part of non-coding strand of the predicted hypothetical ORF , GL50803_31692 . The above analysis confirms the contribution of 3 independent genes , namely 1 ) HspN , 2 ) HspC and 3 ) non-coding strand of the hypothetical gene in the generation of the full length GlHsp90 , the latter two i . e . , HspC and non-coding strand of hypothetical gene being expressed as a common transcript . Hsp90 is an ATP dependent molecular chaperone , which requires binding and hydrolysis of ATP for maturation of the client proteins [32] . According to the annotated sequence of HspN and HspC in GiardiaDB , HspN harbors all the residues required to form the nucleotide binding domain of canonical Hsp90 . The catalytic “Arginine” however is absent in either of the predicted ORFs . Only the reconstructed full length Hsp90 generated by trans-splicing would have all the features required for binding as well as hydrolysis of ATP . To ascertain whether the trans-spliced GlHsp90 is catalytically active and possesses all the features of canonical Hsp90 , we cloned , purified and biochemically characterized full length GlHsp90 . To analyze whether the trans-spliced product could bind and subsequently hydrolyze ATP , we have first determined the binding strength of ATP to Hsp90 in vitro by monitoring the decrease in the intrinsic fluorescence of protein upon binding to ligand as described under “materials and methods” In brief , increasing concentration of ATP was incubated with a constant amount of protein and the intrinsic fluorescence was monitored . The saturation curve was obtained when difference in intrinsic fluorescence was plotted against molar concentration of the ligand which was further analyzed by Graphpad Prism 4 software using non-linear regression analysis . The observed Kd value for ATP binding was 629 . 6 µM ( Figure 2A ) which was in the same range as that of other known Hsp90s ( Table 1 ) . Similarly , we have determined Kd value for GA and its analog , 17-N-allylamino-17-demethoxygeldanamycin ( 17AAG ) , and found it to be 1 . 5 µM and 17 . 06 µM respectively ( Figure 2B and 2C ) . The binding data confirmed the ability of the trans-spliced Hsp90 to bind to its cognate ligand ATP as well its inhibitor GA with high affinity . The binding of Hsp90 to ATP is followed by its hydrolysis which drives the chaperone cycle towards completion and helps in the maturation/stabilization of client proteins [33] . The junctional peptide , that we identified ( Figure 1 ) harbors the key residue which confers Hsp90 its ATPase activity . To assess the biochemical parameters of Hsp90 ATPase activity we have incubated the purified Hsp90 with increasing concentrations of ATP and γP32 ATP as tracer and analyzed the rate of hydrolysis by TLC based method as described previously [12] . The data was analyzed in Graphpad Prism 4 using non-linear regression analysis for Michaelis-Menten equation ( Figure 2D ) . The results have been tabulated ( Table 1 ) , and as is clear that the trans-spliced Hsp90 is an active ATPase , with a catalytic efficiency of 4 . 4×10−5 min−1 µM−1 and Kcat of 4×10−2 min−1 which was found to be similar to human Hsp90 . Since full length GlHsp90 is generated from two independently expressed transcripts ( HspN and HspC ) , it was important to examine the relative levels of full length and its precursor transcript . Analysis of the upstream sequences of HspN and HspC showed initiation elements ( Inr ) typically present at −40 to −60 regions of genes in early branching eukaryotes like G . lamblia and Trichomonas vaginalis [34] . These Inr elements are highly similar to the tubulin and GAPDH genes which have been studied very well in Giardia [35] , [36] To identify a putative Inr element of Hsp90 gene components we aligned the upstream elements of tubulin with the upstream sequences of Hsp90 gene components . On comparison , it was observed that HspC Inr elements are more similar to tubulin Inr than HspN [35] . In addition to a canonical Inr element of 21 nts found in both HspN and HspC , we found a unique , 15 nts Inr element located at position −61 specifically in HspC ORF . ( Figure 3A ) . The differences in the promoter elements upstream of HspN and HspC ORFs described above suggested differential transcriptional activities of these ORFs . In order to compare the levels of transcripts of full-length GlHsp90 with those of individual HspN and HspC ORFs , we conducted quantitative real time PCR . Towards this , we have isolated total RNA from Giardia trophozoites as described in “materials and methods” . The primers for qRT-PCR were designed against regions which are “specific” to the full-length , HspN , HspC or GAPDH . The specificity of qPCR products was confirmed by melting curve analysis and/or gel-based post-PCR analysis . The genomic DNA contamination was ruled out with PCR for HspN from cDNA prepared with and without RT as control ( Figure S1 in Text S1 ) . As can be seen from Figure 3B , the levels GlHsp90 transcript was found to be 90 folds higher than HspN and 5 fold higher than HspC precursors . To investigate the effect of heat stress on the expression of trans-spliced Hsp90 , we seeded cells from the stationary phase and subjected them to heat shock at 40°C for 30 mins and maintained the control cells at 37°C as described under “materials and methods” . Following heat shock , cells were recovered at 37°C for 1 hour and harvested as described previously . We quantified the degree of modulation of Hsp90 and its gene components at the transcript level under heat stress using real Time PCR . Towards this we isolated RNA and prepared total cDNA . We used equal amounts of cDNA from heat shock treated and control cells and compared the Ct values for full-length , HspN and HspC . The amplification of GAPDH transcript was used as a control which did not get affected upon heat stress . We analysed our data by comparing Δ Ct and Δ ( Δ Ct ) values for full-length , HspN and HspC . The relative fold induction of all three transcripts has been represented in the graph as shown in Figure 3D . Upon heat shock , full-length Hsp90 , HspN and HspC transcripts were found to be induced at 2 . 4 , 3 . 2 and 2 folds respectively . To examine if the observed up-regulation of the Hsp90 transcript upon heat shock also hold true at the protein level , we probed cell lysates of trophozoites with and without heat shock with α-Hsp90 antibody . Equal amount of protein was loaded and the fold up-regulation was determined by western blot analysis . It was observed that the increase in Hsp90 was about 2 . 3 fold at the protein level ( Figure 3D , right panel ) . The above data suggest that despite the split nature of Hsp90 gene , Giardia has retained its ability of Hsp90 heat induction through an intricate co-ordination of transcription and trans-splicing events . However , the factors contributing towards mRNA and protein stability may also play a role in observed increase in the levels of Hsp90 at transcript and protein level respectively . Giardia has a biphasic life cycle with a proliferative trophozoite stage and a latent cyst stage [19] . The molecular machinery and mechanisms involved in the above stage transition remain unclear . As Hsp90 is known to be involved in the ability of eukaryotic cells to sense and respond to environmental changes [10] , [37] , we examined its potential involvement in Giardia stage transition . Towards this , we first established encystation process in the axenic culture of Giardia trophozoites as described under “materials and methods” . According to previous reports we were able to obtain Giardia cysts by modulating pH and bile concentration of the medium [30] . The presence of mature water resistant cysts was confirmed by IFA using α-Cysts Wall Protein 1 ( CWP1 ) antibody , a cyst specific protein , and counter stained for nucleus using propidium iodide [38] , [39] . As shown in Figure 4A , in the upper panel , CWP1 shows a characteristic ring like pattern around the cyst and four nuclei are seen in our IFA analysis . We examined the levels of GlHsp90 both at transcript and protein levels . qRT-PCR was performed as described under “materials and methods” from trophozoites and mature cysts to compare the levels of GlHsp90 transcript . Briefly , equal number of trophozoites and cysts were harvested and RNA was prepared using Trizol as described under “materials and methods” . Equal concentration of RNA corresponding to cyst and trophozoite was used to prepare corresponding cDNAs and subjected to qRT-PCR . The resulting Ct values from trophozoites and cysts reactions were normalised to GAPDH level in respective samples . As can be seen in Figure 4B , we found GlHsp90 to be significantly down-regulated in cysts in comparison to trophozoites . Specifically , GlHsp90 transcript showed 3 . 5 fold down-regulation in cysts as compared to trophozoites . To test if the qRT-PCR observation also reflects at GlHsp90 protein level , we have carried out western blot analysis of trophozoites and cysts . We resolved equal amount of protein from encysting trophozoites at different time intervals ( 0 , 24 and 48 hours ) [38] , until completion of encystation , on SDS-PAGE and transferred onto nitrocellulose membrane . The membrane was probed with GlHsp90 specific antibody and developed appropriately . As shown in Figure 4C , we found gradual decrease in GlHsp90 levels during the course of encystation . The mature cysts showed almost 50% reduction in GlHsp90 protein levels as compared to trophozoites . This result was in agreement with the qRT-PCR data shown above . To test if the drop in GlHsp90 levels during trophozoites to cysts transition was mechanistically linked to encystation process , we examined the effect of pharmacological inhibition of GlHsp90 on encystation . We used a semisynthetic derivative of Geldanamycin namely 17AAG which was previously shown to inhibit Giardia growth with an IC50 of 711 nM [24] . As a pre-requisite we determined the IC50 of 17AAG during pre-encystation conditions ( Figure S2A in Text S1 ) . In the previously described two step protocol of encystation , we incorporated sub-lethal concentrations of 17AAG ( 0–1 µM ) on the last day of step1 ( pre-encystation ) and progressed with step 2 ( encystation ) as described under “materials and methods” . We scored for the number of cysts formed against total number of cells ( encystation efficiency ) at the end of encystation as described under “materials materials and methods” [40] . As shown in Figure 5A , cells treated with 17AAG on the last day of pre-encystation showed 60 folds higher efficiency as compared to the control cell ( DMSO treated ) . We confirmed that 17AAG induced cysts possess all the characteristic features of a mature cyst by examining expression of CWP1 by IFA . As shown in Figure 5B , 17AAG induced cysts showed characteristic staining for CWP1 antibody . The results indicated that down-regulation of GlHsp90 could be a potential trigger for encystation . We also attempted genetic approach for down-regulation of GlHsp90 using an antisense approach as explained under “materials and methods” . Upon selection , i . e , scoring for puromycin resistance , live parasites were obtained in vector control; however , no viable parasites were obtained in Hsp90 antisense ( data not shown ) . We confirmed that the stage transition was indeed a specific result of Hsp90 inhibition and not a general stress response , by carrying out encystation from trophozoites which have undergone other stressful conditions like drug pressure using metranidazole [41] , [42] , and thermal stress by heat shock [43] . As a preliminary study we determined the IC50 value of metranidazole in the pre-encystation condition as represented in Figure S2B in Text S1 . Under the similar conditions , encystation was set up and the treatment was carried out both at the last day of step 1 and the number of cysts were counted at the end of encystation . As represented in Figure 5C , the encystation ratio remained similar in all the concentrations of metranidazole used , suggesting that encystation in Giardia was not a general response to drug treatment . To understand whether or not thermal shifts had any role in encystation by modulating Hsp90 activity , we carried out encystation of the parasites exposed to heat shock and found that heat shock did not affect formation of cysts in these conditions ( Figure 5D ) . Both the above results indicate that the phenomenon of encystation might not be a general stress response in Giardia .
Hsp90 is an essential molecular chaperone in all eukaryotes studied so far . Many regulatory proteins like transcription factors and protein kinases are known to be dependent on Hsp90 for their activity and regulation . Hsp90 has been shown to possess none , one or sometimes multiple cis-spliced introns in most organisms studied . However , G . lamblia Hsp90 is coded by two ORFs that are spliced in trans [24] , [25] . The trans-splicing phenomenon is different from the classical trans-splicing described in Trypanosomes , wherein a leader sequence is spliced to an exon . In Giardia , messages from two independent ORFs get spliced in trans to form full-length Hsp90 mRNA . The resulting mature mRNA coding for full-length Hsp90 protein has a unique junctional sequence encoding 33 amino acids which is not present in either HspN or HspC ORFs . The junctional sequence so formed is contributed by the 5′ UTR of the HspC transcript , which is also the sequence covering the non-coding strand of a predicted Hypothetical ORF . This junctional peptide houses the catalytic argenine which is known to be essential for the ATPase activity of Hsp90 [44] . Previously we have predicted that the full length Hsp90 possess all the essential features in its primary structure for its function; however , no functional role was ascribed to the trans-spliced GlHsp90 . While the essential features of the trans-splicing reaction were described previously , the formal evidence for the junctional peptide sequence was not provided . Using proteomic approaches , in the present study , we have sequenced the junctional peptide present in between the HspN and the HspC sequence . The presence of the junctional peptide in the parasite is important for the functionality of the trans-spliced GlHsp90 as residues in this region are conserved and shown to play an important role in Hsp90 ATPase activity [44] . The Hsp90 transcript formed through trans-splicing possesses all the functional signatures for binding and hydrolysis of ATP . ATP hydrolysis is an essential step which fuels the Hsp90 chaperone cycle . As evident from our biochemical assays using purified Giardia Hsp90 , the trans-spliced Hsp90 bound cognate ligand ATP and its inhibitor 17AAG and GA with high efficiency . The binding affinity ( Kd ) was found to be 629 µM for ATP and 1 . 5 µM and 17 . 06 µM for GA and 17AAG respectively . The bound ATP was further hydrolyzed through the residues from the junctional peptide with catalytical efficiency of 4 . 4×10−5 min−1 . µM−1 . The biochemical parameters of GlHsp90 ATP hydrolysis were found to be similar to those of human Hsp90 ( 4 . 6×10−5 min−1 . µM−1 ) [12] . Trans-splicing of full length Hsp90 mRNA from two independent pre-mRNAs must require co-ordination between transcription of individual ORFs and their subsequent splicing . Bioinformatic analysis of upstream region of HspN and HspC revealed presence of independent promoters with HspC showing the presence of an additional Inr in comparison to HspN . Analysis of the transcripts by real time qRT-PCR confirmed this pattern with HspC transcripts showing 15 folds abundance over HspN . Hsp90 is known to be induced upon various stress conditions [1] . How would the presence of split Hsp90 gene affect its transcriptional upregulation in response to heat shock ? In addition to upregulating the individual HspN and HspC transcripts , their trans-splicing machinery will also have to cope with such increased levels of these precursor transcripts . Our qRT-PCR and western blot experiments from the heat stressed cells showed significant up-regulation of not only the precursors , HspN and HspC , but also the trans-spliced product GlHsp90 at transcript and protein levels . The increased levels of precursors are the substrates of spicelosomal machinery for the formation of full length Hsp90 . It is important to note that core spliceosomal components ( Table S2 ) show no significant change upon heat shock , as described in GiardiaDB . However , the spliceosomal machinery was able to cope with increased levels of precursors towards the formation of full length Hsp90 , required under stress . Previous reports from Leishmania , Plasmodium as well as Candida suggest an important link between Hsp90 function and stage transitions in these parasites . For instance , in Leishmania it has been shown that Hsp90 regulates stage transition from the promastigote ( insect stage ) to amastigote ( mammalian stage ) . Invasion of mammalian host by Giardia is marked by conversion of the parasite from environmentally resistant , latent cysts to actively growing trophozoites that causes pathogenesis of the disease . The excystation and encystation events in the parasite life cycle have been established in vitro through manipulation of pH and bile concentration in the growth medium; however , the molecular mechanism and triggers effecting these transitions have not been fully understood [22] , [45] . Analysis of GlHsp90 transcript and protein levels during trophozoite to cyst transition suggest that GlHsp90 may be involved in triggering this transition . Indeed inhibition of GlHsp90 function in actively proliferating trophozoites robustly induced their transformation to cysts . Specifically , 17AAG pre-exposed trophozoites successfully transformed to cyst forms upon encystation cues . Hsp90 induced stage conversion could not be reproduced with other pharmacological agent or environmental perturbation tested by us , suggesting that GlHsp90 may be mechanistically involved in this cellular transformation . Our efforts to genetically turn down GlHsp90 expression to validate this observation were unsuccessful as Giardia transfectants expressing antisense constructs of GlHsp90 did not remain viable . It must be emphasized that the induction of encystation upon treatment with Hsp90 inhibitors is dependent on the timing of its exposure to Giardia trophozoites . It was important to expose Giardia to Hsp90 inhibitors in the pre-encystation stage to see the robust induction of encystation . Exposure to Hsp90 inhibitors following pre-encystation , during encystation step did not result in any increase in the number of cysts . The observation rules out the toxicity or stress related effects of Hsp90 inhibitors and suggests that perturbation of Hsp90 activity through its inhibitors was priming Giardia trophozoites towards encystation . While the downstream events upon Hsp90 inhibition resulting in induction of encystation remain unclear , our study for the first time implicates this important chaperone in regulating stage transition on Giardia .
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Giardia lamblia is one of the most common causes of diarrhoea across the globe . The disease can result in fatalities especially in small children . The parasite is transmitted by contaminated food through faeco-oral route due to unhygienic habits . The parasite exhibits two stages during its lifecycle; namely cysts and trophozoites . Due to their environmentally resistant hardy nature cysts are transmitted through contaminated food into the human body . Upon entry into the human body they convert into active trophozoites and cause pathogenesis of the disease . In the course of infection within the host , some of the trophozoites convert back into cysts and are released in the environment through the faeces . The mechanisms and signals that convert the parasite from trophozoites to cysts are not yet known . Our study , for the first time , implicates heat shock protein 90 of the parasite in the conversion of trophozoites into cysts in the intestine of the infected human body . Hsp90 is famous for its ability to sense environmental changes and provide cues for stage-switch in related parasites . In addition to providing a glimpse into molecular mechanisms of stage inter-conversion , our results suggest potential new ways of treating this important human infection .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"spectrometric",
"identification",
"of",
"proteins",
"cellular",
"stress",
"responses",
"giardia",
"enzymes",
"cell",
"processes",
"enzymology",
"microbiology",
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] |
2014
|
Trans-spliced Heat Shock Protein 90 Modulates Encystation in Giardia lamblia
|
Genomic instability is a major driver of intra-tumor heterogeneity . However , unstable genomes often exhibit different molecular and clinical phenotypes , which are associated with distinct mutational processes . Here , we algorithmically inferred the clonal phylogenies of ~6 , 000 human tumors from 32 tumor types to explore how intra-tumor heterogeneity depends on different implementations of genomic instability . We found that extremely unstable tumors associated with DNA repair deficiencies or high chromosomal instability are not the most intrinsically heterogeneous . Conversely , intra-tumor heterogeneity is greatest in tumors exhibiting relatively high numbers of both mutations and copy number alterations , a feature often observed in cancers associated with exogenous mutagens . Independently of the type of instability , tumors with high number of clones invariably evolved through branching phylogenies that could be stratified based on the extent of clonal ( early ) and subclonal ( late ) instability . Interestingly , tumors with high number of subclonal mutations frequently exhibited chromosomal instability , TP53 mutations , and APOBEC-related mutational signatures . Vice versa , mutations of chromatin remodeling genes often characterized tumors with few subclonal but multiple clonal mutations . Understanding how intra-tumor heterogeneity depends on genomic instability is critical to identify markers predictive of the tumor complexity and envision therapeutic strategies able to exploit this association .
Cancer is a dynamic and ever-changing disease that mutates and evolves during its progression [1] . While the transformation from healthy to malignant cell is characterized by a few selected oncogenic alterations [2] , genomic instability is frequently observed in formed tumors , where it fuels the acquisition of novel molecular changes diversifying the cancer cell population [3] . As a result , each tumor is a composite of multiple clones , defined as groups of cells that are genetically identical within each group , but different among them [4] . Genomic instability has been long considered a major driver of intra-tumor heterogeneity . Multiple implementations of genomic instability have been identified and characterized in tumors [5] . These differ by type of genetic lesions being accumulated , e . g . somatic mutations [6] or copy number alterations [7] , as well as by the extent of time and space throughout the genome that is affected by these lesions [8] , [9] . Importantly , recent studies have reported diverse association between specific types of genomic instability and clinical outcome . In particular , chromosomal instability was found indicative of worse prognosis in lung adenocarcinoma and other diseases [10] , [11] , even though tumors with extreme mutational or chromosomal instability were reported having better prognosis than less altered tumors in multiple tumor types [6] , [12–14] . Genomic instability therefore encompasses diverse molecular phenotypes associated with distinct mutational processes and clinical outcome . Whether these phenotypes are associated with diverse extent and patterns of intra-tumor heterogeneity remains an outstanding question . Approaches based on single-cell profiles [15–18] or multiple biopsies of the same tumor [19–21] have revealed a daunting diversity among cancer cells . Unfortunately , single-cell analyses of tumors or profiling of multiple samples for each patient face technical and cost limitations , thus large scale datasets of these types are currently limited for systematic investigations . In response to these limitations , algorithmic approaches have been proposed to infer the clonal composition of a tumor from the genetic profile of a single sample [22–26] . Using such tools , different clonality and timing of emergence have been shown for specific therapeutically actionable mutations [27] and an association has been found between intra-tumor heterogeneity and patients’ prognosis [28] . Here , we used computational inference of intra-tumor heterogeneity to explore its association with genomic instability . Briefly , we collected data for 5 , 593 human cancer genomes from 32 tumor types profiled by The Cancer Genome Atlas ( TCGA ) ( S1 Table ) and inferred the clonal composition of each tumor from its repertoire of somatic mutations and copy number alterations . The resulting cohort of tumor clonal phylogenies allowed us to assess how intra-tumor heterogeneity depends on diverse forms of genomic instability and whether these are associated to specific genetic lesions or mutational signatures that can act as markers of the underlying tumor complexity .
To further characterize how intra-tumor heterogeneity emerged in our set of tumors , we explored their inferred phylogenies , i . e . the ensemble of clone-to-clone relationships that describe which clone descends from the others . Linear phylogenies are the result of the sequential generation of clones along the same lineage , i . e . the last clone is the product and summary of all its predecessors . Conversely , in branching phylogenies multiple clones spur from the same common ancestor , generating independent lineages that can evolve in distinct populations with little similarity from one another . Tumor phylogenies are typically combinations of linear and branching evolution and they can be represented as connected graphs or trees where clones are the nodes of the tree and two clones are connected if one descends from the other . According to this representation , the first emerged clone is the root of the tree , while clones emerging last without descendants are the leaves . Intuitively , the more branching a phylogeny , the closer each leaf will be to the root , conversely perfectly linear phylogenies will have only one leaf at the maximal possible distance from its root . We formalized this intuition and quantify each phylogeny with the following score: Treescore=1−1L∑ld ( l , root ) N−1 where L is the total number of leaves , N the total number of clones , and d ( l , root ) is the length of the path connecting a leaf l to the root of the tree . Based on this definition , all linear phylogenies will obtain a score equal to 0 , while the Tree score will increase with its degree of branching and number of branching nodes ( Fig 3A ) . Tree scores computed for our tumor cohort indicated that branching phylogenies were almost invariably observed as the number of clones increased . In tumor predicted having more than 5 clones , linear phylogenies or phylogenies with minimal branching ( i . e . linear phylogenies with only two branching leaves ) were almost never observed ( Fig 3B ) . Importantly , this association was independent of the type of genomic instability ( S4A Fig ) . Notably , as for the number of clones , genomically unstable tumor subtypes exhibiting high numbers of both copy number alterations and mutations were associated with higher Tree score than tumors with extreme numbers of exclusively one type of alteration ( S4B Fig ) . Within each tumor type , patients with Tree scores above the average did not show significantly different survival , except in 4 tumor types were a consistent trend was observed . Indeed , in all 4 cancer types , patients with high Tree scores exhibited on average 4 or more clones and were associated with better prognosis ( S5A–S5D Fig ) , consistent with previous observations made on a subset of the TCGA cohort [28] . A stratification of patients based on low ( <0 . 3 ) and high ( >0 . 6 ) Tree scores confirmed that patients with high Tree scores had higher median overall survival than patients with low Tree score in the majority of the tumor types ( S5E Fig ) . Branching phylogenies have been previously reported to be associated with the clonal expansion that characterizes tumor progression , rather than initiation [32] . To verify this association , we used a previously proposed mathematical model of tumor progression [33] based on two parameters: the mutation rate μ and fitness s ( see Methods ) . Briefly , at each iteration cells can either replicate or die with complementary probabilities that depend on the number of driver mutations k and the fitness parameter s ( the higher k and s , the higher the probability of replicating ) . Replicating cells will acquire a mutation with probability μ , and such mutation will be considered a driver with probability Kμ ( here K = 0 . 025 ) . Using this model , we simulated and characterized the evolution of approximately 40 , 000 simulated tumors spanning a wide range of evolutionary parameters ( μ and s ) ( see Methods ) . Observed number of clones and Tree scores of the simulated tumors were remarkably concordant with the inferred values in the human cohort ( Fig 3C ) , confirming that high intra-tumor heterogeneity emerging during exponential growth gives rise to branching phylogenies . The model that we adopted allows the emergence of mutations improving the cell fitness ( i . e . driver ) and , thus , it mimics tumor evolution under selection . However , it has been proposed that a fraction of human tumors displays features that can be explained exclusively by neutral evolution [34] . In our dataset , we detected samples with such features across all tumor types ( S5F Fig ) . Interestingly , in 8 distinct tumor types we found that tumors exhibiting features of neutral evolution had significantly higher Tree scores than tumors without such features ( Fig 3D ) , whereas the opposite association was never observed . This trend was confirmed in the pan-cancer cohort ( Fig 3E ) and suggests that neutral evolution could foster intra-tumor heterogeneity and the emergence of branching lineages . Tumor phylogenies allow to explore the temporal emergence of individual or groups of mutations . In particular , previous characterizations of tumor phylogenies have focused on the dichotomy between clonal and subclonal mutations [10] , [27] , [35] . Clonal mutations are present in all cancer cells and are typically considered early events . Subclonal mutations emerge later during tumor evolution and thus characterize only individual or subsets of clones . Starting from this premise , we quantified the number of clonal and subclonal mutations for each tumor in our dataset , and explored whether different types of genomic instability are themselves early or late emerging events . In our tumor phylogenies , clonal mutations are grouped in the root , which corresponds to the oldest detectable clone and it documents , at least in part , the previous history of the tumor . The root could either represent the first clone that underwent clonal expansion or the last one able to outcompete all previous clones , typically in association with an evolutionary bottleneck where cells undergo strong selective pressure ( e . g . therapeutic intervention or metastatic migration ) . Tumor types in our human dataset exhibited a variable average number of clonal mutations , with most of them ranging between 30 to 60% of their total number of mutations ( Fig 4A and S1 Table ) . Cancers that exhibited the highest extent of intra-tumor heterogeneity , such as lung , bladder , and stomach cancers , were characterized by high numbers of both clonal and subclonal alterations indicating that genomic instability is here emerging early and continues to evolve as the tumor progresses . An interesting exception was skin melanoma which was characterized by the highest number of clonal mutations , consistent with all of these samples being metastatic and not primary tumors ( Fig 4A ) . In this case , the root of the tumor phylogeny is likely to represent the clone that was able to migrate from an advanced primary tumor and seed the metastasis . Next , we explored whether the emergence and selection of genomic alterations were associated with the extent of clonal and subclonal mutations . First , we estimated within each patients which mutational processes ( or mutational signatures ) could explain the emergence of the observed patterns of mutations [36] , [37] . For each tumor type , we compared the numbers of clonal and subclonal mutations in patients exhibiting a given signature and in patients that did not ( Fig 4B , S2 Table ) . As expected , the UV light signature ( S7 ) was strongly associated with melanoma patients with high number of clonal mutations , consistent with these been mostly metastatic samples and characterized by high numbers of clonal events . Signatures characteristic of tumor subtypes with a high mutation load were associated with high numbers of both clonal and subclonal mutations . For example , lung and head neck cancer patients exhibiting a signature associated with tobacco smoking ( S4 ) had higher number of both clonal and subclonal mutations than patients with smoke unrelated tumors , even though in lung squamous-cell cancer only the different numbers of clonal mutations reached statistical significance ( FDR < 0 . 1 ) . Similarly , DNA repair deficiencies , such as double strand break repair ( DSB , S3 ) in breast and ovarian cancer , or mismatch repair ( MMR , S6 and S20 ) , in colorectal and stomach cancer were associated with higher number of both clonal and subclonal mutations . Interestingly , colorectal and stomach cancer patients exhibiting a signature of unknown etiology ( S21 ) but associated with microsatellite instability ( MSI ) had significantly higher numbers of clonal , but not subclonal , mutations than patients without such signature . Finally , patients exhibiting signatures of APOBEC-associated mutagenesis ( S13 and S2 ) had higher numbers of subclonal mutations in diverse tumor types , except for metastatic melanoma , consistent with this mutational process occurring late in tumor development [27] . Then , we performed a similar analysis to test whether the selection of ~500 cancer-associated mutations and copy number alterations [38] was associated with a high number of clonal or subclonal mutations ( S2 Table ) . Surprisingly , alterations that were associated with a higher number of subclonal , but not clonal , events , included for the most part copy number changes ( 67% ) , especially in sarcomas , breast and ovarian cancers , and TP53 mutations , in lung adenocarcinoma , low grade glioma , and breast cancer ( Fig 4C ) . Alterations associated with high number of clonal , but not subclonal , events were instead prevalently recurrent mutations ( 87% ) , mostly occurring in colorectal and stomach cancer , and skin melanoma . Interestingly , these mutations were enriched for events targeting chromatin remodeling factors such as SWI/SNF components PBRM1 , ARID2 , ARID1A , and ARID1B , lysine methyltransferase KMT2D , and histone acetyltransferase CREBBP ( Fig 4C ) . Finally , highly recurrent mutations in MSI tumors , such as those affecting RNF43 and BRAF in gastric cancers [39] , [40] were associated with high number of clonal and subclonal mutations , consistent with MSI tumors having a higher mutational load than micro-satellite stable tumors . Overall , mutational signatures and cancer-associated alterations further highlighted that distinct patterns of genomic instability are associated with different extents of intra-tumor heterogeneity .
Intra-tumor heterogeneity is intrinsically difficult to measure as a limited portion of a tumor is typically accessible for molecular analyses , providing only a static snapshot of a disease in constant evolution . Computational techniques can help to infer tumor progression , extract shared evolutionary patterns through the analysis and comparison of large-scale sample cohorts , and predict the missing pieces of an otherwise incomplete picture . Nonetheless , these approaches often have limited power , especially if relying only on whole-exome sequencing of single samples , they depend on sequencing coverage and mutation calling , and still mostly rely on genetic data to infer clonal diversity . Based on a simple comparison of different tools that were applied to the same tumors , we observed that results on individual cases are often inconsistent , however , trends derived from the whole set of samples were reproducible . In this study , we combined two different methods that used both mutation and copy number data to explore the association between intra-tumor heterogeneity and diverse forms of genomic instability . Surprisingly , tumors with the highest alteration burden were not found to be the most heterogeneous . Indeed , both mutational instability associated with DNA repair deficiencies and high chromosomal instability ( CIN ) were associated with less intra-tumor heterogeneity than instability associated with exogenous mutagens ( e . g . tobacco smoke and UV-radiation ) . In particular , the most heterogeneous tumors were those concurrently exhibiting high , yet not extreme , numbers of mutations and copy number alterations . This molecular phenotype was common in lung and skin melanoma , but also bladder , head and neck and CIN stomach cancer ( S2 Fig ) , and could represent a marker of high intra-tumor heterogeneity . Tumors likely undergo multiple phases of clonal expansions and diversification punctuated by evolutionary bottlenecks ( e . g . therapeutic intervention or nutrients depletion ) where only one or a few clones harbor the necessary molecular features to survive . Computationally inferred phylogenies from single samples are thus likely representative of one such phase , but cannot capture the whole evolutionary history of the disease . This was nicely evidenced by the analysis of metastatic skin melanoma , where the large numbers of clonal mutations likely resulted from the development and progression of heterogeneous primary tumors , out of which a clone was able to seed the metastasis . Within this context , the distinction between clonal and subclonal mutations , provided us with a simple but useful means to explore the early versus late emergence of genomic instability . However , tumor phylogenies were here inferred from a single sample from each tumor , hence mutations that appeared as clonal , might actually be only “locally clonal” , i . e . different regions of the same tumor might not exhibit such mutations or exhibit them only in a fraction of cells . Overall , tumors with greatest intra-tumor heterogeneity exhibited high numbers of both clonal and subclonal mutations , suggesting that genomic instability emerged early , but was sustained and fostered during tumor evolution . An interesting case was represented by gastric tumors with microsatellite instability ( MSI ) . MSI tumors are associated with mismatch repair deficiency , which has been associated with multiple signatures ( S6 , S20 , S15 , S21 , and S26 ) [41] . Nonetheless , the extent of clonal and subclonal mutations associated with these signatures were different , especially between signatures S6 and S21 ( Fig 4B ) , potentially suggesting the existence of distinct MSI subtypes associated with different mutational processes . On the other hand , we found that chromosomal instability characterized by multiple copy number changes and TP53 mutations , was often accompanied by multiple subclonal mutations confirming previous observations in glioma [42] and extending them to other tumor types . Moreover , amplification of TP53 inhibitory proteins MDM2 ( 12q15 ) and MDM4 ( 1q32 ) and deletion of the MDM2 inhibitor ARF ( CDKN2A , 9p21 ) exhibited a trend for being associated with high numbers of subclonal mutations in 8 tumor types ( p value < 0 . 1 , S2 Table ) . Interestingly , while TP53 mutations or alterations in the p53 pathway are invariably observed in chromosomally unstable tumors [31] , only few other mutations have been reported as recurrent in these tumor subtypes , suggesting these multiple subclonal events might be only a “passenger” byproduct of p53 deficiency . Targeted sequencing of cancer-associated variants is empowering clinicians with the ability to tailor therapeutic protocols to the genetic fingerprint of each tumor . These decisions however often rely on a single and potentially incomplete observation . While single-cell sequencing or multiple sampling of the same tumor are still for the most part unfeasible in the clinic , the identification of tumors at “high-risk” of intra-tumor heterogeneity could provide a means to better prioritize patients likely to benefit from additional analysis and profiling . Genomic instability has been often proposed as a major driver of intra-tumor heterogeneity and , thus , as a potential marker of its extent . Our study delved into the diverse implementations of such instability and characterized their potential to anticipate low or high intra-tumor heterogeneity . With a more comprehensive understanding of the risks and vulnerabilities posed by highly unstable genomes , strategies can be envisioned to exploit these phenotypes to control intra-tumor heterogeneity and enhance therapeutic response .
Molecular data for the tumor types analyzed in this study has been collected from the FireHose ( https://gdac . broadinstitute . org/ ) and cBioPortal ( Cerami et al . , 2012 ) ( http://www . cbioportal . org/ ) data repositories for The Cancer Genome Atlas ( TCGA ) . Mutation files ( MAF format ) and copy number segmentation files used for the analyses in this manuscript are available at https://zenodo . org/record/1404658# . W4VNVJMzbOQ . Reported numbers of mutations per sample ( S1 Table ) include all variants , reported numbers of copy number alterations correspond to the number of segment with copy number value > 0 . 3 ( gain ) or < -0 . 3 ( loss ) . To model cancer evolution , we rely on the model proposed by Bozic et al . [33] . This model is a discrete time Galton-Watson branching process in which cells can at each time step either replicate ( with a probability b ) or die ( with a probability d ) . During the replication , one of the two daughter cells can acquire a new alteration with a probability μ . If an alteration occurs , this can be of two types: a driver alteration confers to the cell a selective advantage by reducing its probability to die , while a passenger mutation has a neutral effect . The probability to die of a cell i that has accumulated k driver mutations , dki is given by: dik=12 ( 1−s ) k where s is the fitness parameter . According to the previous equation , the replication probability for the cell i with k mutations is bki = 1 − dki . μ and s are the input parameters of the model and remain the same during the simulation and for all cells . The probability to die will change during the simulation depending on the number of accumulated driver alterations . Given the available mutation data for human samples is limited to the exome , we estimated the mutation rate across multiple tumor types by assessing the number of mutations per nucleotide of the coding genes in the TCGA cohort . In our dataset , the number of mutations per nucleotide ranged between 7x10-8 to 10−4 ( assuming an exome length equal to 6x107 , corresponding to ~2% of the genome length ) . Accordingly , we generated simulation with μ ∈ [10−7 − 10−3] , which covers the estimated range in human tumors allowing for even higher mutation rate values . Similarly , variable fitness values have been previously proposed ranging between 0 . 0001 and 0 . 1 [43] , [44] . In our simulations we reflected this variability setting s ∈ [10−4 − 5x10-1] . Finally , the probability for a new mutation to be a driver is defined as μ x K , with K = 0 . 025 , chosen based on an estimation of 500 cancer associated alterations ( e . g . as in COSMIC Cancer Census: http://cancer . sanger . ac . uk/census ) . In our analyses , after each replication step , if no alteration has occurred then the two daughter cells will remain in the same clone , otherwise the sibling with the new alteration will create a new clone . Importantly , a new clone is formed whether the new alteration is a driver or a passenger . To calculate the mean number of clones and Tree score , only clones with a number of cells greater or equal to 1% of the total population are retained . This is in accordance with the fraction of sequencing reads typically required by cancer exome sequencing studies to retain a somatic mutation ( S7 Fig ) . The model of clonal evolution is implemented in Python , using the ETE environment .
|
Cancer is characterized by cells accumulating molecular alterations promoting specific phenotypic features , such as uncontrolled proliferation and survival . Cancer cells sometimes exhibit a high number of such alterations , often driven by defects of the DNA repair pathway or by external mutagens , such as tobacco smoking or UV-radiation . Highly altered cells are termed genomically unstable . A major consequence of genomic instability is that a single tumor is often composed by cells that have accumulated distinct alterations . This diversity is termed intra-tumor heterogeneity and represents a critical clinical challenge . In this study , we examined how different forms of genomic instability are associated with intra-tumor heterogeneity . We inferred intra-tumor heterogeneity in ~6000 human tumors and found that tumors with extreme mutational or chromosomal instability were not the tumors with the highest number of clones . Instead , tumors harboring both mutational and chromosomal alterations were the most diverse . Furthermore , we identified specific genetic fingerprints that are associated with early and/or late genomic instability . These results show that cancer genomic instability does not necessarily lead to high intra-tumor heterogeneity and , importantly , they provide markers to recognize when it does .
|
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2018
|
Pan-cancer inference of intra-tumor heterogeneity reveals associations with different forms of genomic instability
|
In the nematode Caenorhabditis elegans , cholinergic motor neurons stimulate muscle contraction as well as activate GABAergic motor neurons that inhibit contraction of the contralateral muscles . Here , we describe the composition of an ionotropic acetylcholine receptor that is required to maintain excitation of the cholinergic motor neurons . We identified a gain-of-function mutation that leads to spontaneous muscle convulsions . The mutation is in the pore domain of the ACR-2 acetylcholine receptor subunit and is identical to a hyperactivating mutation in the muscle receptor of patients with myasthenia gravis . Screens for suppressors of the convulsion phenotype led to the identification of other receptor subunits . Cell-specific rescue experiments indicate that these subunits function in the cholinergic motor neurons . Expression of these subunits in Xenopus oocytes demonstrates that the functional receptor is comprised of three α-subunits , UNC-38 , UNC-63 and ACR-12 , and two non–α-subunits , ACR-2 and ACR-3 . Although this receptor exhibits a partially overlapping subunit composition with the C . elegans muscle acetylcholine receptor , it shows distinct pharmacology . Recordings from intact animals demonstrate that loss-of-function mutations in acr-2 reduce the excitability of the cholinergic motor neurons . By contrast , the acr-2 ( gf ) mutation leads to a hyperactivation of cholinergic motor neurons and an inactivation of downstream GABAergic motor neurons in a calcium dependent manner . Presumably , this imbalance between excitatory and inhibitory input into muscles leads to convulsions . These data indicate that the ACR-2 receptor is important for the coordinated excitation and inhibition of body muscles underlying sinusoidal movement .
Acetylcholine activates ligand-gated ion channels in muscles and is a major neurotransmitter in the brain , modulating a variety of cognitive and addictive behaviors [1] . Ionotropic acetylcholine channels result from the assembly of five individual subunits . Each subunit has four membrane-spanning domains , with the second transmembrane ( TM2 ) domain lining the pore of the channel . Subunits assemble to form a pentameric channel; some subunits , such as α7 , can form homopentameric channels [2] , but most receptors are heteromeric [3] . For example , the well-studied muscle receptor in mammals contains two ligand-binding α subunits and three non–α-subunits [4] , [5] . By contrast , the composition and expression pattern of most neuronal acetylcholine receptors have not been well defined . The subunit composition of a channel and the identity of the pore-lining residues are crucial for ion selectivity , gating , desensitization , ligand affinity , and pharmacology . Because of the diversity of acetylcholine receptor subunits and promiscuous assembly under nonnative conditions , it remains a major challenge to define the in vivo compositions and , consequently , the cell-specific functions of acetylcholine-gated channels . The genome of the nematode C . elegans encodes 29 acetylcholine receptor subunits [6] , [7] . The most well-studied receptor is the levamisole-sensitive receptor expressed in the body muscle . The levamisole-sensitive receptor is composed of three α-subunits , UNC-38 , UNC-63 , and LEV-8 , and two non–α-subunits , UNC-29 and LEV-1 [7]–[13] . This receptor functions as the main excitatory postsynaptic receptor at neuromuscular junctions . Genome-wide transgene expression studies indicate that a large number of acetylcholine receptor subunits are expressed in neurons [14] . However , candidate null mutations for many acetylcholine receptor subunits cause few discernable defects . The roles and compositions of most neuronal acetylcholine receptors remain unknown , and reconstitution experiments have not been performed . In this study , we identify all five subunits of a neuronal acetylcholine receptor and characterize its function in a neural circuit controlling C . elegans locomotion . Our approach relied on a mutant strain exhibiting severe convulsions due to overstimulation of the muscles . Molecular characterization demonstrated that this mutant strain possessed an activating mutation in an acetylcholine receptor subunit , ACR-2 . We identified the other components of the ACR-2 receptor by screening for second-site mutations that ameliorated the convulsive phenotype . Combined with cell-type expression studies and receptor reconstitution in Xenopus oocytes , these data led to a complete description of the subunit composition of a neuronal acetylcholine receptor . We further demonstrated that the ACR-2 receptor functions to maintain excitability of the cholinergic neurons , by recording synaptic activity in the null and activated mutants . The hyperactivating ACR-2 mutation leads to enhanced neurotransmitter release from the cholinergic motor neurons , and intriguingly , an inactivation of the GABAergic motor neurons that receive inputs from the cholinergic motor neurons . The imbalance in the excitation and inhibition within the motor circuit disrupts coordinated body muscle contraction .
C . elegans crawls by generating a sinusoidal wave that is propagated from the head to the tail . These contractions are generated by acetylcholine released from ventral cord motor neurons . The cholinergic motor neurons form dyadic synapses , simultaneously innervating the musculature and GABAergic motor neurons [15] . The GABAergic motor neurons form neuromuscular junctions on the opposite side of the animal and thereby relax muscles on the opposite side of the body . In the absence of GABA neurotransmission C . elegans hypercontract or “shrink” when stimulated to move by gentle touch [16] . We isolated the mutation n2420 in a screen for mutants that shrink in response to gentle touch . However , n2420 worms have an additional phenotype not expressed by classic shrinker mutants: they shrink spontaneously , referred to here as a “convulsion” ( Video S1 ) . Thus , the mutant combines the classic phenotype of a loss of GABA function with spontaneous activation of muscle contraction . The convulsion phenotype is recessive , although n2420 is a semidominant gain-of-function mutation ( described below ) . We mapped n2420 between lon-2 and unc-6 on chromosome X ( Figure 1A ) . Microinjection of the cosmid C46C10 rescued both the spontaneous convulsions and uncoordinated behavior of acr-2 ( n2420 ) animals . The rescuing activity was further narrowed to a 10-kb DNA fragment containing the acr-2 gene ( Figure 1B ) . The acr-2 gene encodes a 580–amino acid subunit of an ionotropic acetylcholine receptor [17] . The predicted genomic organization was confirmed by the sequences of four cDNAs that correspond to the acr-2 locus ( see Materials and Methods ) . acr-2 is the upstream gene in an operon , with the closely related gene acr-3 immediately downstream ( Figure 1B ) [18] . ACR-2 and ACR-3 are non–α-subunits in the heteromeric receptor clade . They are closely related to the UNC-29 and LEV-1 subunits of the levamisole-sensitive receptor and more distantly to the vertebrate heteromeric receptor subunits ( Figure 1C ) . The molecular lesion of acr-2 ( n2420 ) is consistent with its being a gain-of-function mutation in the ion channel . The mutation in n2420 results in a valine 309 to methionine substitution , which is at the 13′ position in the pore-forming TM2 domain of the ACR-2 subunit ( TM2 numbering scheme as in [19] ) ( Figure 1D ) . The 13′ position is thought to line the pore and contribute to ion selectivity [20] . A similar mutation at the 13′ position leads to neurodegeneration in the neuronal acetylcholine receptor DEG-3 [21] . A corresponding mutation in the β-subunit of the muscle acetylcholine receptor is found in human patients with myasthenia gravis [22] . This substitution generates a receptor with increased sensitivity to acetylcholine , prolonged open times , and spontaneous activity in the absence of acetylcholine [22] . The pharmacological responses of the n2420 strain are consistent with an activation mutation in an acetylcholine receptor . The convulsions were reversibly suppressed by mecamylamine ( Figure 2A , Video S2 ) , a noncompetitive open-channel blocker [23] . The mutant animals were hypersensitive to aldicarb ( Figure 2B ) , an acetylcholinesterase inhibitor that prolongs endogenous acetylcholine stability in the synaptic cleft [7] . acr-2 ( n2420 ) animals were also hypersensitive to the acetylcholine agonist levamisole ( Figure 2C ) , which activates a class of acetylcholine receptors expressed on nematode muscles . The hypersensitivity to both aldicarb and levamisole has been observed in several mutants that exhibit an increased level of acetylcholine release and a reduced level of GABA release onto the muscle [24] . The n2420 mutation genetically behaves as a weak semidominant mutation . Heterozygous n2420 animals ( n2420/+ ) are slightly uncoordinated when moving backward . The dominant phenotype is not caused by haploinsufficiency of this interval , since hemizygous animals carrying a deficiency of the region in trans to a wild-type chromosome ( +/Df ) move normally ( see Materials and Methods ) . Nevertheless , the convulsion phenotype of n2420 mutants is recessive ( Figure 2D ) . Homozygotes of n2420 contract spontaneously at a frequency of 9±1 convulsions per minute , whereas heterozygotes of n2420/+ do not exhibit convulsions . In fact , animals that are heterozygous for acr-2 ( n2420 ) and a deficiency of the region ( n2420/Df ) ( see Materials and Methods ) or a null mutation ( Figure 2D and described below ) also lack spontaneous convulsions , indicating that the mutation causes altered gene function [25] . The convulsion behavior is not observed in young larvae and is first visible at the L3 stage ( Figure 2E ) . To determine the site of action of acr-2 , we constructed transcriptional reporter genes by placing the fluorescent proteins GFP or mCherry under the control of a 3 . 5-kb or a 1 . 8-kb acr-2 promoter region ( see Materials and Methods ) . Both promoters drove expression predominantly in the neurons of the ventral cord from L1 larvae to adults ( Figure 3A ) . Based on their birth times , numbers , positions , and axon morphologies , these neurons were determined to be cholinergic motor neurons of the VA , VB , DA , and DB classes , and not the AS and VC classes [26] . GFP expression driven by the longer acr-2 promoter was frequently seen in the PVQ and DVC neurons in the tail and was infrequently observed in a few head neurons ( Figure 3A ) . Coexpression with reporter genes for GABAergic neurons and interneurons confirmed that the acr-2 transcriptional reporters were not expressed in the ventral cord GABAergic motor neurons or in the interneurons expressing glr-1 or nmr-1 ( Figure 3B ) . The acr-2 ( n2420gf ) convulsion defect was not rescued when we expressed the ACR-2 protein in the GABAergic neurons ( Figure 3C ) . Expression of an acr-2 mini-gene in which the genomic sequences from exon 2 through 3′ UTR was replaced by acr-2 cDNA , driven under the 1 . 8-kb short promoter , could rescue the convulsion defect to a similar degree as the full-length acr-2 ( Figure 3C ) . These data suggest that ACR-2 functions in the cholinergic ventral cord motor neurons . We next sought to obtain null mutations in acr-2 by performing a genetic screen for suppressors of acr-2 ( n2420gf ) convulsions ( see Materials and Methods ) . We identified 30 suppressors that exhibited dominant suppression of the convulsion ( Figure 2D ) . They were mapped to the X chromosome and were found to be tightly linked to acr-2 ( n2420gf ) . Sequencing of the DNA revealed that 26 of these linked suppressors contained mutations in the acr-2 gene itself ( Figure 4A , Table S1 ) . Mutations in the other four linked suppressor strains have not yet been identified in the acr-2 or in the immediately downstream acr-3 open reading frames . The identification of acr-2 intragenic second-site suppressor mutations further supports the conclusion that acr-2 ( n2420gf ) results in a hyperactive receptor . The analysis of the molecular lesions in the acr-2 intragenic mutations indicates that eight are likely to be strong lossof-function or null mutations , since they introduce stop codons or alter splice junctions that would result in truncated or nonfunctional products . For example , two independent isolates ( n2595 and n2651 ) introduce an opal stop at Trp175 about halfway through the extracellular domain ( Figures 4A and S1 ) , likely representing null mutations in acr-2 . Among the other 16 intragenic revertants with amino acid substitutions , 13 affect amino acid residues in the extracellular domain ( Figure 4A ) . Some of these mutations alter candidate ligand-binding residues ( such as n2603 and n2581 ) , consistent with the idea that these intragenic revertants abolish the activity of acr-2 ( n2420gf ) . Two intragenic mutations ( n2594 and n2604 ) are located in the M2-M3 linker , a region that affects gating of acetylcholine receptors [27] . The acr-2 loss-of-function mutants are healthy but exhibit slightly sluggish locomotion . The speed of young adult animals was determined using a worm-tracking system . The average velocity of acr-2 ( n2595 n2420 ) worms was reduced by 28% compared to that of the wild type ( Figure 4B ) . This defect was not caused by background mutations , since this phenotype could be rescued by a wild-type transgene . We also obtained a deletion allele acr-2 ( ok1887 ) that removes the 5′ region of the gene ( Figure 4A ) . By movement , acr-2 ( ok1887 ) was indistinguishable from acr-2 ( n2595 n2420 ) . Both acr-2 ( n2595 n2420 ) and acr-2 ( ok1887 ) mutants were fully sensitive to levamisole , the muscle receptor agonist , but were moderately resistant to aldicarb ( Figure 2B and 2C ) . The overall axon morphology and synapses of the cholinergic motor neurons are grossly normal in acr-2 ( lf ) animals ( unpublished data ) . These data suggest an impairment in acetylcholine release in acr-2 null mutants . To determine the nature of the synaptic defect in acr-2 null mutants , we performed patch-clamp recordings of the muscle under voltage-clamp conditions . Acetylcholine release was monitored by quantifying miniature postsynaptic currents ( “minis” ) . Each miniature current is caused by neurotransmitter release from a single , or very few , synaptic vesicles from a motor neuron . Under standard conditions , acetylcholine and GABA currents are isolated pharmacologically [28]; however , we wished to preserve interactions between cholinergic and GABAergic motor neurons , so we used recording conditions in which these inputs into muscles could be distinguished without resorting to the use of drugs . Specifically , intracellular solutions were used in which the equilibrium potential for chloride was −59 mV , and the equilibrium potential for cations was 0 mV ( see Materials and Methods ) . At −60 mV , acetylcholine-induced minis would be inward currents , and GABA-induced minis not distinguishable , whereas at −10 mV , GABA-induced minis would be outward currents , and acetylcholine-induced minis would be very small inward currents . In the presence of 2 mM calcium in the external solution , the frequency of acetylcholine minis was reduced to 60% in acr-2 ( n2595 n2420 ) mutants and 62% in acr-2 ( ok1887 ) mutants compared to that of the wild type ( Figures 4C , 4D , S3A , and S3B ) . In addition , GABA mini frequency was reduced to 70% in acr-2 ( n2595 n2420 ) and 67% in acr-2 ( ok1887 ) mutants compared to the wild type , although this reduction did not reach significance ( Figures 4C , 4D , S3A , and S3B ) . The amplitude of minis remained unchanged in both mutants ( Figures S2 and S3C ) . These results indicate that in the absence of acr-2 function , neurotransmission from the cholinergic motor neurons is impaired , leading to a weak defect in locomotion . The cholinergic motor neurons form synapses onto the GABAergic motor neurons [15] . The mild effect on GABA neurotransmission might be an indirect effect of reduced excitation from the cholinergic motor neurons . Since loss of acr-2 function causes a decrease in cholinergic motor neuron activity , then it is likely that the gain-of-function mutation hyperactivates cholinergic neurons . The hypersensitivity of acr-2 ( n2420gf ) animals to aldicarb ( Figure 2B ) is consistent with this idea . Our recordings of endogenous mini currents in the dissected preparation supported this prediction , but we also found that hyperactivity of the cholinergic neurons was highly sensitive to calcium . We recorded the frequency of acetylcholine-induced mini currents at three concentrations of calcium: 0 . 5 mM , 2 mM , and 5 mM . At 0 . 5 mM calcium , the acetylcholine mini frequency in acr-2 ( n2420gf ) animals was 150% compared to the wild type ( Figure 5A ) , demonstrating that the altered pore domain of this mutant receptor caused an increase in activity in cholinergic neurons . In 2 mM extracellular calcium , the frequency of acetylcholine minis in acr-2 ( n2420gf ) was not increased but was similar to the wild type ( Figure 5B ) . At 5 mM calcium , mini frequency in the acr-2 ( n2420gf ) cholinergic neurons was reduced to 30% compared to the wild type ( Figure 5C ) . Thus , in acr-2 ( n2420gf ) animals , the cholinergic neurons are more active than those in the wild type at low levels of calcium , but these motor neurons are inhibited at very high levels of calcium in the mutant . No discernable abnormalities in morphology and synapses of the cholinergic motor neurons were observed in acr-2 ( n2420gf ) animals ( Figure S4 ) . Hyperactive acetylcholine neurotransmission is a likely cause of the spontaneous muscle contractions observed in the gain-of-function acr-2 mutant . In addition to spontaneous muscle contraction , acr-2 ( n2420gf ) mutants behave as though they are impaired for GABA transmission , that is , they shrink when touched . The shrinking behavior could be the result of GABAergic neuron developmental defects [29] . Neurogenesis and axon morphology were normal based on GFP expression in the GABAergic neurons ( unpublished data ) . Synaptic development was assayed by quantifying synaptic varicosities in the GABAergic motor neurons . Synaptic vesicle clusters were marked by expressing GFP-tagged synaptobrevin in the GABAergic neurons and fluorescent puncta along the ventral cord were counted . The number of fluorescent clusters was similar in acr-2 ( n2420gf ) animals and the wild type ( Figure S5A ) , suggesting that there is no gross morphological defect of GABA neuromuscular junctions . We assayed synaptic function from GABA neuromuscular junctions by recording minis in the muscles of acr-2 ( n2420gf ) mutants . Our electrophysiological recordings revealed a reduction in GABA neurotransmission in the gain-of-function mutant . However , unlike cholinergic motor neurons , the activity of GABAergic motor neurons was reduced at all concentrations of calcium tested . At 0 . 5 mM calcium , the mini frequency from GABAergic neurons was only slightly reduced in acr-2 ( n2420gf ) animals , 78% compared to the wild type ( Figure 5A ) . But at 2 mM or 5 mM calcium , the mini frequency from GABAergic motor neurons was reduced to about 15% ( Figure 5B and 5C ) . This reduction in neurotransmission from GABAergic motor neurons is consistent with the shrinking behavior observed in these animals . In addition to a reduction in activity from GABAergic motor neurons , the shrinking behavior could be caused by a reduction in postsynaptic sensitivity to GABA release . We therefore assayed muscle sensitivity to exogenous GABA to determine whether postsynaptic GABA responses are normal in acr-2 ( n2420gf ) mutants . We pressure-ejected GABA ( 100 µM ) onto body muscle cells and recorded currents from muscle cells of wild-type or acr-2 ( n2420gf ) animals . GABA-evoked outward currents were similar in mean amplitude in the mutant compared to the wild type ( Figure S5B ) , suggesting that GABA receptors are expressed and functional on the membrane of the muscle . To further determine whether the receptors are localized to synapses , we measured GABA-mediated mini amplitudes , which is a measure of the number of receptors activated by the release of GABA from synaptic vesicles at neuromuscular junctions . Mini amplitudes were normal in acr-2 ( n2420gf ) mutants ( ) , suggesting that clustering and function of postsynaptic GABA receptors are normal in acr-2 ( n2420gf ) mutants . From these results , we inferred that postsynaptic response to GABA is normal , but that neurotransmitter release from the GABAergic motor neuron is impaired in acr-2 gain-of-function worms . Because acr-2 is not expressed in GABAergic neurons , depression of neurotransmission in GABAergic neurons is likely to be an indirect effect of hyperactivated cholinergic neurons expressing acr-2 . ACR-2 is a non–α-subunit and must interact with other subunits to form a functional receptor . To identify the partners of acr-2 , we analyzed extragenic suppressor mutations of acr-2 ( n2420gf ) ( see Materials and Methods ) . Ten suppressor mutations were linked to chromosome X and fully suppressed convulsions of the acr-2 ( n2420gf ) strain . The mutants exhibited no obvious movement defects when separated from the acr-2 gain-of-function mutation . We mapped one of these mutations to a region between +11 . 80 and +12 . 93 ( see Materials and Methods ) . Within this interval is the acr-12 gene , which encodes an acetylcholine receptor α-subunit that is most similar to the LEV-8 ( ACR-13 ) acetylcholine receptor subunit ( Figure 1C ) . Analysis of the DNA sequence revealed that all ten suppressors contained missense or nonsense mutations in acr-12 ( Figure 6A ) . An acr-12 ( ok367 ) deletion mutation that removes a large part of the protein also fully suppressed acr-2 ( n2420gf ) convulsion ( Figure 6A and 6C ) . Furthermore , microinjection of a genomic fragment containing the wild-type acr-12 gene into the suppressed acr-2 ( n2420gf ) acr-12 ( ok367 ) strain restored the convulsive phenotype in transgenic animals ( Figure 6C ) , thereby confirming the identification of the acr-12 gene as the suppressor locus . Nine extragenic suppressor mutations caused sluggish movement when separated from acr-2 ( n2420gf ) , and the mutant animals were resistant to the acetylcholine agonist levamisole ( Table S1 ) . These mutations fully suppressed the convulsions of acr-2 ( n2420gf ) mutants ( Figure 6B ) , but the double mutants remained uncoordinated and resistant to levamisole ( Table S1 , and unpublished data ) . Genetic mapping , complementation tests and DNA sequence analyses demonstrated that these nine mutations were alleles of two α-subunits , unc-38 and unc-63 , and of two genes required for transport of acetylcholine receptors to the cell surface , unc-50 and unc-74 ( Table S1 ) [8] , [30] ( D . Williams and E . M . Jorgensen , unpublished data ) . Multiple lines of evidence indicate that the cellular focus of the ACR-2 and the other acetylcholine receptor subunits is in the cholinergic motor neurons and not in the muscles . First , the other subunit genes that contribute to the levamisole-sensitive receptor in the muscle , lev-8 , unc-29 , and lev-1 , were not identified in the suppressor screen , and mutations in these genes indeed did not suppress the acr-2 ( n2420gf ) phenotype in double mutants ( Figure 6B ) . Second , mutations in the nicotine-sensitive muscle receptor acr-16 did not suppress acr-2 ( n2420gf ) ( Figure 6B ) . Third , specific expression of unc-63 cDNA in cholinergic neurons driven by the 1 . 8-kb acr-2 promoter , but not in muscles ( by the myo-3 promoter ) or in GABAergic neurons ( by the unc-25 promoter ) , restored convulsions in unc-63 ( lf ) ; acr-2 ( n2420gf ) double mutants ( Figure 6C ) . Fourth , acr-12 is expressed in neurons , but not muscles [31] , [32] . Specific expression of acr-12 in the cholinergic motor neurons , but not GABAergic motor neurons , restored convulsions in acr-2 ( n2420gf ) acr-12 ( ok367 ) double mutants ( Figure 6C ) . Last , we tested mutations in other neuronally expressed acetylcholine receptor subunits , including acr-5 , acr-9 , acr-14 , and acr-19 , and found that none of them suppressed acr-2 ( n2420gf ) ( Figure 6B and unpublished data ) . Together , these results support the conclusion that UNC-38 , UNC-63 , ACR-12 , and ACR-2 are components of a receptor that functions in the cholinergic motor neurons . To further verify the subunit composition of the ACR-2–containing receptor ( referred to as ACR-2R ) and characterize its pharmacology , we performed reconstitution experiments using Xenopus oocytes . Previous attempts to reconstitute C . elegans levamisole-sensitive acetylcholine receptors in Xenopus oocytes demonstrated that the requirements for functional expression in vitro recapitulate genetic requirements in vivo [13] . Specifically , the ancillary proteins UNC-50 and UNC-74 , and to a lesser extent RIC-3 , which are involved in the assembly and trafficking of levamisole-sensitive acetylcholine receptors in worms [33] , are required for function of levamisole-sensitive acetylcholine receptors in oocytes . The finding that loss-of-function mutations in unc-50 and unc-74 suppress acr-2 ( n2420gf ) suggests that these two ancillary proteins also function in ACR-2R assembly and trafficking . ric-3 animals were not identified in the acr-2 ( n2420gf ) suppressor screen , likely because these animals are severely uncoordinated and unhealthy . We therefore constructed ric-3; acr-2 ( n2420gf ) double mutants and found that the convulsion frequency was dramatically reduced ( Figure 6B ) , indicating a requirement of ric-3 for acr-2 ( n2420gf ) function . Consequently , we coinjected cRNAs for acr-2 , acr-12 , unc-38 , and unc-63 , together with unc-50 , unc-74 , and ric-3 cRNAs at equal molar ratios ( see Materials and Methods ) . This experiment yielded little or no current ( Figure 7D ) , suggesting that a factor was missing . Since the closely related acetylcholine receptor subunit acr-3 is part of the acr-2 operon ( Figure 1B and 1C ) , it is likely that the ACR-2 and ACR-3 subunits are coexpressed in the same cells . When the acr-3 cRNA was added to the previous injection mix , robust expression of an acetylcholine-gated ion channel was observed ( Figure 7A and 7D ) . Pharmacological characterization demonstrated that the ACR-2R channel was weakly activated by nicotine and DMPP , and almost completely insensitive to levamisole or choline , and was efficiently blocked by mecamylamine ( Figure 7A and 7C ) . The estimated median effective concentration ( EC50 ) of the ACR-2R receptor was 14 . 1±1 . 2 µM , and the Hill coefficient was 1 . 25±0 . 12 ( Figure 7E ) . To further analyze the impact of the acr-2 ( n2420gf ) mutation on receptor physiology , we replaced the wild-type cRNA of acr-2 with a cRNA carrying the n2420 mutation , and analyzed the mutant receptor ( referred as ACR-2 ( V13′M ) R ) . Introduction of this point mutation caused a 14-fold increase in current compared to the wild-type ACR-2 subunit ( Figure 7D ) . The pharmacological profile of the ACR-2 ( V13′M ) receptor was also modified: 1 ) response to 100 µM DMPP was strongly increased , and 2 ) choline and levamisole caused modest receptor activation ( Figure 7B and 7C ) . However , the acetylcholine EC50 was not significantly changed ( 17 . 8±1 . 4 µM; Figure 7E ) , and no leak current could be recorded . The ACR-2 ( V13′M ) receptor remained fully blocked by mecamylamine , in agreement with the suppression of convulsions of acr-2 ( n2420gf ) by this drug ( Figure 2A ) . acr-3 was required in oocytes for expression of ACR-2R receptors , but acr-3 was not identified as an extragenic suppressor of acr-2 ( n2420gf ) . Moreover , a loss-of-function mutation in acr-3 did not affect the convulsion behavior caused by transgenic expression of acr-2 ( V13′M ) ( Figure S6 ) . Hence , we analyzed the properties of a putative ACR-2 ( V13′M ) receptor missing ACR-3 . Removing acr-3 cRNA from the injection mix only partially reduced the average current size , which remained almost 4-fold higher than what we observed with the full complement of wild-type subunits ( Figure 7D ) . This finding likely explains why eliminating acr-3 in an acr-2 ( n2420gf ) gain-of-function background would not lower the convulsion phenotype enough for it to be identified in our suppressor screen . In summary , our oocyte reconstitution studies identified the complete molecular composition of ACR-2 channel and demonstrated that it is a bona fide acetylcholine receptor .
The ACR-2–containing acetylcholine receptor in neurons is closely related to the levamisole-sensitive acetylcholine receptor that functions in C . elegans body muscle . Both receptors contain five distinct subunits , including three α- and two non–α-subunits . The UNC-38 and UNC-63 α-subunits are common to both receptors , yet the pharmacological profiles of the two receptors are very distinct ( Table 1 ) . ACR-2R receptors are slightly more sensitive to acetylcholine than levamisole-sensitive receptors , with an EC50 of 14 µM as compared to 26 µM , respectively ( this study and [13] ) . Strikingly , levamisole has no effect on the ACR-2R neuronal receptor but potently activates the levamisole-sensitive muscle receptor . Nicotine weakly activates ACR-2R receptor but inhibits the levamisole receptor ( this study and [13] ) . In ionotropic acetylcholine receptors , agonist binding sites are formed at the interface between the ( + ) side of an α-subunit and the ( − ) side of the adjacent subunit [4] , [5] . Binding sites for noncompetitive agonists and antagonists have also been identified at the non-α ( + ) /α ( − ) subunit interface [34] . If UNC-38 and UNC-63 are not adjacent within receptor pentamers , the unique subunits ( Table 1 ) will modify the complementary surface of each binding site and change the binding pocket and transduction residues for these drugs . The gain-of-function mutation in acr-2 changes a valine to a methionine at the 13′ position of the pore-forming transmembrane domain of the ACR-2 subunit . The 13′ position is in the upper half of the lumen and faces the pore [20] . A valine residue at this position is highly conserved in acetylcholine receptors , suggesting that it is important for proper receptor function . A V13′M mutation in the β1-subunit of the human muscle acetylcholine receptor causes myasthenia gravis [22] . When expressed in HEK cells , the receptors containing the β1 ( V13′M ) subunit exhibited higher acetylcholine affinity than the wild-type receptor [22] . Single-channel recording indicated that the mutant channel had longer open times and spontaneous openings . Patients with the hyperactive receptor displayed progressive degeneration of muscle end-plates that characterizes the myasthenic syndrome . The importance of the valine residue at position 13′ was also investigated in the chick α7-subunit . This subunit forms a homomeric nicotinic receptor that can be efficiently expressed in Xenopus oocytes . Mutating this valine into a threonine causes an almost 10-fold increase in the mean current amplitude and a 100-fold increase in the acetylcholine affinity [35] . Since this mutation affects a residue facing the pore lumen , increased current amplitude could arise simply from changed channel conductance . However , the single V13′T point mutation in the α7-subunit has pleiotropic effects . Specifically , multiple channel conductances were identified; moreover , the competitive antagonist DHβE was converted into a partial agonist [35] . These effects were interpreted as a change in the allosteric states of the channel as it transitions to the desensitized state [36] . The V13′M mutation in the ACR-2 subunit causes similar defects . Recording of ACR-2R expressed in Xenopus oocytes shows that mutating this valine caused a dramatic increase in currents , similar to the chick α7-subunit . ACR-2R receptor pharmacology is also changed; DMPP elicits larger currents from the mutant channel than the wild-type channel , whereas activation by nicotine is unaffected . These phenotypes cannot be explained by a simple change in efficiency of receptor assembly or increased channel conductance but rather suggest that changes to the pore affect dynamic transitions throughout the receptor . Characterization of acr-2 ( n2420gf ) mutant animals suggests that the ACR-2 ( V13″M ) subunit generates a hyperactive channel in vivo . Worms expressing the ACR-2 ( V13′M ) subunit exhibit spontaneous convulsions , which can be reversed by the channel-blocker mecamylamine . In addition , convulsions can be suppressed by null mutations in genes encoding any of the subunits of the receptor except the acr-3 gene . The nonessential role of ACR-3 can be explained by the observation that a functional channel is formed in the absence of ACR-3 when the ACR–2 subunit contains the V13′M mutation . Although the mutant ACR-2 ( V13′M ) channel is less active if it lacks the ACR-3 subunit , it is still almost four times more active than the wild-type receptor . The valine at the 13′ position of the pore in the chick α7 acetylcholine receptor limits calcium influx [20] , [37] . Our recordings from the acr-2 ( n2420gf ) mutant animals also show that neuronal activity involving ACR-2 ( V13′M ) R is hypersensitive to calcium levels . Thus , in vivo , the ACR-2 ( V13′M ) gain-of-function channel might result in increased excitability of the neurons and increased calcium influx , which could have broader effects because of the action of calcium as a second messenger . ACR-2 is expressed and functions in the ventral cord cholinergic motor neurons that provide the major excitatory inputs to the body muscles involved in locomotion . Of these motor neurons , VA and VB innervate the ventral muscles , and DA and DB innervate the dorsal muscles [15] . These motor neurons are required for the sinusoidal posture and locomotion of the worm . Animals lacking ACR-2 are still capable of locomotion , but they move more slowly . Our electrophysiological recordings from muscles demonstrate that the ACR-2 receptor is required to maintain normal levels of excitation in the cholinergic motor neurons . The cholinergic motor neurons showed reduced neurotransmitter release in acr-2 ( lf ) animals , whereas these motor neurons in acr-2 ( n2420gf ) animals displayed normal morphology and increased neurotransmitter release . ACR-2 could maintain the activity state of these neurons by regulating presynaptic release directly , perhaps as an autoreceptor , or indirectly through other pathways . Our data further provide functional evidence for inputs from cholinergic motor neurons into GABAergic motor neurons . The GABAergic neurons have processes adjacent to acetylcholine neuromuscular junctions and based on electron micrograph reconstructions of the nervous system appear to receive input from cholinergic motor neurons at dyadic synapses [15] . Our data are consistent with a stimulatory input from cholinergic neurons to GABAergic neurons . In acr-2 loss-of-function mutants , a reduction in cholinergic motor neuron activity is coupled with a reduction in GABAergic motor neuron activity . The gain-of-function mutation in acr-2 also exhibits nonautonomous effects on the GABAergic motor neurons . The acr-2 ( n2420gf ) mutant was originally identified because it exhibited a spontaneous shrinking behavior . Shrinking typifies mutants with defects in GABA transmission . Consistent with this phenotype , physiological recordings from dissected animals demonstrated that GABA transmission was greatly reduced in acr-2 ( n2420gf ) mutants . However , other mutants that eliminate GABA function , such as mutations in the biosynthetic enzyme for GABA or in the GABA receptors do not exhibit spontaneous hypercontractions [38] , [39] . In addition , other mutations with hyperactivation of the cholinergic motor neurons , such as mutations in Goα or in the calcium-activated potassium channel [24] , [40] , do not show the convulsive shrinking behavior . The convulsive nature of the acr-2 ( n2420gf ) mutant rather relies on the simultaneous activation of the cholinergic motor neurons and the nonautonomous suppression of activity in the GABAergic motor neurons . In these mutants , homeostatic mechanisms within the motor circuit do not seem to compensate for the imbalance in network activity; in fact , the imbalance in excitation and inhibition is most severe at physiological levels of calcium . The convulsive behaviors of acr-2 ( n2420gf ) bear similarities to the neurological features underlying some forms of epilepsy [41] . For example , genetic mutations in nicotinic acetylcholine receptors have been linked to frontal lobe epilepsy [42] . In the future , it will be interesting to determine the mode of ACR-2–mediated neurotransmission and how changes in motor circuit properties suppress or contribute to such imbalances .
All C . elegans strains were grown at 20°C as described [43] . The wild-type strain N2 was mutagenized with EMS following standard procedures [43] . The n2420 mutation was isolated based on its shrinker behavior from among the F2 progeny of animals carrying approximately 6 , 000 mutagenized haploid genomes . The n2420 mutation was backcrossed against N2 multiple times . It was mapped to the X chromosome by linkage to lon-2; also , n2420 males showed spontaneous shrinking behavior . Further three-factor mapping placed n2420 between dpy-8 and unc-6: from n2420/dpy-8 unc-6 heterozygotes , 18/19 Dpy non-Unc segregated n2420 , and 1/18 Unc non-Dpy segregated n2420 . We tested two deficiencies uDf1 and stDf1 that remove the region containing acr-2 and observed that n2420/Df animals exhibited wild-type movement as did Df/+ animals . Suppressors of n2420 were isolated as following: ten EMS-mutagenized n2420 L4 P0 animals were placed on a large NGM plate and were transferred to fresh plates daily for 2 d . Young adult F2 animals were collected from each P0 plate and placed away from the bacteria food on a new plate . After 1 h , worms that had crawled into the food were collected . Only one to two such animals per plate were saved to ensure independence of isolates . We screened an estimated 120 , 000 mutagenized haploid genomes . Fifty-three suppressor mutants were backcrossed with N2 . A list of the strains containing suppressor mutations is in Table S1 . We identified those that did not segregate the n2420 mutant phenotype after backcrossing as presumptive intragenic mutations , for which we determined DNA sequences of acr-2 locus . Extragenic suppressor mutations segregated n2420-like animals and were grouped into levamisole-resistant or levamisole-sensitive classes . Complementation tests with known levamisole-resistant mutants were performed using standard procedures , and DNA sequence determination of the suppressor mutants subsequently confirmed gene identities . The acr-12 ( n2616 ) mutation was mapped between X:11 . 80 ( pkP6133 ) and X:12 . 93 ( pkP6122 ) using single-nucleotide polymorphisms between the N2 strain and the Hawaiian strain CB4856 [44] , [45] . All acr-12 mutations were confirmed by DNA sequence determination . Other double mutants were constructed using standard procedures , and genotypes were confirmed by allele sequence determination . Information about these strains is shown in Table S2 . General molecular biology was performed according to Sambrook et al . [46] . A pJB8-based cosmid library [47] was used in the initial germline transformation rescue of the acr-2 ( n2420gf ) phenotype . Subclones pSC175 , pSC176 , and pSC178 were generated from the rescuing cosmid C46C10 clone . Cosmid and plasmid DNAs were injected at 10 ng/ml and 50 ng/ml , respectively , using pRF4 as a coinjection marker following standard procedures [48] . Multiple independent lines were examined for rescue of the convulsion phenotype . For mutation sequence determination , pairs of primers were used to amplify all exons and exon–intron boundaries . acr-2 cDNAs were isolated by screening a mixed-stage cDNA library prepared by P . Okkema , using acr-2 genomic DNA as probe . Four independent clones were isolated from 2×106 plaques . Three had similarly sized inserts and identical end sequences . Full sequences of the cDNA 21A clone were determined , which confirmed the predicted gene structure of acr-2 . Transcriptional acr-2 promoter-driven GFP ( pSC205 ) or mCherry ( pCZGY847 ) constructs were made using 3 . 5 kb or 1 . 8 kb of acr-2 5′ upstream sequences , respectively . The 3 . 5-kb promoter also included the entire upstream gene F38B6 . 1 and portion of F38B6 . 2; the 1 . 8-kb promoter included only the promoter region of acr-2 . Punc-25-acr-2 ( pSC374 ) was constructed by replacing the acr-2 promoter with 1-kb unc-25 promoter . Pacr-12::acr-12 transgenes was generated using PCR-amplified acr-12 genomic DNA that included 1 . 4 kb of 5′ upstream sequences , the entire coding region , and 0 . 9 kb of 3′ downstream sequences . unc-63 cDNA was subcloned from pAF55 ( Prab-3::unc-63 ) [49] . Punc-25-acr-12 ( pCZGY745 ) , Pacr-2-acr-12 ( pCZGY744 ) , Punc-25-unc-63 ( cDNA ) ( pCZGY745 ) , and Pacr-2-unc-63 ( cDNA ) ( pCZGY744 ) were constructed using the Gateway cloning technology ( Invitrogen ) ( Table S2 ) . The sequences of resulting DNA clones were confirmed . Transgenic lines were generated using either plin-15 ( + ) , pRF4 , or Pttx-3-XFP as coinjection markers ( Table S2 ) . Integration of extrachromosomal arrays was preformed following Trimethyl Psoralen-UV mutagenesis . Ten to 20 L4 larvae were placed on freshly seeded NGM plates . The following day , young adults were transferred to fresh plates and recorded by video for 90 s , five frames per second . Videos were scored by observers blind to genotype . A “convulsion” was defined as an event involving the nose of the worm moved backwards without the tail of the worm moving . For each strain , video observation was performed on worms from at least two independent experiments . All drug manipulations were performed according to published procedures [7] , [8] , [21] . Drugs were purchased from Sigma-Aldrich . For levamisole and aldicarb assays , 1-d-old adult hermaphrodites were placed on plates containing the drug of chosen concentration , and the effects on animal movement were observed at 15- to 30-min intervals . Animals were scored as paralyzed when no body movements were observed in response to poking . In mecamylamine tests , the effects of the drug on acr-2 ( n2420gf ) animals were first assessed using a concentration series from 50 µM to 400 µM , and the behavior of acr-2 ( n2420gf ) animals was suppressed to nearly wild type after 5 h on plates containing 100 µM to 400 µM mecamylamine . Quantification of the convulsion rate was performed on 1-d-old adult hermaphrodites . Animals were first placed on seeded plates with no drug , and the convulsion rate was recorded by video as above to set time 0 . The animals were then transferred to seeded plates containing 100 µM mecamylamine , and the convulsion rate recorded every 60 min for 3 h . Animals were then transferred to plates containing no drug and recorded by video at 30 min afterwards . Worm-tracking experiments were performed according to [50] . Standard NGM plates were prepared with the addition of 0 . 01% bromophenol blue ( Sigma-Aldrich ) and were allowed to cool for at least 5 h . Plates were then spread with 240 µl of 2% HB101 bacteria in M9 medium and were incubated overnight at room temperature . The following day , five gravid worms were placed on each plate in a 5-µl drop of M9 medium . Assays were recorded at a frequency of 1 frame/s for 10 min , starting when the drops of M9 had absorbed . Video images were analyzed using ImageJ software ( NIH ) . Electrophysiological methods were adapted from previous studies [28] , [51] . Adult nematodes were glued ( Histoacryl Blue , B . Braun ) along the dorsal side of the body to the surface of a plastic coverslip . A sharpened tungsten rod ( A-M Systems ) was used to perform a lateral incision and to remove the viscera . The cuticle flap was glued back to expose the ventral medial body wall muscles , and the preparation was treated by collagenase type IV for 20 s at a concentration of 0 . 5 mg/ml . For Figures 4 , 5A , 5B , S2 , and S4 , membrane currents were recorded in the whole-cell configuration using an EPC-10 patch-clamp amplifier ( HEKA ) . Acquisition and command voltage were controlled using the HEKA Patchmaster software . For Figures 5C and S3 , membrane currents were recorded using a RK-400 patch-clamp amplifier ( Bio-Logic ) . Acquisition and command voltage were controlled using the pClamp9 software ( Axon Instruments ) driving a 1322A Digidata ( Axon Instruments ) . Data were analyzed and graphed using Mini Analysis ( Synaptosoft ) and Microcal Origin software ( Microcal Software ) . The resistance of recording pipettes was within 3–4 . 5 MΩ . Capacitance , resistance , and leak current were not compensated . All experiments were performed at room temperature . The bath solution contained 150 mM NaCl , 5 mM KCl , 1 mM MgCl2 , 10 mM glucose , 15 mM HEPES , and sucrose to 340 mOsm ( pH 7 . 35 ) . External CaCl2 concentration was 0 . 5 , or 2 or 5 mM , as indicated in each figure . For the 0 . 5 mM CaCl2 solution , the concentration of MgCl2 was increased to 4 mM in order to help stabilize the membrane [52] . The pipette solution contained 125 mM K gluconate , 20 mM KOH , 10 mM hepes , 1 mM MgATP , 3 mM NaATP , 5 mM EGTA , 15 mM KCl , and sucrose to 335 mOsm ( pH 7 . 2 ) . GABA was diluted to 0 . 1 mM in the bath solution containing 2 mM CaCl2 and was pressure-ejected in the vicinity of muscle cells . All chemicals were obtained from Sigma-Aldrich . X . laevis oocytes were prepared , injected , voltage-clamped , and superfused according to the procedure described in [13] . Each set of recordings was done on the same day , 2 or 3 d after the cRNA injections . Dose-response experiments were performed as described in [13] . Values obtained at 500 µM and 1 mM were excluded from the fit because of the open-channel block observed at high acetylcholine concentrations . RNA isolation was performed as described in [13] . cDNAs were obtained by reverse-transcription PCR using the following primer combinations . acr-2 ( + ) and acr-2 ( n2420gf ) : oTB429 5′-AAACTCGAGatgaagaagacggtcaaaat-3′ and oTB430 5′-TTTGGGCCCttaagaatacatatcagac-3′ acr-3: oTB439 5′-AAACTCGAGatgcagaaaatatggttatt-3′ and oTB440-5′-TTTGGGCCCtcatgaattcaacatttc-3′; acr-12: oTB431 5′-AAACTCGAGatgctctataaaaaacg-3′ and oTB432-5′-TTTGGGCCCtcacttcaagttccatgaac-3′ . PCR fragments were digested with XhoI and Bsp120I restriction enzymes and cloned into pTB207 , an expression vector for in vitro transcription that contains the 3′ UTR of the Xenopus laevis β-globin gene . The resulting plasmid clones are pTB244 acr-2 , pTB245 acr-2 ( n2420gf ) , pTB246 acr-12 , and pTB247 acr-3 . In addition , we used the following clones described in [13]: pTB211 unc-38 , p+TB212 unc-63 , pTB215 ric-3 , pTB216 unc-74 , and pTB217 unc-50 . cRNA was synthesized in vitro from linearized plasmid DNA templates using the mMessage mMachine T7 transcription kit ( Ambion ) . Lithium chloride–precipitated cRNA was resuspended in RNAse-free water and stored at −80°C . Acetylcholine chloride ( ACh ) , ( − ) -nicotine hydrogen tartrate ( Nic ) , 1 , 1-dimethyl-4-phenylpiperazinium iodide ( DMPP ) , choline bitartrate ( Cho ) , ( − ) -tetramisole hydrochloride ( levamisole , Lev ) , mecamylamine hydrochloride ( Mec ) were purchased from Sigma-Aldrich .
|
Neuronal acetylcholine receptors modulate a wide range of activities in vertebrates and invertebrates . The activity and sensitivity of these receptors to particular pharmacological agents is determined by the subunit composition of the receptors . A rich diversity of acetylcholine receptors are expressed in the nervous system of the nematode C . elegans , and like their mammalian counterparts , their subunit compositions are not understood . Here , we identify an activating mutation in a neuronal acetylcholine receptor subunit that causes convulsive body muscle contractions . By isolating suppressors of the convulsive phenotype , we are able to identify the genes required for the assembly and function of this acetylcholine receptor . Reconstitution studies in oocytes demonstrate that this acetylcholine receptor is composed of five different subunits . The contraction and relaxation of body muscles are coordinated by the neurotransmitters acetylcholine and GABA , respectively . In vivo recordings reveal that loss of this ion channel leads to a decrease in the activation state of the cholinergic motor neurons . By contrast , hyperactivation of the ion channel leads indirectly to the silencing of GABAergic motor neurons . The resulting imbalance in the locomotory circuit causes convulsions of the body muscle . This imbalance in excitation and inhibition of the locomotion circuit mimics the neurological features observed in epilepsy .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"neuroscience/behavioral",
"neuroscience",
"genetics",
"and",
"genomics/gene",
"function"
] |
2009
|
A Neuronal Acetylcholine Receptor Regulates the Balance of Muscle Excitation and Inhibition in Caenorhabditis elegans
|
Dengue virus is endemic in peninsular Malaysia . The clinical manifestations vary depending on the incubation period of the virus as well as the immunity of the patients . Glucose-6-phosphate dehydrogenase ( G6PD ) deficiency is prevalent in Malaysia where the incidence is 3 . 2% . It has been noted that some G6PD-deficient individuals suffer from more severe clinical presentation of dengue infection . In this study , we aim to investigate the oxidative responses of DENV2-infected monocytes from G6PD-deficient individuals . Monocytes from G6PD-deficient individuals were infected with DENV2 and infection rate , levels of oxidative species , nitric oxide ( NO ) , superoxide anions ( O2− ) , and oxidative stress were determined and compared with normal controls . Monocytes from G6PD-deficient individuals exhibited significantly higher infection rates compared to normal controls . In an effort to explain the reason for this enhanced susceptibility , we investigated the production of NO and O2− in the monocytes of individuals with G6PD deficiency compared with normal controls . We found that levels of NO and O2− were significantly lower in the DENV-infected monocytes from G6PD-deficient individuals compared with normal controls . Furthermore , the overall oxidative stress in DENV-infected monocytes from G6PD-deficient individuals was significantly higher when compared to normal controls . Correlation studies between DENV-infected cells and oxidative state of monocytes further confirmed these findings . Altered redox state of DENV-infected monocytes from G6PD-deficient individuals appears to augment viral replication in these cells . DENV-infected G6PD-deficient individuals may contain higher viral titers , which may be significant in enhanced virus transmission . Furthermore , granulocyte dysfunction and higher viral loads in G6PD-deificient individuals may result in severe form of dengue infection .
Dengue infection is among the leading causes of morbidity and mortality in the tropics and subtropics where as many as 100 million people are infected with 22 , 000 deaths yearly [1] . Dengue infection is caused by dengue virus ( DENV ) , an RNA virus of the family Flaviviridae . There are four serotypes of the virus which are referred to as DENV1 , DENV2 , DENV3 and DENV4 . All four serotypes can cause the full spectrum of disease [2] . DENV is primarily transmitted to humans by the bite of infected Aedes mosquitoes , particularly Aedes aegypti . Other Aedes species that transmit the disease include A . albopictus , A . polynesiensis , and A . scutellaris [3] . Although uncommon , DENV can also be transmitted via infected blood products and through organ transplantation [4] , [5] . A large percentage ( ∼80% ) of people infected with DENV show only mild symptoms such as fever . On the other hand , some patients experience more severe illness such as dengue hemorrhagic fever ( DHF ) and dengue shock syndrome ( DSS ) , which can be life threatening [6] . Several factors have been associated with the development of severe dengue including prior infection with a heterotypic serotype , the strain of infecting virus , age and gender , and the genetic background of the patient . DENV-related illness is more common in children , and in contrast to other infections , well-nourished children are more predisposed to severe illness [7] . Polymorphism in genes coding for tumor necrosis factor alpha ( TNFα ) , mannan-binding lectin 2 ( MBL2 ) , Cytotoxic T-Lymphocyte Antigen 4 ( CTLA4 ) , transforming growth factor β ( TGFβ ) , DC-SIGN , and human leukocyte antigen ( HLA ) class I and II alleles have been linked with an increased risk of severe dengue complications [8] . Yet another genetic abnormality reported to have a link with DHF/DSS is the deficiency of glucose-6-phosphate dehydrogenase ( G6PD ) , a ubiquitous X-linked enzyme which is part of the innate defense mechanisms [9] . G6PD deficiency is the most common enzymopathy worldwide , with highest prevalence among Sub-Saharan African countries [10] , [11] . High frequencies ( 6 . 0–10 . 8% ) of G6PD deficiency have also been reported for Southeast Asian countries [12] . G6PD deficiency affects the production of reactive nitrogen ( RNS ) and oxygen species ( ROS ) such as nitric oxide ( NO ) , superoxide ( O2− ) , and hydrogen peroxide ( H2O2 ) resulting in alterations of normal redox state of the cells [13] . Cells of immune system employ RNS/ROS to kill invading pathogens . A reduction of the redox state of immune cells may render immune cells less effective against invading organisms , resulting in an increased severity of the infection [14] . It is found that monocytes from G6PD-deficient patients show an increased susceptibility to DENV2 infection with higher replication ability than those from normal controls [9] . Although there appears to be a connection between G6PD deficiency and enhanced DENV replication [15] , no studies have been carried out to elucidate the molecular mechanism behind this observation . In the present study , we aim to find out whether it is the altered redox state of monocytes from G6PD-deficient individuals , which is responsible for the increased susceptibility of monocytes to enhanced DENV replication .
This study was approved by the research and ethics committee of Clinical Research Center ( CRC ) , Ministry of Health and Research , and Ethics Committee , Universiti Sains Malaysia . The study was carried out between January 2010 and December 2011 . Written informed consent was obtained from all the blood donors who agreed to participate in the study . Standard inclusion criterion for blood donors was followed and only male donors were included . Additionally , donors with prior history of DENV infection were excluded . After blood was donated , a portion ( 2 . 5 mL ) of blood from the tubing of the blood bag was taken . The blood was screened for G6PD deficiency using ultraviolet test ( Cat #: SQMMR 500 , R&D system , Athena-Greece ) and G6PD activity was assayed using G6PD kit ( Cat #: PD2616 , RANDOX Laboratory Antrim , UK ) following manufacturers' protocols . Donors without G6PD deficiency on screening were selected as controls . The sera of G6PD-deficient and G6PD-normal donors were screened for DENV-reactive antibodies using IgG/IgM Capture ELISA kits ( IgM Cat #: E-DEN01M and IgG Cat #: E-DEN02G , Panbio , Brisbane , Australia ) according to the manufacturer's protocol . The C6/36 mosquito A . albopictus cell line ( CRL-1660 ATCC , USA ) was used to propagate DENV2 ( D2MY00-22563 ) , kindly provided by Prof Shamala of University of Malaya , in Leibovitz culture medium ( L-15 ) ( SIGMA , USA ) supplemented with 1% L-glutamine ( SIGMA , USA ) , 19% tryptose phosphate broth ( Hi MEDIA , India ) , 1% of penicillin/streptomycin ( GIBCO , Grand Island , USA ) and 5% fetal bovine serum FBS ( GIBCO , Grand Island , USA ) . DENV2 in conditioned medium was titrated using a plaque assay on Vero cells ( CCL-81 ATCC , USA ) essentially as described previously [16] and stored at −80°C in aliquots . Peripheral blood mononuclear cells ( PBMCs ) were isolated from both G6PD-deficient and normal controls' blood by density gradient centrifugation using Lymphoprep medium ( Cat #: N07-1114547 Axis Shield , Norway ) following manufacturer's protocol . PBMCs were then used to isolate primary monocytes using a MACS kit system II ( Cat #: 130-091-153 , MiltenyiBiotec GmbH , Gladbach , Germany ) . The purified monocytes were enumerated and cell viability was determined by trypan blue exclusion assay . The cells were then seeded in 24-well plates ( Costar , Corning , USA ) at 2×105cells/well and maintained in humidified incubator at 37°C in the presence of 5% CO2 . Monocytes from G6PD-deficient and normal controls were infected with DENV2 at multiplicity of infection ( MOI ) 0 . 1 for 3 hours at 37°C/5% CO2 as described previously [9] . For optimal virus contact to the monocytes , the plates were gently agitated every 15 min . After 3 hours of incubation , the cells were washed twice with serum-free medium , re-suspended in complete growth medium and cultured at 37°C/5% of CO2 for five days . Mock-infected monocyte cultures were set simultaneously as negative controls . Conditioned media were harvested at various time points ( 24 , 48 , 72 , 96 , 120 hours ) post-infection and the number of infected cells and virus titers were determined using flow cytometry and plaque assay respectively . Virus-containing conditioned media were stored in aliquots at −80°C . Infected and mock-infected monocytes were harvested and washed twice with cold phosphate buffered saline ( PBS ) containing 0 . 1% NaN3 ( SIGMA-USA ) . Surface labeling was performed by incubating the cells with 100 µl ( 1∶10 diluted ) of CD14-PE MAb ( Cat#: 347497 Becton Dickinson , San Jose , CA , United States ) for 30 min on ice in dark . Cells were fixed and permeabilized in 200 µl Cytofix/Cytoperm solution ( BD Biosciences , San Diego , CA ) for 15 min at room temperature followed by 2× washing in Cytoperm/Cytowash solution . Cells were stained with 100 µl ( 1∶20 diluted ) of DENV2 E protein-specific monoclonal antibody ( Abcam # ab41349 ) and incubated for 1 hour on ice in dark and washed twice with Cytoperm/Cytowash solution . Secondary FITC-labeled goat anti-mouse-IgG ( Cat #: 349031 , Becton Dickinson , Biosciences USA ) antibody was added to a final concentration of 3 . 5 µg/ml and incubated for 30 minutes on ice in dark . Finally , the cells were washed twice in Cytoperm/Cytowash solution and resuspended in 0 . 5 ml of 1% paraformaldehyde containing 0 . 1% NaN3 and subjected to flow cytometry . Mock-infected monocytes were used as negative controls and were treated and run simultaneously with the infected groups ( G6PD-deficient and G6PD-normal monocytes ) to improve gating , setup of compensation and the precision of measurement . A minimum of 10 , 000 events were acquired using a FACS Calibur II flow cytometer ( Becton Dickinson , Biosciences , USA ) . Data were analyzed using flowing software Ver 1 . 0 ( Turku Center for Biotechnology , University of Turku , Finland http://www . flowingsoftware . com/ ) . Isotype-matched antibody was used as a negative control . The percentage of DENV2 positive cells was determined from FITC fluorescence histograms using a region that was defined based on the analysis of the mock-infected control cells . Dengue virus particles were titrated by plaque assay as previously described by Roehrig JT et al . [16] . Briefly , Vero cells were seeded in 12-well plates at a density of 2×105 cells/well in complete DMEM medium ( without phenol red ) and incubated overnight at 37°C/5% CO2 . Serial 10-fold dilutions of virus-containing supernatant in DMEM/2% FBS were added to the wells and incubated at 37°C/5% CO2 for 90 minutes with gentle agitation every 15 minutes . The medium was carefully removed and the cells were overlaid with 1 . 0 ml of DMEM/10% FBS containing 0 . 5% carboxymethylcellulose ( CMC ) ( SIGMA - USA ) and incubated at 37°C/5% CO2 for 7 days . CMC was removed by washing the wells twice with 2 . 0 ml PBS/well . Cells were fixed with freshly prepared cold 4% paraformaldehyde and stained with 1% crystal violet ( AMRESCO , USA ) in 20% ethanol for 30 minutes . The viral titers were expressed as plaque forming units ( PFU ) /ml = [ ( number of plaques per well ) × ( dilution ) ]/ ( inoculums volume ) . Nitric oxide ( NO ) released into the conditioned media of DENV2 and mock-infected monocytes was determined by measuring the stable nitrite using a Greiss reagent assay kit ( Cat #: R&D SYSTEM ) according to the manufacturer's protocol . The absorbance was measured at 540 nm using Bio Mate 3 ( Thermo Scientific , USA ) spectrophotometer . All measurements were performed in duplicates . The intracellular superoxide anion and ROS production were quantified in the DENV2 infected and control cells using a total ROS/Superoxide detection kit ( Cat #: ENZ 51010 , ENZO LIFE SCIENCES INC . , USA ) following manufacturer's protocol . Briefly , DENV2 and control cells were harvested at 24 , 48 , 72 , 96 , and 120 hours and washed using ROS buffer supplied with the kit . The positive , negative , DENV2-infected and control cells were then incubated with 500 µl ROS/superoxide detection reagents for 30 minutes at 37°C/5% of CO2 in RPMI-1640/10% FBS and subjected to flow cytometry analysis . Data analysis and anticipated results were obtained by generating a log FL1 ( X-axis ) versus a log FL2 ( Y-axis ) dot plot with quadrants added to it . Monocytes with increased production of superoxide demonstrated a bright orange fluorescence and detected using the FL2 channel appeared in the two upper quadrants of a log FL1 ( X-axis ) versus a log FL2 ( Y-axis ) dot plot . Monocytes with high production of oxidative stress demonstrated a bright green fluorescence and registered in FL1 channel appeared in the upper and lower right quadrants of a log FL1 ( X-axis ) versus a log FL2 ( Y-axis ) dot plots . Results of experiments are presented as a percentage of the cells with increased superoxide and ROS production or as an increase in the mean fluorescence of induced samples versus controls . Results were reported as mean ± SD . Data were analyzed using SPSS software version 11 . 5 . The p-value was calculated using Student's T test . A p-value less than 0 . 05 were considered significant . Error bars were expressed as means ± SD .
Four hundred blood donors were screened for G6PD enzyme deficiency and 16 donors were found to be G6PD deficient by the ultraviolet test . Out of 384 G6PD-normal donors , 16 were matched for age ( 33 . 18±4 . 28 years for G6PD-normal and 34 . 95±4 . 34 years for G6PD-deficient ) and selected as normal controls ( G6PD-normal donors ) . The G6PD deficient ( n = 16 ) and age-matched G6PD-normal donors ( n = 16 ) were then subjected to G6PD enzyme activity assay . G6PD-normal donors will be referred to as normal controls from hereafter . All 16 G6PD-deficient individuals had less than 10% of normal G6PD activity as measured by the fluorometric assay . The mean G6PD activity for G6PD-deficient individuals was 0 . 285±0 . 26 IU/g Hb , which was significantly ( p<0 . 0001 ) lower than the mean activity of G6PD-age matched normal controls ( 13 . 56±2 . 02 IU/g Hb ) as shown in Fig . 1 . In G6PD-deficient monocyte cultures , increased DENV2 infected cell rates were detected by flow cytometry until 48 hours post-infection , when peak values were reached . After this peak , the percentage of DENV2 infected cells decreased ( Fig . 2A ) . Monocytes from 16 G6PD-deficient subjects showed mean virus infection percentage of 40 . 868±7 . 330% at 48 hours post infection . In normal control monocytes culture , the frequency of DENV2 infected cells increased until 72 hours post-infection , when peak values were reached followed by a decrease ( Fig . 2A ) . Monocytes from 16 normal controls showed mean virus infection percentage of 24 . 3%±4 . 6% at 72 hours post infection . These results indicate that the mean percentage of DENV2-infected monocytes from G6PD-deficient subjects exceeded the percentage of DENV2-infected monocytes from normal controls . This difference of infected cells from two groups was statistically significant ( p<0 . 0001 ) . Moreover , the peak infection was delayed by 24 hours in monocytes from normal controls compared to monocytes from G6PD-deficient subjects . This data was further verified by measuring the cell-free DENV2 in the supernatant of infected monocyte cultures ( Fig . 2B ) . In G6PD-deficient monocyte cultures , increased DENV2 titers were detected by plaque assay until 48 hours post infection , when peak values were reached . After this time point , DENV2 titers decreased ( Fig . 2B ) . DENV2 released from 16 G6PD-deficient monocyte cultures showed mean peak titers of 37×102 PFU/ml on the 48 hours post infection . In normal control monocyte cultures , increased DENV2 titers were detected until 72 hours post infection , when peak values were reached . After this time point , DENV2 titers decreased ( Fig . 1B ) . DENV2 released from 16 normal control monocyte cultures showed mean peak titers of 22×102 PFU/ml on the 72 hours post infection . These results indicate that significantly ( p<0 . 0001 ) more DENV2 was released by the infected monocytes from G6PD-deficient subjects compared to infected monocytes from normal controls . The peak DENV2 titers were detected at an earlier time point ( 48 hours post infection ) in G6PD-deficient monocyte cultures compared to normal control monocyte cultures ( 72 hours post infection ) . As illustrated in Fig . 3 , DENV2 infection induced the production of NO in both normal control and G6PD-deficient monocytes in a time dependent manner . In G6PD-deficient monocyte cultures , increased NO produced until 48 hours post infection , when peak values were reached followed by a decrease ( Fig . 3 ) . NO released from 16 G6PD-deficient monocyte cultures showed mean peak levels of 603 . 75 µM L−1 at 48 hours post-infection . On the other hand , in the normal control monocytes , increased NO produced until 72 hours post-infection , when peak values were reached ( Fig . 3 ) . NO released from the normal control monocytes showed mean peak levels of 1050 µM L−1 at 72 hours post-infection . The difference in NO levels produced between G6PD-deficient and normal control monocytes was significant ( p<0 . 0001 ) and the peak NO levels were detected at an earlier time point ( 48 hours post infection ) in G6PD-deficient monocyte cultures compared to normal control monocyte cultures ( 72 hours post infection ) . The lower levels of endogenous NO production induced after the infection of G6PD-deficient monocytes correlate with the accelerated replication of DENV2 in these cells . As shown in Fig . 4 , DENV2 infection of monocytes induced generation of superoxide anions ( O2 . − ) in both normal control and G6PD-deficient monocytes ( p<0 . 001 ) in a time dependent manner . In G6PD-deficient monocyte cultures , increased O2 . − levels were observed until 24 hours post infection , when peak values of 23 . 3% were reached ( Fig . 4 ) . On the other hand , in normal control monocyte culture , O2 . − levels increased until 48 hours post-infection , when peak value reached to 70 . 3% ( Fig . 4 ) . These results indicate that significantly ( p<0 . 0001 ) less O2 . − was generated by the infected monocytes from G6PD-deficient subjects compared to infected normal control monocytes . The peak O2 . − levels were noticed at an earlier time point ( 24 hours post infection ) in G6PD-deficient monocyte cultures compared to normal control monocyte cultures ( 48 hours post infection ) . The lower level of endogenous O2 . − generation induced after the infection of G6PD-deficient monocytes seems to markedly enhance the course of DENV2 infection in these cells . As displayed in Fig . 5 , DENV2 infection of monocytes induced oxidative stress accumulation in both normal control and G6PD-deficient monocytes ( p<0 . 001 ) in a time dependent manner . In G6PD-deficient monocyte cultures , oxidative stress accumulation increased until 72 hours post infection , when peak levels of 84 . 4% were reached ( Fig . 5 ) . Conversely , in normal control monocyte culture , oxidative stress accumulation increased until the 96 hours post-infection , when peak values of 63 . 2% were reached ( Fig . 5 ) . These results indicate that significantly ( p<0 . 0001 ) more oxidative stress was accumulated by the infected monocytes from G6PD-deficient subjects compared to infected monocytes from normal control . The peak oxidative stress accumulation was noticed at an earlier time point ( 72 hours post infection ) in G6PD-deficient monocyte cultures compared to normal control monocyte cultures ( 96 hours post infection ) . A higher and earlier accumulation of oxidative stress in G6PD-deficient monocytes appears to be a result of greater viral replication in these cells compared to normal control monocytes . There was a significant strong , positive correlation ( r = 0 . 702; p = 0 . 001 ) between the DENV2-infected cells and NO levels in monocyte from G6PD-deficient subjects ( Fig . 6A ) , compared to monocytes from normal controls ( Fig . 6B ) . There was a significant moderate , positive correlation ( r = 0 . 476; p = 0 . 040 ) between the DENV2-infected cells and O2 . − levels in monocyte from G6PD-deficient subjects ( Fig . 6A ) , compared to monocytes from normal controls ( Fig . 6B ) . There was also a significant moderate , positive correlation ( r = 0 . 368; p = 0 . 121 ) between the DENV2-infected cells and O2 . − levels in monocyte from G6PD-deficient subjects ( Fig . 6A ) , compared to monocytes from normal controls ( Fig . 6B ) . These correlation studies further support the hypothesis that oxidative state of the cell may contribute to enhanced DENV infection .
DENV causes infection which may range from mild to severe in affected patients . The severe form is associated with DHF and DSS , which can be life threatening . The factors that define progression towards severe forms ( DHF/DSS ) of dengue infection remain to be elucidated . One possible correlate though may be the innate G6PD deficiency , which is the most common enzymopathy worldwide . G6PD , through the pentose phosphate pathway ( PPP ) , provides the reduced form of NADPH for various cellular reactions including glutathione ( GSH ) recycling , superoxide anion production via NADPH oxidase , nitric oxide ( NO ) synthesis , and cholesterol synthesis [12] . Inhibition of G6PD results in the generation of less reactive oxygen and nitrogen species ( super-oxide anion , hydrogen peroxide , hydroxyl radical , and NO ) in the granulocytes and endothelial cells [17] , [18] . Recurrence of microbial infections in G6PD deficient individuals has been reported [19] . The effect of cellular redox state on the course of viral infections is also well documented . For instance , replication of corona virus , coxsackie virus , rhinovirus , influenza , HIV , hepatitis , and enterovirus 71 virus is modulated by the redox milieu [20] , [21] , [22] , [23] , [24] , [25] . Coxsackie viruses found replicate to a higher titer in C3H/JHe mice fed with diets deficient in selenium ( Se ) , vitamin E or both than in mice given a normal diet [26] . Similarly , glutathione administration exhibited antiviral effect on influenza virus [22] . These findings suggest that redox imbalance may be conducive to enhanced replication and virulence of certain viruses . Chao et al . , [9] reported that monocytes from G6PD-deficient patients , using an ex vivo culture system , were more readily infected with the two DENV2 strains- ( 1 ) the New Guinea C strain from the DF patient or ( 2 ) the 16681 strain from the DHF patient than with those from normal controls . However , the underlying mechanism of this enhanced susceptibility was not investigated . Here we confirm Chao's findings and show in addition that reduced production of NO and O2− as well as earlier accumulation of oxidative stress contribute significantly to enhanced DENV2 infectivity in monocytes from G6PD-deficient subjects . Enhanced infection of G6PD-deficient monocytes by DENV may be attributable to increased viral receptors on these cells or greater production of viral particles or a combination of both . We have not studied the up-regulation of viral receptors but did clearly see an enhanced production of viral particles in these cells . Based on our findings and those reported by others , we propose a model for the association between the redox status of the host cells and DENV . Following entry into the cell , DENV fuses with endosomal membrane to release its nucleocapsid into the cytoplasm where viral RNA is replicated and translated into proteins . These processes may be affected by the cellular redox state , being more efficient in an oxidizing environment , thus resulting in enhanced virus production . Findings of Chao et al . , [9] and the ones presented here suggest that the high competency for DENV infection in monocytes of G6PD-deficient individuals may result in increased replication and higher virus yield . Higher viral loads in G6PD-deficient individuals may increase the probability of dengue transmission to others via infected mosquitos . Several reports have shown a correlation between severe dengue and high viral loads [27] , [28] , [29] , [30] , [31] . G6PD deficiency is reported to cause granulocyte dysfunction [18] , [32] . A combination of granulocyte dysfunction and enhanced replication of DENV in monocytes of G6PD-deficient individuals may prevent the clearing of the primary infection thus predisposing infected individuals to severe form of disease . It remains to be seen whether G6PD deficiency-mediated enhanced viral replication has any outcome on severity of dengue infection in areas where both dengue infection and G6PD deficiency are endemic . In Thailand , the prevalence of G6PD deficiency in the general population is approximately 14% . A cohort of 89 males diagnosed with DHF was studied to determine if G6PD deficiency was related to occurrence and/or course of dengue infection . Out of 89 , a total of 17 ( 19 . 1% ) DHF patients had G6PD deficiency , thus no significant association established between G6PD deficiency and DHF [15] . However , this study is based on only a modest number of hospitalized patients and therefore provides little information on the incidence of severe dengue in G6PD deficient individuals . High prevalence of G6PD is also reported in African population [33] but a low incidence of severe dengue is noted in populations of African origin in a couple of studies conducted in Cuba [34] and Haiti [35] . Since the outcome of these studies is inconclusive , well-designed studies are needed to demonstrate whether G6PD-deficient individuals are at risk of severe dengue with statistical significance . Such studies would be potentially beneficial in providing added knowledge of host defense mechanism , and may be clinically important for G6PD-deficient individuals travelling to or living in DENV endemic areas .
|
An estimated 50 to 100 million cases of dengue fever occur each year worldwide . Among these , there are 200 , 000 to 500 , 000 cases of life-threatening dengue hemorrhagic fever ( DHF ) and dengue shock syndrome ( DSS ) . Factors contributing to the development of DHF/DSS are not yet fully identified . Glucose-6-phosphate dehydrogenase ( G6PD ) deficiency is prevalent in Southeast Asian countries where dengue is also endemic . Besides affecting normal function of erythrocytes , G6PD deficiency also affects other cells by causing abnormal cellular redox . Altered redox state of cells may render them less effective in clearing up microbial and viral infections . Here we confirm previous findings that monocytes from G6PD-deficinet individuals support better dengue virus replication . In addition , we show that reduced production of reactive oxygen , and nitrogen species and elevated levels of oxidative stress are responsible for the enhanced viral replication . We suggest that redox imbalance observed in infected monocytes from G6PD-deficient individuals may facilitate dengue transmission and affect clinical outcome . However , a handful of studies carried out in areas where both G6PD deficiency and dengue are endemic , reveal no statistically significant correlation between severity of Dengue and G6PD deficiency . Well-designed studies are needed to demonstrate that G6PD-deficient individuals are at risk of severe dengue .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"genetics",
"immunology",
"biology",
"microbiology",
"molecular",
"cell",
"biology"
] |
2014
|
Dengue Virus Type 2 (DENV2)-Induced Oxidative Responses in Monocytes from Glucose-6-Phosphate Dehydrogenase (G6PD)-Deficient and G6PD Normal Subjects
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Foxp3+ regulatory T ( Treg ) cells are essential for the maintenance of immune homeostasis and tolerance . During viral infections , Treg cells can limit the immunopathology resulting from excessive inflammation , yet potentially inhibit effective antiviral T cell responses and promote virus persistence . We report here that the fast-replicating LCMV strain Docile triggers a massive expansion of the Treg population that directly correlates with the size of the virus inoculum and its tendency to establish a chronic , persistent infection . This Treg cell proliferation was greatly enhanced in IL-21R−/− mice and depletion of Treg cells partially rescued defective CD8+ T cell cytokine responses and improved viral clearance in some but not all organs . Notably , IL-21 inhibited Treg cell expansion in a cell intrinsic manner . Moreover , experimental augmentation of Treg cells driven by injection of IL-2/anti-IL-2 immune complexes drastically impaired the functionality of the antiviral T cell response and impeded virus clearance . As a consequence , mice became highly susceptible to chronic infection following exposure to low virus doses . These findings reveal virus-driven Treg cell proliferation as potential evasion strategy that facilitates T cell exhaustion and virus persistence . Furthermore , they suggest that besides its primary function as a direct survival signal for antiviral CD8+ T cells during chronic infections , IL-21 may also indirectly promote CD8+ T cell poly-functionality by restricting the suppressive activity of infection-induced Treg cells .
The immune system has to efficiently eliminate pathogens but simultaneously needs to avoid the potential self-damage and immunopathology caused by excessive immune activation . Therefore , a tight regulation of immune responses is critical for host survival . The subset of CD4+CD25+ regulatory T ( Treg ) cells exerts key negative regulatory mechanisms of the immune system that prevent autoimmunity and T cell mediated inflammatory disease [1] . Treg cells are best defined by expression of the signature transcription factor forkhead box P3 , Foxp3 [2] , [3] , [4] , [5] , [6] , [7] . Their fundamental role in the maintenance of immune homeostasis and tolerance is well established [8] , [9] , [10] and unambiguously demonstrated by the severe multi-organ autoimmune disease , allergy and inflammatory bowel disease that develops in Foxp3-deficient mice or patients with immune dysregulation , polyendocrinopathy , enteropathy , X-linked ( IPEX ) syndrome [3] , [11] , [12] , [13] . However , the relevance of Treg cell responses for shaping adaptive immunity against pathogens , in particular in the context of chronic infections , refigmains much less understood . Treg cells potentially have both beneficial and adverse effects on disease outcomes during viral infections . By dampening effector immune responses , Treg cell responses mitigate immunopathology resulting from exaggerated inflammation and tissue destruction during acute [14] , [15] , [16] , [17] , or protracted infections [18] , [19] , [20] , [21] , [22] . In addition , Treg cells have been shown to support antiviral immunity by modulating T cell migration to the site of infection [15] , [23] . Conversely , Treg cells were shown to suppress CD8+ T cell responses in some infections [21] , [24] , which may prevent immunopathology , but hampers effective pathogen control and ultimately promotes persistent infection [18] , [21] , [25] , [26] . Thus , while Treg cells favorably influence pathogen clearance in many acute infections [14] , [15] , [16] , [23] , they seem to negatively regulate CD8+ T cell responses during chronic infections [18] , [19] , [20] , [24] , [26] . Furthermore , elevated numbers of Treg cells have also been associated with persistent viral infections in humans [27] , [28] , [29] . However , to date little is known as to whether Treg cell activation represents a mechanism of immune evasion that facilitates persistence , or about host factors that might regulate Treg cell responses during chronic viral infection . Many characteristics of persistent viral infections in humans , such as HIV , HCV , or HBV , are also observed during chronic infection of mice with the arenavirus lymphocytic choriomeningitis virus ( LCMV ) . Accordingly , murine models of chronic LCMV infection have been extensively studied and have provided key insights into the molecular mechanisms leading to virus persistence and the associated modulation of adaptive immunity in face of persistent infection . For example , infection with a high dose of the fast-replicating strain LCMV Docile ( or Armstrong clone 13 ) results in virus persistence that is accompanied by a progressive functional impairment and – for some specificities – even leads to deletion of the virus-specific CD8+ T cell response , termed T cell “exhaustion” [30] . Exhausted antiviral T cells gradually lose their capacity to produce antiviral cytokines and to proliferate ex vivo [31] , [32] , [33] . Similar states of T cell exhaustion have been demonstrated in patients with chronic HIV or HCV infections [34] . CD8+ T cell exhaustion directly correlates with the antigen load , i . e . is enhanced during prolonged or high viral replication . Furthermore , CD8+ T cell exhaustion is also influenced by the sustained expression of several inhibitory receptors [35] and immunomodulatory cytokines , such as IL-10 [36] , [37] and TGFβ [38] . Moreover , it can be aggravated by the loss of help or essential cytokines provided by CD4+ T cells . In particular , we and others [39] , [40] , [41] have recently identified the pro-inflammatory cytokine IL-21 as an essential CD4+ T cell-produced factor that prevents CD8+ T cell exhaustion during chronic viral infection . Here we have investigated the role of Treg cell responses in chronic LCMV infection and found that the persistence-prone strain Docile ( DOC ) induces a substantial dose-dependent expansion of the Treg cell population that directly correlated with the magnitude of the virus inoculum . The expanded Treg cell population massively impaired the functionality of antiviral CD8+ T cells , interfered with virus clearance , and thereby promoted chronic viral infection . Strikingly , enforced expansion of the Treg cell population with IL-2/anti-IL-2 immune complexes ( IL-2ic; [42] ) permitted the persistence of LCMV-DOC already at much lower infection doses and even predisposed to chronic infection with an LCMV-strain that generally fails to establish persistence in immunocompetent mice . Importantly , this infection-triggered expansion of the Treg cell population was greatly inhibited by IL-21R signaling in Treg cells , thus defining a novel role for IL-21 in preventing T cell exhaustion and viral persistence via limiting virus-driven Treg cell proliferation .
We examined the role of Foxp3+ Treg cell responses in chronic viral infections in mice infected with the fast-replicating strain of LCMV-DOC . LCMV-DOC is characterized by its potential to establish a chronic , persistent infection depending on the size of the virus inoculum . Although low doses of LCMV-DOC ( 2×102–2×103 PFU ) induce potent CD8+ T cell-mediated immunity and are cleared in immunocompetent hosts , infection with intermediate and high doses ( 2×104–2×106 PFU ) results in virus persistence due to the exhaustion of the virus-specific CD8+ T cell response [30] , [31] . By choosing different virus inocula , we determined dynamics of the Treg cell population during an acute , resolving infection and a chronic infection . After an initial decline of Treg cell numbers between days 0–7 that was independent of the dose of infection and the LCMV strain used ( i . e . DOC or WE ) ( Figure 1A and data not shown ) , we observed a striking dose-dependent expansion and recovery of the Treg cell population in LCMV-DOC infected mice ( Figure 1B , C ) that directly correlated with the ability of the virus to establish persistence ( Figure 1D ) . Compared to animals infected with a low dose of LCMV-DOC ( 200 PFU ) , mice infected with intermediate ( 2×104 PFU ) or high ( 2×106 PFU ) virus doses exhibited markedly increased proportions of Treg cells in spleen and peripheral organs that amounted up to 20% of all CD4+ T cells at 15 days post infection ( Figure 1B , C ) . At this time , infectious virus had been cleared from the blood and organs of all mice infected with 200 PFU and some of the animals infected with 2×103 PFU of LCMV-DOC ( Figure 1D and data not shown ) ; while mice infected with persistence-inducing doses ( 2×104–2×106 PFU ) of LCMV-DOC still exhibited high viral titers in blood , spleen , liver and kidney , and subsequently failed to control the infection ( Figure 1D and data not shown ) . As expected , we also detected a dose-dependent reduction in the frequency of gp33-specific CD8+ T cells , which was indicative of the progressing exhaustion of the T cell response , particularly in the spleen ( Figure 1E ) , and the resulting inability to resolve the infection ( Figure 1D ) . Consumption and bioavailability of IL-2 by Treg cells has been suggested to restrict IL-2-dependent effector T cell differentiation and expansion [43] , [44] , [45] , [46] . Conversely , IL-2-driven expansion of CD8+ T cell expansion during an immune response can occur at the expense of Treg proliferation/survival [47] , [48] . Moreover , IL-21 has been suggested to interfere with Treg-mediated suppression by inhibition of IL-2 [49] . To monitor IL-2 and IL-21 production during acute and chronic infection , we took advantage of Il2-emGFP-Il21-mCherry dual reporter transgenic mice [50] that were infected with low dose ( i . e . 200 PFU ) LCMV-WE or high dose ( i . e . 2×105 PFU ) LCMV-DOC . Both Il2 ( GFP ) and Il21 ( mCherry ) were predominantly expressed by CD4+ compared to CD8+ T cells and frequencies increased from days 7–15 post infection with low dose LCMV-WE ( Figure 1F–H ) . Interestingly , chronic infection with high dose LCMV-DOC potently suppressed Il2-GFP expression , but did not affect Il21-mCherry expression by CD4+ T cells ( Figure 1F–H ) . Together , these data demonstrate a direct correlation between the size of the virus inoculum , CD8+ T cell dysfunctionality , virus persistence and the expansion of Treg cells , thus indicating a potential contribution of Treg cells to the impaired T cell function and the induction of viral persistence . IL-21 receptor ( IL-21R ) signaling is essential for the maintenance and sustained functionality of antiviral T cell responses in chronic infections [39] , [40] , [41] . As a consequence , IL-21R−/− animals have an impaired control of LCMV-DOC , and exhibit much higher virus loads [40] . In LCMV-DOC infected WT mice , the main Treg cell expansion was observed between days 10 to 20 post infection , peaking at 15 days before returning to almost naïve levels by five weeks post infection ( Figure 2A , B ) . Strikingly , this virus-driven Treg cell expansion was much more pronounced and longer lasting in mice lacking IL-21R expression , which suggested that the pro-inflammatory cytokine IL-21 restricted the proliferation of Treg cells in viral infections ( Figure 2A , B ) . Indeed , the Treg cells of naïve and LCMV-DOC infected WT mice expressed the IL-21R as assessed by flow cytometry ( Figure 2C ) . We thus investigated whether the increased Treg expansion observed in IL-21R−/− mice represented a direct inhibitory effect of IL-21 on Treg cells or rather was related to the increased viral replication in these mice . To address this issue , we generated mixed bone marrow ( BM ) chimeras by reconstituting lethally irradiated WT ( CD45 . 1+ ) mice with a 1∶1 ratio of WT ( CD45 . 1+ ) and IL-21R−/− ( CD45 . 2+ ) BM , and evaluated their Treg cell responses to infection with LCMV-DOC . Analysis of naïve BM chimeras confirmed similar reconstitution efficacy of the CD8+ and CD4+ T cell populations including Treg cells from both WT and IL-21R−/− donor BMs at 8 weeks after BM transfer ( Figure 2D , and data not shown ) . However , upon infection the population of IL-21R−/− Treg cells expanded 3-fold over that of WT Treg cells to represent 30% versus 10% of all CD4+ T cells , respectively ( Figure 2E–G ) . Since this augmented proliferation of IL-21R−/− Treg cells was detected side by side to WT Treg cells in the same animals and identical viral loads , it clearly established the direct inhibitory effect of IL-21 on Treg cells in vivo . Nevertheless , we considered that the higher Treg cell numbers in infected IL-21R−/− mice could not only result from the absence of inhibitory IL-21R signaling but might also indicate a compensatory proliferation to overcome a potential functional deficit of the IL-21R−/− Treg cells . To exclude the latter possibility , we isolated CD4+CD25+ GFP+ Treg cells from WT DEREG and IL-21R−/− DEREG mice by FACS-sorting 15 days post infection with 2×106 PFU LCMV-DOC , and compared their suppressive activity in a classical T cell inhibition assay . As shown in Figure 2H , both WT and IL-21R−/− Treg cells comparably inhibited the proliferation of anti-CD3/CD28–stimulated naïve CD25negCD4+ T cells in vitro , suggesting a normal function of IL-21R−/− Treg cells . Thus , the increased expansion of IL-21R−/− Treg cells in LCMV-DOC infected mice highlighted an important inhibitory role of IL-21 in restraining Treg cell expansion during chronic LCMV infection . Except for a slightly reduced CD25 expression , IL-21R−/− Treg cells were comparable to WT Treg cells with respect to the expression or characteristic Treg cell surface markers ( Figure 2I ) . We did not detect any IL-10 producing or gp61-specific Treg cells in infected mice , suggesting that the suppressive activity of Treg cells in LCMV-DOC infection involved neither IL-10–mediated suppression nor virus-specific Treg cells ( Figure 2J and data not shown ) . IL-6 has been suggested to regulate the balance between Treg and pro-inflammatory Th17 cell responses [51] , [52] similar to IL-21 [53] . While we have previously shown that IL-17–producing CD4+ T ( Th17 ) cells were barely detectable in LCMV-DOC infection [40] , it remains possible that IL-21 inhibits Treg cell expansion by regulation of IL-6 . However , comparing IL-6−/− and WT mice we found no differences in Treg cell expansion ( Figure 2K ) , antiviral CD8+ and CD4+ T cell responses ( Figure 2L , M ) , and virus titers ( Figure 2N ) up to day 30 post infection with 2×104 PFU LCMV-DOC . Together with the Treg cell-intrinsic negative IL-21R−/− signaling observed in WT:IL-21R−/− mixed BM chimeras , these data suggest that IL-21 restricted the virus-driven Treg cell expansion in LCMV-DOC infection independently of IL-6 signaling . To directly evaluate the impact of Treg cells on viral persistence , we next sought to analyze antiviral T cell responses and viral clearance in the absence of Treg cells . We therefore studied LCMV-DOC infection using the DEREG mouse model , in which Treg cells can be ablated by diphtheria toxin ( DT ) treatment due to transgenic expression of a high affinity DT receptor under control of the Foxp3 promoter [54] . DEREG and nontransgenic WT control mice were treated with DT and infected with 2×105 PFU LCMV-DOC on day 0 , and the DT treatment was continued throughout the experiment ( Figure 3A ) . Since a single DT injection depleted Treg cells in naïve mice for 3 days , we injected DT every 3 days to achieve complete Treg cell ablation . While this treatment effectively depleted all Foxp3+ Treg cells in naïve DEREG mice ( [54] , and data not shown ) , we consistently observed in LCMV-DOC infected DEREG mice the emergence of a residual GFPnegFoxp3+ Treg cell population not depleted by DT , presumably due to lacking expression of the GFP-DTR fusion protein ( Figure 3B ) . Compared to DT-treated WT controls , Treg cell-depleted DEREG mice exhibited a greatly enhanced morbidity in response to high dose LCMV-DOC infection , as indicated by an increased weight loss ( Figure 3C ) . As a result , a significant number of DT-treated , LCMV-DOC–infected DEREG mice had to be prematurely removed from the experiment and euthanized , whereas no equivalent morbidity was observed in DT-treated WT mice ( Figure 3D ) . Thus , Treg cell depletion – even if not absolute – substantially aggravated the disease severity in LCMV-DOC–infected DEREG mice . Depletion of Treg cells did not affect the numbers of virus-specific CD8+ T cells , as indicated by the percentage of gp33-specific CD8+ T cells in spleens and livers ( Figure 3E ) . However , Treg cell-depletion to some extent restored the functionality of the antiviral CTL and significantly increased the frequencies of gp33-specific and overall splenic CD8+ T cells producing IFN-γ upon restimulation in vitro ( Figure 3F ) . In comparison , Treg cell-depletion did not enhance frequencies of IFN-γ–producing virus-specific CD4+ T cells ( Figure 3G ) . In spite of partly restoring CD8+ T cell cytokine responses , Treg cell depletion did not influence virus control , and LCMV-DOC replicated to comparably high levels in the blood and organs of DT-treated WT and DEREG mice at 15 days post infection ( Figure 3H ) . Thus , Treg cells appeared to inhibit the functionality rather than the expansion of antiviral T cells . Consistent with the observed onset of immunopathology , the effect of Treg cell depletion on the antiviral T cell response was more pronounced at 10 days post infection ( Figure 3I–M ) . Depletion of Treg cells resulted in higher percentages of gp33-specific CD8+ T cells ( Figure 3J ) and increased percentages of cytokine-producing antiviral CD8+ and CD4+ T cells ( Figure 3K , L ) , yet did not affect viral titers ( Figure 3M ) . We next assessed the impact of increased Treg cell numbers in IL-21R−/− mice and treated both IL-21R−/− and WT mice with DT between days 8 to 15 post infection with 2000 PFU LCMV-DOC ( Fig . 4A ) . Although Treg cells were considerably ( but not entirely ) depleted , frequencies of gp33-specific CD8+ T cells remained unchanged ( Figure 4B , C ) similar to the results obtained by depletion through the entire course of infection ( Figure 3B , E ) . However , Treg cell depletion partially restored frequencies of IFN-γ-producing gp33-specific cells in IL-21R−/− mice to levels found in WT mice ( Figure 4D ) . Furthermore , Treg cell depletion lowered virus titers significantly in liver and lung of IL-21R−/− mice , although viral loads in spleen and kidney remained unaffected ( Figure 4F ) . Whether the differences in antiviral CD8+ T cells were too small to result in better virus control or whether the failure to detect differences in viral titers ( Figure 3H , M ) has to be attributed to the suppressive activity of the residual Treg cells that resisted DT depletion ( Figures 3B and 4B ) remains to be clarified . Regardless , our results establish a link between the functional impairment of the CD8+ T cell response and the elevated Treg cell levels observed during chronic infection in absence of IL-21 . We next examined the potential of Treg cells to limit the antiviral immune response and to promote virus persistence in a gain of function approach . For this purpose , we injected immune complexes ( ic ) comprised of recombinant IL-2 and the anti-IL-2 antibody JES6-1 [42] , to selectively expand the subset of Foxp3+CD4+ Treg cells in vivo ( Figure 5A ) . In naïve mice , three injections of IL-2ic drastically expanded the Treg population to represent 40–50% of all CD4+ T cells in blood , spleen and liver within 5 days after the first injection ( Figure S1 and Figure 5B ) . Similarly , IL-2ic treatment triggered a pronounced expansion of Treg cells in mice infected with 2×103 PFU LCMV-DOC , established stably elevated levels of Treg cells in blood for at least 30 days ( Figure 5B ) , and thus appeared comparable to that observed during high dose LCMV-DOC infection . The Treg cell population of infected , IL-2ic-treated mice was fully comparable to that of untreated , infected mice , with respect to cell surface expression of FR4 , GITR , CD103 and CD25 ( Figure 5C ) as well as TCR–Vβ profiles ( data not shown ) . The IL-2ic-stimulated expansion of the Foxp3+ Treg cell population profoundly interfered with generation and maintenance of gp33-specific CD8+ T cells , CD62L downregulation , and their capacity to produce IFN-γ and TNF-α as measured in spleen and liver at days 15 , 30 , and 65 post infection ( Figure 5D–G , and Figure S2 ) , which is reminiscent of the state of exhaustion that usually coincides with viral persistence in high dose LCMV-DOC infection . Accordingly , this long lasting impairment of antiviral T cells in presence of the enhanced Treg cell expansion prevented IL-2ic–treated animals from controlling a low dose LCMV-DOC infection . While infectious virus was readily cleared from the blood and most organs in control mice within 15 days , IL-2ic-induced Treg cell expansion resulted in a failure to clear virus in spleens , livers , kidneys and lungs for more than 2 months ( Figure 5H–J ) . Notably , IL-2ic expanded Treg cells also impaired antiviral CD8+ T cell effector responses and viral clearance of low dose LCMV-WE infection , which is otherwise rapidly cleared irrespectively of the viral inoculate ( Figure S3 ) . Taken together , the experimental expansion of Treg cells recapitulated both the long lasting functional impairment of the antiviral T cell response and the viral persistence that characterize high-dose LCMV-DOC infection in the setting of a low dose inoculum , and thus emphasized the remarkable potency of Treg cells to facilitate persistent viral infections . While Sprent and colleagues have clearly shown that IL-2:IL-2mAb ( JES6-1 ) primarily target high affinity IL-2R+ Treg cells and have minimal effects on low affinity IL-2R+ naïve and memory CD8+ T cells [42] , we cannot completely rule out the possibility that the IL-2:IL-2mAb also target high affinity antiviral effector CD8+ T cells resulting in terminal differentiation and exhaustion . However , this scenario appears unlikely considering the time of treatment with the IL-2:IL-2mAb complexes ( i . e . days 0–2 ) , their short half-life ( i . e . 4 h ) [42] , and the normal expansion of antiviral CD8+ T cells until day 7 . The above data demonstrate the differential regulation of the Treg cell population during viral infection by IL-2 and IL-21 . To better understand counter-regulation of Treg cells by IL-2 and IL-21 in the absence of confounding virus dynamics , we delivered the IL-21 gene by hydrodynamic injection to IL-2ic treated mice in the absence of viral infection ( Figure 6 ) . Indeed , IL-21 significantly inhibited IL-2ic driven expansion of Treg cells . Similar results were obtained by co-injection of an engineered IL-21-Fc fusion protein together with IL-2ic . Together , these data indicate that IL-21 interferes with IL-2 driven expansion of Treg cells to optimize antiviral effector T cell responses .
T cell exhaustion represents a state of T cell dysfunction associated with clinically relevant diseases , such as persistent viral infections or cancer . Even though the molecular signature of exhausted T cells has been characterized in detail at the functional and transcriptional level [32] , [34] , we are only beginning to understand the immunological mechanisms that support or counteract the development of T cell exhaustion during chronic infections [35] . In this study we report two major findings that establish a pathway of T cell exhaustion mediated by Treg cells during viral infection , and indicate its modulation by both , the pathogen and the host . First , we show that a persistence-inducing virus triggers the massive proliferation of Treg cells between days 9–20 post infection and demonstrate the potential of Treg cells to promote T cell exhaustion and chronic infection . Second , we identify IL-21 as a crucial host factor that antagonizes this virus-driven expansion of the Treg cell population . Together , these results suggest enhanced Treg cell responses as a mechanism of immune evasion that could be therapeutically targeted with IL-21 . Treg cells are essential for immune homeostasis and T cell tolerance [8] , [9] , yet their contribution to anti-infectious immune responses is poorly defined . In the setting of persistent viral infections that was investigated here , Treg cells appear to down-regulate antiviral T cell responses , and thus prevent the potentially lethal immunopathology caused by prolonged immune activation in presence of highly replicating virus ( Figure 3 ) . Supporting this notion , clinical studies have associated high numbers of Treg cells to chronic infection with HIV [29] , HCV [28] , [55] , or HBV [56] , whereas low Treg cell numbers have been reported for elite HIV controllers [57] , which collectively suggests that Treg cells modulate the equilibrium between host immune response and persisting virus [27][34] . Our study has now recapitulated these observations in a murine model of persistent viral infection and applied experimental manipulation of the Treg cell response to define the direct link between virus-induced Treg cell expansion and T cell exhaustion . Our initial observation that titrated doses of LCMV-DOC induce graded degrees of Treg cell proliferation and T cell exhaustion allowed us to assess the impact of such virus-induced Treg cell expansion under “non-persisting conditions” using the identical LCMV strain . Although the expansion of Treg cells with IL-2ic achieved very high Treg cell numbers , it is important to note that this level of Treg cell expansion was fully comparable to that induced with LCMV-DOC at high doses or in IL-21R−/− mice . The Treg cell responses triggered by LCMV-DOC in presence or absence of IL-2ic treatment did not phenotypically differ with respect to their cell surface marker expression and exhibited TCRβ profiles similar to that described for LCMV clone 13 infection [58] . Thus , the IL-2ic-elicited Treg cell proliferation appeared to truly mimic the physiological Treg cell expansion during chronic LCMV-DOC infection . These experiments exposed two facets of the antiviral Treg cell response in LCMV-DOC infected mice that are central for our understanding of the role and potential of Treg cells within antiviral and anti-tumoral responses . First , the increased morbidity in Treg-depleted DEREG mice infected with LCMV-DOC clearly revealed the critical role of Treg cells for preventing lethal immunopathology caused by potent immune responses to persisting antigen . Second , both the depletion as well as the gain of function approach demonstrated that Treg cells primarily modulate the functionality ( e . g . cytokine response , antiviral activity ) of antiviral T cells rather than influencing their priming consistent with an earlier report showing compromised cytolysis but no defect in priming , proliferation and motility of regulated CTLs [59] . This finding is especially promising , since it implies that the primed but exhausted antiviral T cells present in chronically infected subjects could be therapeutically rescued by removal of Treg cell-mediated suppression . Dynamics of regulatory and effector T cell populations in homeostasis and during an immune response are very sensitive to the availability of IL-2 . Competition for IL-2 between effector and regulatory T cells has been suggested to control tolerance and immunity or the outcome of infectious disease . Associated with the rapid expansion of virus-specific CD8+ T cells in the early phase of infection between days 0 to 8 , we observed a remarkable drop in Treg cells below naïve levels irrespective of the LCMV inocula and strain used . This has also been observed in other infections and suggested to be due to consumption of IL-2 by expanding CD8+ T cells and required for efficient clearance of the invader [47] , [48] . While this appears feasible , it should be noted that expansion of CD8+ T cells in the acute phase of LCMV infection is independent of IL-2 [60] , [61] and that hyper-proliferation of Treg cells between days 10–20 in chronic LCMV infection was associated with potent suppression of IL-2 production by CD8+ T cells ( Figure 1F , G ) arguing that IL-2 availability does not sufficiently explain cross-regulation of effector CD8+ T cells and Treg cell proliferation in acute and chronic LCMV infection . In vitro experiments suggested that IL-21 could inhibit Treg cells by suppression of IL-2 production in CD4+ effector T cells [49] . However , our results in the WT:IL-21R−/− mixed BM chimeric mice demonstrate that IL-21 inhibits Treg cell expansion directly in a cell intrinsic manner ( Figure 2E–G ) . The finding that expansion of Treg cells induced by IL-2ic treatment was impaired by simultaneous ( hydrodynamic ) overexpression of the IL-21 gene ( Figure 6 ) further supports this conclusion . Thus , IL-2 and IL-21 exert opposing activities on Treg cells , while they cooperate in driving effector and memory T cell responses , which adds another level of complexity to theoretical and experimental models addressing the dynamics of Treg cells and effector T cells [45] . Amongst the known Treg cell effector molecules , IL-10 has been shown to support the functional impairment of T cell responses during chronic infection with LCMV clone 13 [36] , [37] . However , we were unable to detect any IL-10-producing Treg cells in LCMV-DOC infected mice ( Figure 2J ) . Furthermore , IL-10 blocking antibodies or genetic IL-10-deficiency did not prevent T cell exhaustion and viral clearance in response to LCMV-DOC , in contrast to LCMV clone 13 infection [62] . The exhausted T cells in chronically LCMV infected mice have been shown to upregulate expression of several co-inhibitory receptors , e . g . PD-1 and Tim3 , which contribute to T cell exhaustion in the LCMV model [63] , [64] and in human HIV patients [65] , [66] . It will thus be important to test whether these pathways are involved in the Treg cell-induced T cell exhaustion described in our study . Though a detailed characterization is beyond the scope of the current analysis , the experiments described in this manuscript will provide the framework for further mechanistic studies . IL-6 and IL-21 have similar activities and interact in the cross-regulation of inducible Treg and Th17 cell development in vitro and in vivo depending on the experimental model [51] , [52] , [53] , [67] , [68] . Interestingly , while IL-6 has recently also been shown to be essential for viral control by enhancing follicular T helper cell responses at late stages of chronic infection [69] , virus titers and Treg numbers were comparable in LCMV-DOC infected IL-6−/− and WT mice up to day 30 post infection ( Figure 2K–N ) [69] . Therefore , during chronic LCMV-DOC infection , the inhibition of virus-induced Treg cell expansion is a distinct function of IL-21 , which is not accompanied by elevated IL-17 production of CD4+ T cells [40] . The importance of cell-intrinsic IL-21R signaling for the maintenance of CD8+ T cell functionality has been well documented [39] , [40] , [41] and is considered as the primary effect of IL-21 promoting the immune control of chronic viral infections . However , the present report clearly demonstrates that besides this direct function on CD8+ T cells , IL-21 also efficiently restricts the virus-induced expansion of the Treg population in a cell intrinsic manner . It remains to be clarified to which extent the direct effects of IL-21 on CD8+ T cells and its indirect effects on CD8+ T cells via the inhibition of Treg cells differentially contribute to the overall protective function of IL-21 in chronic viral infections considering that we only achieved limited recovery of T cell functionality ( e . g . regain of cytokine production ) and improved viral clearance only in lung and liver but not spleen and kidney of IL-21R−/− mice by Treg depletion . It should be noted , however , that , for reasons unknown , a fraction of ( GFPnegFoxp3+ ) Treg cells resisted depletion by diphtheria toxin during LCMV infection in both IL-21R−/− and WT mice , although Treg depletion in naive mice was almost complete ( >98% ) . It remains to be investigated whether the undeletable GFPnegFoxp3+ cells can compensate for the deleted Treg cells or represent a subpopulation of Treg cells that is responsible for maintenance of regulatory activity in LCMV-infected DEREG mice . Regardless , our findings suggest a dual importance of IL-21 for preventing T cell exhaustion during chronic viral infections , and demonstrate that IL-21 in addition to its known direct effects on antiviral T cells [39] , [40] , [41] also partially alleviates the suppressive activity of Treg cells . Notably , in a model of acute lung infection , it was recently demonstrated that IL-21R−/− mice are protected from fatal lung immunopathology induced by pneumonia virus [70] . It is tempting to speculate that IL-21 might aggravate immunopathology by suppression of Treg cells in this infection model . In summary , our data support the concept of virus-induced Treg cell expansion as an active immune evasion strategy , and thus highlight a novel pathway by which viruses exploit regulatory mechanisms of the immune system to establish persistent infection . In view of the relevance to human disease these results have direct therapeutic implications and suggest strategies that boost IL-21 signaling in T cells as novel treatment options for chronic viral infections and cancer .
The LCMV strains WE and DOC were originally provided by Rolf Zinkernagel ( University of Zurich , Switzerland ) and were propagated on L929 or MDCK cells , respectively . C57BL/6 WT and IL-6−/− mice [71] were from Charles River Inc . SMARTA-2 mice ( expressing a transgenic TCR specific for LCMV-GP 61–80; [72] and IL-21R−/− mice [73] were bred locally . DEREG-mice [54] were kindly provided by Tim Sparwasser ( TWINCORE , Hannover , Germany ) and crossed with the IL-21R−/− strain at our facility . Il21-mCherry/Il2-emGFP dual-reporter transgenic mice [50] were kindly provided by Warren Leonard , National Institutes of Health , Bethesda , MD , USA . Mice were housed in individually ventilated cages under specific pathogen free conditions at BioSupport AG ( Zurich , Switzerland ) . For the generation of BM chimeras , recipient mice were lethally irradiated ( 9 . 5 Gy , using a cesium source ) one day before reconstitution with 1×107 CD4/CD8-depleted ( Miltenyi Biotec ) BM cells . Mice were infected i . v . with the indicated virus doses . Ethics statement: All animal experiments were approved by the local animal ethics committee ( Kantonales Veterinärsamt Zürich , licenses 217/2008 and 113/2012 ) , and performed according to local guidelines ( TschV , Zurich ) and the Swiss animal protection law ( TschG ) . All cell lines were originally obtained from the American Tissue Culture Collection ( ATCC ) . Chemicals were purchased from Sigma-Aldrich except were otherwise stated . PE- and APC-conjugated peptide-MHC class I tetramers ( H-2Db/gp33-41 ) were generated as described [74] or kindly provided the NIH tetramer core facility . The LCMV-GP peptides gp33-41 ( KAVYNFATM ) and gp61-80 ( GLNGPDIYKGVYQFKSVEFD ) were bought from Mimotopes . The following antibodies ( all eBioscience unless otherwise stated; clone names given in parentheses ) were used for flow cytometry: FITC-labeled anti-CD4 ( L3T4 ) , anti-CD62L ( MEL-14 ) ; PE-labeled anti-CD4 ( GK1 . 5; conjugated in our laboratory ) , anti-CD8α ( 53-6 . 7; BioLegend ) , anti-CD25 ( PC61 ) , anti-GITR ( DTA-1; BioLegend ) , anti-CD103 ( 2E7 ) , anti-FR4 ( 12A5; BioLegend ) , anti-IL21R-biotin ( 4A9 ) – Streptavidin-RPE ( BioLegend ) and anti-TNF-α ( MP6-XT22 ) ; PerCP-labeled anti-CD4 ( RM4-5; BioLegend ) , anti-CD45 . 1 ( A20; BioLegend ) , anti-CD8 ( 53-6 . 7; BD ) ; APC-labeled anti-CD4 ( GK1 . 5 ) , anti-CD127 ( SB/199; Biolegend ) , anti-CD8 ( 53-6 . 7; BioLegend ) , anti-CD45 . 2 ( 104 ) , anti-IL21R ( 4A9; BioLegend ) , anti-Foxp3 ( FJK-16S ) , anti-IFN-γ ( XMG1 . 2; BioLegend ) , anti-IL-2 ( JES6-5H4 ) , anti-IL-10 ( JES5-16E3 ) . In depletion experiments , DEREG and WT control mice were i . p . injected with DT ( Merck ) diluted in PBS . After an initial dose of 200 ng DT , mice were treated with 100 ng DT every third day unless otherwise indicated . To boost Treg cells , mice received 3 daily i . p . injections of IL-2ic generated from carrier-free recombinant mouse IL-2 and anti-IL-2 mAb ( JES6-1A12; both from eBioscience ) as described [42] . Tetramer and antibody staining was performed on blood cells and single cell suspensions prepared from organs . Spleens and kidneys were passed through a 70 µM cell strainer to obtain single cell suspensions . Livers were first dissected into small pieces , and then passed through a cell strainer before lymphocytes were purified by Lympholyte M gradient centrifugation ( Cedarlane Laboratories Ltd . ) . Blood samples were pretreated with red blood lysis buffer ( 155 mM NH4Cl , 10 mM KHCO3 , 0 . 1 mM EDTA , pH 7 ) for 10 min at RT . Cells were incubated with anti-CD16/CD32 mAb ( 2 . 4G2 ) to block FcγR . For surface staining , cells were incubated at RT with peptide MHC I tetramers in FACS buffer ( FB; PBS containing 0 . 5% BSA ) for 15 min followed by addition of the relevant surface antibodies and incubation for additional 20 min at 4°C . Cytokine-production by T cells was assessed using intracellular cytokine staining of single cell suspensions that had been stimulated in presence of 2 µg/ml Monensin with either 1 µM specific peptide or 100 ng/ml PMA and 1 µg/ml Ionomycin for 4 hours in vitro . The cells were surface-stained , fixed with 4% Formaldehyde in PBS and permeabilized with permeabilization buffer ( FB containing 1% Saponin ) . Intracellular staining was then performed in permeabilization buffer at 4°C for 20 minutes . After 2 washes with permeabilization buffer , cells were resuspended in FB . All samples were acquired on a FACSCalibur with CellQuest software ( both BD Biosciences ) and analyzed using the FlowJo software ( Tree Star Inc . ) . Blood samples were obtained from LCMV-infected mice at indicated times , diluted 5-fold in MEM ( 5% FCS ) containing 50 U . I . of Liquemin ( Drossapharm ) and frozen . Organs were collected in 1 ml MEM ( 5% FCS ) and smashed with a Tissue Lyser ( Qiagen ) . Samples were stored at −80°C until further analysis by plaque forming assay [75] . Responder CD4+ T cells were purified from naïve spleens by positive MACS separation ( Miltenyi Biotec ) and labeled with 25 µM CFSE ( Molecular Probes , C-1157 ) at a density of 106 cells/ml in PBS containing 0 . 5% BSA for 7 min at RT . The labeling reaction was stopped with pure FCS and cells were washed twice with IMDM containing 10% FCS . As suppressor cells , CD25+ Treg cells were FACS-sorted from MACS-purified CD4+ T cells isolated from LCMV-DOC infected DEREG and IL-21R−/− DEREG mice . CFSE-labeled responder CD4+ T cells ( 1×105/well ) and sorted Treg cells were then incubated at defined responder/suppressor ratios ( 1∶1 , 2∶1 , 4∶1 ) in RPMI ( 10% FCS , 50 µM β-ME and 100 U/ml IL-2 ) for 6 days in the presence of 5×106 anti-CD3/CD28-coated ( both eBioscience ) latex beads . Mouse IL-21 coding sequence was amplified by PCR and linked with hIgG1 Fc domain and cloned into pLIVE in vivo expression vector ( Mirus Bio ) . Endotoxin-free plasmid DNA ( 100 µg ) was injected i . v . in PBS in a volume equal to 10% body weight ( 0 . 1 ml/g ) within 5 s . As a control , a hIgG1 expression vector was injected . To supplement IL21 , mice received i . p . injections of 2 µg recombinant IL-21-hIgG1 fusion protein ( kindly provided by Daniel Christ , Garvan Institute for Medical Research , Sydney , Australia ) or PBS two times daily . Data are shown as average ±SEM . Statistical analysis was performed with the unpaired two-tailed t–test ( except for Fig . 1B–E ) using the Prism 4 . 0 software ( GraphPad Software ) . Differences were considered significant for p<0 . 05 and were denoted as * , p<0 . 05; ** , p<0 . 01; *** , p<0 . 001 .
|
T cell exhaustion represents a state of T cell dysfunction associated with clinically relevant diseases , such as persistent viral infections or cancer . Although the molecular signature of exhausted T cells has been characterized in detail at the functional and transcriptional level , the immunological mechanisms that lead to T cell exhaustion during chronic infections remain poorly understood . Our present study reports two major findings that illustrate a pathway that contributes to T cell exhaustion during viral infection , and indicate its modulation by both , the pathogen and the host . First , we show that a persistence-inducing virus triggers the massive proliferation of Foxp3+ regulatory T ( Treg ) cells and demonstrate the potential of Treg cells to promote T cell exhaustion and chronic infection . Second , we identify IL-21 as a crucial host factor that antagonizes this virus-driven expansion of the Treg population in a cell intrinsic manner independent of IL-2 . Thus , in addition to its known pre-dominant direct positive effects on antiviral T cells , IL-21 can also alleviate the suppressive activity of Treg cells . Together , these results suggest enhanced Treg cell responses as a mechanism of immune evasion that could be therapeutically targeted with IL-21 .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"immunopathology",
"medicine",
"infectious",
"diseases",
"lymphocytic",
"choriomeningitis",
"immunology",
"biology",
"viral",
"diseases"
] |
2013
|
IL-21 Restricts Virus-driven Treg Cell Expansion in Chronic LCMV Infection
|
Sequence conservation and co-variation of base pairs are hallmarks of structured RNAs . For certain RNAs ( e . g . riboswitches ) , a single sequence must adopt at least two alternative secondary structures to effectively regulate the message . If alternative secondary structures are important to the function of an RNA , we expect to observe evolutionary co-variation supporting multiple conformations . We set out to characterize the evolutionary co-variation supporting alternative conformations in riboswitches to determine the extent to which alternative secondary structures are conserved . We found strong co-variation support for the terminator , P1 , and anti-terminator stems in the purine riboswitch by extending alignments to include terminator sequences . When we performed Boltzmann suboptimal sampling on purine riboswitch sequences with terminators we found that these sequences appear to have evolved to favor specific alternative conformations . We extended our analysis of co-variation to classic alignments of group I/II introns , tRNA , and other classes of riboswitches . In a majority of these RNAs , we found evolutionary evidence for alternative conformations that are compatible with the Boltzmann suboptimal ensemble . Our analyses suggest that alternative conformations are selected for and thus likely play functional roles in even the most structured of RNAs .
RNA is unique in that it is both a messenger of genetic information and it can fold to adopt highly specific functional conformations that carry out catalysis in the cell [1]–[4] . Large RNAs have a high propensity to misfold , requiring chaperones and in many cases protein co-factors to achieve an active conformation [5]–[7] . Riboswitches are a class of RNAs that must adopt at least two conformations to function , since it is ligand binding induced conformational change that allows them to regulate transcription and/or translation [8]–[12] . These molecules present an interesting evolutionary challenge since the sequence space should allow both conformations [13]–[16] . Furthermore , even small changes in sequence can significantly alter their structure and favor non-functional conformations [11] , [17] . Co-variation of RNA bases across species is one of the strongest signals in biological sequences [18]–[21] and is observed when homologous sequences of RNAs are aligned [22]–[24] . The near perfect isostericity of the canonical G-C and A-U base-pairs results in their interchangeability in most RNA stems [25] , [26] . For an RNA that adopts a single conformation to carry out its function ( e . g . a group I intron ) , we expect that the ensemble of co-varying pairs should point to a single structure . For riboswitches , which must adopt at least two conformations we hypothesize that co-variation should be observed in alignments supporting both conformations . The purine riboswitch is the simplest system in which we hypothesize it should be possible to observe co-variation supporting alternative conformations [27]–[29] . The system is schematically represented in Figure 1A and includes two domains ( P1 , P2 , P3 , which is the aptamer domain ) and the terminator stem . We aim to determine the relative co-variation support for the P1 , terminator and anti-terminator stems of the purine riboswitch to characterize the evolutionary signal for RNAs known to function through multiple conformations . Our analysis of this relatively simple conformational switch provides insight into the strength of the evolutionary signal that can be expected supporting multiple conformations . By then applying a similar analysis to other RNA alignments we aim to determine the likelihood of functionally important alternative conformations in structured RNA .
We begin our investigation into the evolutionary evidence supporting alternative conformations by considering an RNA that adopts at least two conformations to carry out its function . The purine riboswitch changes conformation in the presence of its' ligand ( generally a purine base or derivative ) to regulate protein biosynthesis [30] . Figure 1A represents the secondary structure of the “off” conformation for the consensus purine riboswitch as determined by crystallography [31] . The structure includes the characteristic P1 , P2 and P3 stems of the purine riboswitch as well as the terminator hairpin , which has not been solved by crystallography [32] , [33] . The mechanism of action of this riboswitch is particularly relevant to our study as it involves a significant secondary structure rearrangement . In the “on” state , the P1 stem is not base-paired; instead the anti-terminator is formed ( indicated with red lines in Figure 1A ) . Given that both the “on” and “off” states of the riboswitch are functionally essential , one might expect to see co-variation in the anti-terminator base-pairs across species of purine riboswitches . Co-variation models ( CM ) used to identify purine riboswitches in genomic sequences usually include only the aptamer domain . This is the case of Rfam family RF00167 , which is the starting point of our analyses for this investigation [34]–[36] . To determine the level of co-variation support for the P1 , terminator and anti-terminator stems in the purine riboswitch we aligned the 246 sequences from RFAM family RF00167 in which we could identify a terminator stem within 100 nucleotides of the aptamer domain [36] , [37] . We then computed mutual information ( MI ) and inconsistency scores for the columns in the alignment corresponding to the three stems . These two scores evaluate the extent of co-variation between two columns in the alignment . A high MI score indicates that a specific base in one column is highly predictive of the base that is in the second column . A low inconsistency score indicates that in most cases the co-variation is between canonical base-pairs ( G-C to A-U for example ) . The MIfold package was used to compute these scores considering only canonical and G-U base-pairs , which serves as the metric for co-variation of pairs of nucleotides in this study [18] . A base-pair with high MI and low inconsistency is therefore well-supported by evolutionary evidence . We find that the MI and inconsistency metrics are inversely correlated , as expected and this suggests that our results are mostly metric agnostic ( see Figure S7 ) . These data are often used to confirm and/or improve RNA secondary structure predictions [19] , [21] , [38] . In Figure 1B , the first AU base-pairs in both the P1 and Terminator Stem ( TS ) have high MI values , but we also observe equivalent evolutionary evidence ( MI 0 . 97 , inconsistency 0 . 04 ) for the anti-terminator ( AT ) pair . This is consistent with the RNA adopting multiple conformations when it acts as a ligand-induced switch . We observe similar trends for the three other P1/AT/TS pairs reported in Figure 1B . Additional MI and inconsistency values for the purine riboswitch are reported in Table S1 and confirm this trend . We therefore observe equivalent evolutionary support in the purine riboswitch alignment for the anti-terminator stem relative to the P1 and terminator stems . A riboswitch changes conformation upon ligand binding , allowing it to regulate transcription and/or translation [27]–[29] . To determine whether thermodynamic folding models support alternative conformations we performed Boltzmann suboptimal sampling of the Streptococcus pneumoniae purine riboswitch sequence , including the five single point mutations that most significantly affect structure as determined by SNPfold [11] , [39] . Principal component analysis of 10 , 000 Boltzmann sampled suboptimal structures based on binary base-pair vectors reveals three major clusters of structures [11] , [39] , [40] , with cluster probabilities of 20% ( red ) , 56% ( blue ) and 24% ( green ) . The “off” conformation of the riboswitch ( Figure 2A , green cluster , the terminator stem is formed ) as well as two alternative “on” conformations where the anti-terminator stem ( red and blue clusters ) are represented in the ensemble . The high co-variation base-pairs identified in Figure 1 are highlighted in red ( anti-terminator ) and black ( terminator , P1 ) in Figure 2A , indicating that the Boltzmann ensemble is consistent with the evolutionary analysis presented in Figure 1 . Suboptimal sampling reveals that there are two classes of “on” conformations possible ( red and blue clusters ) , which is not necessarily evident from the co-variation data alone . In addition , it is possible to classify each suboptimal structure into five mutually exclusive categories depending on the structural features present: P1 stem , terminator stem , P1 & terminator , Anti-terminator stem , and no P1 , terminator or Anti-terminator stems . When we classify each of the 10 , 000 suboptimally sampled structures represented in Figure 2A , we see that a majority ( 55 . 6% ) fall into the antiterminator stem category ( Figure 2B ) . We also find that 15 . 7% of the structures have both P1 and the antiterminator stem formed ( Figure 2B , yellow bar ) . Interestingly , 20 . 7% of the suboptimal structures adopt conformations where none of the characteristic riboswitch features are present ( No P1 , terminator or Anti-terminator ) . These data suggest that although the principal component visualization used in Figure 2A suggests three major classes of structures , the RNA suboptimal ensemble is even more complex . This is borne out by the fact that only 37 . 6% of the variance of the structural ensemble is captured in the first two principal components . It is also due to the fact that the PCA space is determined by overall structural similarity and not the restricted analysis of the terminator , P1 and antiterminator stems reported in Figure 2B . In addition , we purposefully explored riboswitch conformational space by including select point mutants that increase structural diversity to generate the principal component space [11] , [39] . We performed suboptimal sampling on all the purine riboswitch sequences previously identified from RFAM RF00167 that include a terminator sequence . We analyzed each ensemble identifying the P1 and terminator elements in the suboptimal structure and plotting their relative abundance using the same coloring scheme as in Figure 2B . We report these data projected onto the phylogenetic tree from our riboswitch alignment in Figure 2C . These data reveal a qualitative correlation between structural partitioning and phylogenetic origin of the riboswitch . Certain riboswitch sequences have evolved to adopt predominantly one structure ( e . g . the top half of Bacilli riboswitches are predominantly “off” with P1 and terminator stems formed , while the Desulfotomaculum sequence is likely on , with predominantly the antiterminator stem formed ) . Thus , evolution appears to fine-tune the partitioning of the Boltzmann ensemble to favor specific conformations . The data presented in Figures 1 and 2 agree with our understanding of riboswitch function and the need for these RNAs to adopt at least two conformations to carry out their function . It is not surprising to find evolutionary evidence supporting alternative secondary structures in riboswitch alignments . However , catalytic ribozymes , such as the Tetrahymena thermophila group I intron must adopt a single structure to precisely organize the catalytic site and carry out their function [5] , [41]–[47] . We might expect to see less evolutionary evidence for alternative RNA conformations in a “highly structured” family of RNAs like the group I introns [5] , [48]–[50] . To test this hypothesis we performed an analogous MI analysis on the group I intron alignment from the Comparative RNA Web ( CRW ) database [51] . Base-pairs with MI values above three different thresholds on a circle diagram representing the T . thermophila group I intron are illustrated in Figure 3A . The secondary structure derived from the crystal structure of the intron is represented in dark gray [52] , base-pairs in the accepted structure with MI values greater than the threshold are indicated in red . Green base-pairs have MI values above the threshold but are not in the crystal structure [52] . In Figure 3B , the same coloring scheme is used to project high-MI canonical pairs onto a crystal structure informed model of the three-dimensional structure of the intron [53] . Visual inspection of the three-dimensional structure reveals that most of the high-MI pairs that are not secondary structure are long range , spanning a significant section of the 3D structural model [25] , [54] . The green pairs illustrated in Figures 3A and 3B are “false-positives” in terms of the prediction of the crystal structure pairs . Extending this logic , red pairs are “true-positives , ” and gray pairs are “false-negatives . ” The rates of true , false , positives and negative vary with MI making it possible to compute sensitivity and Positive Predictive Value ( PPV ) as a function of the threshold . We computed PPV and sensitivity for these data and report the resulting curves in Figure 3C . The sum of PPV and sensitivity ( green curve , Figure 3C ) reveals the reason behind our choice of three MI values as illustrative thresholds , with an MI value of 0 . 41 representing the minima in PPV and Specificity , and the value of 0 . 78 the maximum . The data plotted in Figure 3A clearly show strong MI evidence is found in the group I intron alignment for non-crystal base-pairs at all MI thresholds ( green lines ) . It is important to point out that above the highest MI threshold ( 0 . 78 ) the sensitivity ( gray line , Figure 3B ) is 33% , i . e . a majority of crystal base-pairs are not supported by MI . The significant number of gray base-pairs in Figures 3A and 3B at the 0 . 78 threshold demonstrate that some of the stems are not supported by even a single co-varying base-pair . In their seminal determination of the T . thermophila intron structure , Michel and Westhof did not take into account the non-accepted ( green ) base-pairs for their structural model [38] . These are incompatible with a single structure model and have previously been considered false-positives for structure determination . In general , the paradigm for RNA structure prediction based on co-variation analysis has been to identify the structure that is compatible with the maximum number of co-varying pairs based on the idea that a specific RNA sequence folds to a single conformation [18] , [19] , [21] . Another explanation for the non-accepted base-pair co-variation observed in the group I intron alignment is non-canonical base-pairing and/or tertiary ( 3D ) interactions [25] , [54] , [55] . We expect that if the green pairs in Figure 3A are due to short-range non-canonical 3D interactions , these nucleotides should be close in space in the crystal structure of the group I intron [52] . When these are projected onto the three-dimensional structure of the intron as in Figure 3B , these pairs are not close in three-dimensional space . The mean pair distances for all the pairs above the MI threshold that are not in the accepted structure is plotted in Figure 3D . As a reference the mean distance for accepted base-pairs in the structure ( 18 Å ) is indicated as a red line ( gray indicates ±1 standard deviation ) , while the mean pairwise distance for all pairs ( 48 Å ) is indicated as a green line . We can see that at all MI thresholds , the green pairs are longer range than the expected 18 Å average of a canonical base-pair . It is therefore not likely that this evolutionary signal arises due to long-range tertiary contacts in the RNA . We repeated the analysis performed on group I introns alignments for six other RNA families and summarize our findings in Table 1 , Figures S1 , S2 , S3 , S4 , S5 , S6 include analogous PPV an sensitivity plots for these RNAs . High-MI , low inconsistency pairs are found in all the RNAs we studied that are incompatible with the crystal structure in approximately the same proportion as what we observed with the purine riboswitch and group I intron alignments . More importantly , it is not possible to discern between RNAs that are generally thought to adopt a single conformation ( e . g . tRNA ) and multiple conformations ( e . g . riboswitches ) . Effectively , when viewed through the lens of co-variation , all RNAs are the same in terms of their propensity to evolve alternative conformations . We performed column shuffling on the alignments using the RNAz “rnazRandomizeAln” algorithm to determine the expected number of alternative high-MI pairs [56] , [57] . The RNAz algorithm is designed to maintain local conservation patterns by only shuffling columns in the alignment with similar degrees of conservation [24] , [57] . One limitation of this approach is that no crystal structure exists as a standard for identifying long-range high-MI base-pairs . Furthermore , RNAAlifold predictions based on the shuffled alignment result in sparsely paired RNAs [18] , [58] . We therefore generated a reference structure by considering all base-pairs above a threshold MI so as to have an equivalent number of pairs in the reference as in the crystal . Using this reference we computed expected PPV and sensitivity values for each shuffled alignment and report these in Table 1 . Our data indicate similar PPV and sensitivity values to those computed using non-shuffled alignments in the previous section , albeit with on average slightly higher sensitivity for lower MI thresholds . These results suggest that the evolutionary process does not necessarily select for or against multiple conformations but instead tolerates these from those that are expected by chance .
From a chemical perspective , RNA is one of the simplest biopolymers in the cell being composed of purine and pyrimidine bases linked by a phosphodiester backbone [59] , [60] . It is remarkable that despite this simple chemistry , RNA can fold into complex three-dimensional structures that are capable of catalysis [61]–[63] . However , the simplicity of the RNA nucleotide “alphabet” is at the heart of the structural diversity of the suboptimal ensemble of structures [40] , [43] , [64] . Indeed , the limited base-pairing partners for any of the four bases makes it much more likely to find multiple complementary regions in a given RNA sequence [65] . To illustrate this concept , we roughly estimate that an RNA sequence longer than 314 nucleotides can adopt more conformations than there are atoms in the Universe ( see methods ) . A consequence of this is the remarkable result observed when a program like Sfold ( which performs Boltzmann sampling ) is run twice in a row . The number of identical structures in the two suboptimal samplings of 1000 structures for the same RNA is usually between 2 to 3 for RNAs of moderate lengths [40] and can often be zero for long RNAs [43] , [65] . The probability of the minimum free energy structure in the Boltzman ensemble is in fact negligible for most large RNAs [40] , [66] . Our findings of high MI base-pairs inconsistent with a single secondary structure , but always found in the suboptimal ensemble , suggest that these alternative structures are tolerated by evolution . Given the difficulty of evolving an RNA to adopt a single conformation , it is likely that regulatory systems involving RNA have adapted to these alternative conformations and in some cases even selected for them . Adopting a single conformation is not necessarily a pre-requisite for biological function as long as a significant fraction of the RNAs do adopt the active conformation at any given time . The functional role of alternative conformations in riboswitches is well established [9] , [28] . The data we present in Figure 1 is consistent with at least two structures . Interestingly , Boltzmann sampling of the suboptimal ensemble of the purine riboswitch ( Figure 2A ) reveals three major conformations ( blue , green and red ) . However , even this classification is somewhat of an oversimplification given that the first two principal components only capture a little more than a third of the structural complexity of the suboptimal ensemble . As such , evolving an RNA to adopt a single conformation represents a daunting task , even for an evolutionary process spanning billions of generations . Our data suggest that RNAs are evolved to adopt multiple conformations , even catalytic ribozymes . The data presented in Figure 2C is particularly intriguing from an evolutionary perspective . Indeed , we find that highly related riboswitches seem to preserve ensemble partitioning . This is not a priori surprising , since ensemble partitioning is likely important to function in the cell . We and others have recently shown , however , that there are specific mutations in all RNAs that are highly disruptive to structure ( in many cases these are disease-associated ) and that these single point mutations affect ensemble partitioning [39] , [67] , [68] . The high degree of similarity in the different clades of riboswitches in terms of their ensemble partitioning ( Figure 2C ) suggests that evolution avoids these disruptive mutations . This is consistent with the importance of not only preserving the ability to adopt multiple conformations , but also avoiding deleterious mutations that disrupt it . An important consideration in interpreting our data is the role of RNA co-transcription and kinetic traps in folding to an active conformation [69] . The binding of exogenous molecules ( including RNA chaperones ) can significantly impact folding outcome [7] , [70] . Furthermore , post-transcriptional modifications of RNA will necessarily change the folding landscape [71] , [72] . The sequence ultimately selected by the evolutionary process is therefore under many different forms of selective pressure . Our analysis suggests that alternative conformations are neither selected for or against , but these may just be a consequence of selecting for a sequence that has phenotypically advantageous co-transcriptional folding pathways . Our analysis is also based on a comparison of homologous sequences in a family of RNAs with the assumption that they all have similar functional roles . Some of the alternative conformations consistent with high-MI base-pairs may also be a result of conserved RNA scaffolds adopting alternative function . The ability to adopt specific alternative conformations may confer significant evolutionary advantages to RNAs . Near isoenergetic conformations are ideal for ligand induced switching , since binding of a specific ligand can easily shift the ensemble partitioning . Catalytic ribozymes , on the other hand must adopt a single and specific conformation to carry out catalysis . However , a majority of ribozymes readily misfold and this suggests these molecules may also act as switches in the cell [6] , [49] , [73] . Indeed , RNA chaperones help resolve these misfolds in an ATP dependent manner , suggesting a possible bi-molecular regulatory switch [70] . The ability of RNA to adopt multiple alternative conformations may in fact confer a significant evolutionary advantage in terms of adaptability and ability to control regulatory networks . As such , it is not surprising to find RNA playing such a key role in the central dogma of biology .
The purine riboswitch alignment was obtained from the RFAM [34]–[37] database ( http://rfam . sanger . ac . uk/family/RF00167 ) . The alignment in only included nucleotide positions corresponding to the P1 , P2 and P3 stems . Each sequence was therefore extended by 100 nucleotides in the 3′ direction in order to allow for the ability to search for terminator stems . The RNIE software package was used to scan each of the sequences for Rho-independent terminators [74] . Sequences without a predicted terminator were removed from the analysis . The 3′ alignment of the P1 stem was then folded with the 5′ region of the predicted terminators in RNAfold to search for potential anti-terminator pairs [58] , [75] . Sequences without any predicted pairs were further removed from the analysis . This resulted in a final set of 246 sequences that were used for the co-variation analysis presented in Figure 1 . All alignments used for the analysis in this paper are provided in the supplement in Stockholm file format [76] , [77] . A simple sequence alignment strategy was not sufficient to correctly align the terminator stems in the purine riboswitch alignment . Alignments were thus adjusted to reflect the predicted terminator stems identified using by RNAfold minimum free energy predictions of each individual sequence [58] . There are 8 possible pairing positions in the P1 stem . The nucleotides within the 5′ predicted terminator were aligned according so as to correspond with the predicted base-pairs on the 3′ end of the P1 stem . The corresponding 3′ terminator nucleotides were then retrieved and aligned according to the original predicted terminator pairs . This results in the consensus terminator stem formed by 4 base-pairs shown in Figure 1 . Additional alignments used in this manuscript were retrieved either from the comparative RNA website hosted by the Gutell Lab at the University of Texas at Austin or the RFAM database [51] . The alignments were refined to the most mutationally diverse and gap limited sequences according to the reference sequence and structure . The final analysis included 1332 16S , 2204 23S , 246 purine riboswitch , 289 group I intron , 1642 Glycine ( RF00504 ) and 601 GMP ( RF01051 ) riboswitch sequences . Mutual Information scores were calculated using the MIfold MATLAB package [18] , [78] . The ‘M’ algorithm specified in the package was used to calculate the mutual information scores . This formula computes the score as the information content describing the degree to which the two positions in the alignment can or cannot form a base pair . Canonical and wobble base pairs are specified as the only pairs allowed in the algorithm . The inconsistency parameter specified is the percentage of non-allowable pairs for the indicated positions . Respective ROC curves were generated by incrementally thresholding the mutual information scores for pairs in the accepted structure . True positives were established as base pairs in the accepted structure with an MI value above the threshold . False positives were base pairs not in the accepted structure that had an MI value above the threshold . False negatives were set as base pairs in the accepted structure with an MI value below the threshold . True negatives were base pairs not in the accepted structure that had an MI value below the threshold . The TPR , FPR and PPV were calculated as: ( 1 ) ( 2 ) ( 3 ) The RNAfold TPR and PPV values were generated by sampling pairs at varying MI cutoffs . All pairs with an MI value above the cutoff were sampled and a constraint file was created based on the pairs . The constraint file was used in a secondary structure prediction generated by RNAfold . For any one mutual information score , 100 constraints/predictions were made . The respective accepted structures were also used as constraints . Random pairs in the accepted structure were removed and the remaining pairs were used as a constraint for the RNAfold prediction . The predicted structures were then compared to the accepted structure . Structure diagrams were made using both VARNA and the Circle Compare algorithm found in the RNAstructure software package [79]–[81] . Sequence identity displays are a subset of the full set of sequences used for the analysis and were made using Jalview . Principle component analysis of the suboptimal ensemble was carried out as previously described [39] . All calculations and graphs were done using R version 2 . 1 . 12 and Python 2 . 7 . 2 . We assumed that the number of possible RNA secondary structures can be estimated as increasing with 1 . 8N ( where N is the sequence length ) and that there are approximately 1080 observable atoms in the universe [66] , [82] . Thus , solving for N we estimate that an RNA molecule longer than 314 nucleotides ( e . g . the T . thermophila group I intron ) is able to adopt more conformations than there are atoms in the universe .
|
RNA ( Ribonucleic Acid ) is a messenger of genetic information , master regulator , and catalyst in the cell . To carry out its function , RNA can fold into complex three-dimensional structures . Certain classes of RNAs , called riboswitches , adopt at least two alternative structures to act as a switch . We set out to detect the evolutionary signal for alternative structures in riboswitches as we hypothesize that these RNA sequences must have evolved to allow both conformations . We find that indeed such signals exist when we compare the sequences of riboswitches from multiple species . When we extend this analysis to other RNA regulators in the cell that are not thought of as switches , we detect equivalent evolutionary support for alternative structures . Viewed through the lens of evolutionary structure conservation RNA sequences appear to have adapted to adopt multiple conformations .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"rna",
"rna",
"structure",
"molecular",
"cell",
"biology",
"nucleic",
"acids",
"biology",
"evolutionary",
"biology",
"ribozymes"
] |
2013
|
Evolutionary Evidence for Alternative Structure in RNA Sequence Co-variation
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The polysaccharide capsule of Streptococcus pneumoniae defines over ninety serotypes , which differ in their carriage prevalence and invasiveness for poorly understood reasons . Recently , an inverse correlation between carriage prevalence and oligosaccharide structure of a given capsule has been described . Our previous work suggested a link between serotype and growth in vitro . Here we investigate whether capsule production interferes with growth in vitro and whether this predicts carriage prevalence in vivo . Eighty-one capsule switch mutants were constructed representing nine different serotypes , five of low ( 4 , 7F , 14 , 15 , 18C ) and four of high carriage prevalence ( 6B , 9V , 19F , 23F ) . Growth ( length of lag phase , maximum optical density ) of wildtype strains , nontypeable mutants and capsule switch mutants was studied in nutrient-restricted Lacks medium ( MLM ) and in rich undefined brain heart infusion broth supplemented with 5% foetal calf serum ( BHI+FCS ) . In MLM growth phenotype depended on , and was transferred with , capsule operon type . Colonization efficiency of mouse nasopharynx also depended on , and was transferred with , capsule operon type . Capsule production interfered with growth , which correlated inversely with serotype-specific carriage prevalence . Serotypes with better growth and higher carriage prevalence produced thicker capsules ( by electron microscopy , FITC-dextran exclusion assays and HPLC ) than serotypes with delayed growth and low carriage prevalence . However , expression of cpsA , the first capsule gene , ( by quantitative RT-PCR ) correlated inversely with capsule thickness . Energy spent for capsule production ( incorporation of H3-glucose ) relative to amount of capsule produced was higher for serotypes with low carriage prevalence . Experiments in BHI+FCS showed overall better bacterial growth and more capsule production than growth in MLM and differences between serotypes were no longer apparent . Production of polysaccharide capsule in S . pneumoniae interferes with growth in nutrient-limiting conditions probably by competition for energy against the central metabolism . Serotype-specific nasopharyngeal carriage prevalence in vivo is predicted by the growth phenotype .
Streptococcus pneumoniae is a major pathogen causing several serious human diseases including pneumonia , meningitis and sepsis as well as being a common cause of otitis media . The bacteria are usually surrounded by one of at least 92 known varieties of polysaccharide capsule [1] , [2] which acts as the most important virulence factor by protecting the bacteria from destruction by host phagocytes [3] , [4] . On the basis of the reaction between the polysaccharide capsule and specific antibody , S . pneumoniae is classified by serotype . Whilst some serotypes have a tendency to colonize the nasopharynx more often asymptomatically , others are found to be carried less frequently but are associated with invasive and mucosal disease [5]–[7] . The reasons for this difference between serotypes have not been clearly established . Our previous work suggested a link between colonization prevalence in vivo , capsule type and growth in vitro [8]–[10] . Weinberger et al . [7] have suggested that increased carriage prevalence is associated with heavier encapsulation for protection from neutrophil-mediated killing . This group also found an inverse correlation between carriage prevalence and the predicted metabolic burden of capsule type , based on the number of carbons and the number of high-energy bonds required to generate one polysaccharide repeat unit . Little is known about the nutritional environment which is encountered by S . pneumoniae in the nasopharynx but we propose that when comparing phenotypes of different serotypes in vitro it may be important to consider their behaviour in a relatively nutritionally poor environment . This might more closely reflect the nasopharynx than very rich media commonly used for in vitro culture . For example , albumin concentration in the fluid layer covering the epithelium in the lower respiratory tract is approximately 10-fold lower than serum albumin concentration [11] . Here , we investigated whether the burden of capsule production manifested itself as a delay or reduction in growth of the bacteria under conditions of restricted nutrition as might be experienced in the nasopharynx . The use of 81 different capsule switch mutants allowed us to exclude any effects of the genetic background . Our results suggest that , indeed , capsules of low prevalence serotypes do delay and reduce growth and that this effect is transferrable with the capsule operon . We found a significant correlation between carriage prevalence of serotypes and their growth phenotype . We also found a relationship between amount of capsule produced and serotype: High prevalence serotypes making more capsule than low prevalence serotypes in nutrient-restricted conditions .
Strains representing the low carriage serotypes 4 , 7F , 14 , 15 , and 18C and the high carriage prevalence capsule types 6B , 9V , 19F , and 23F were used . Growth over 20 hours was monitored for wildtype strains , non-encapsulated mutants in which the capsule operon was replaced by the Janus cassette and capsule switch mutants using a nutrient-limited modified Lacks medium ( MLM ) and the rich non-defined BHI+FCS medium . Experiments using capsule switch pairs of two 7F and one 6B strain demonstrated that serotype 7F and 6B exhibit different growth patterns , which could be transferred with the capsule operon ( Figure 1 and Figure S1 ) . In MLM , the 7F wildtype barely grew but loss of the 7F capsule operon allowed enhanced growth and this was only slightly reduced by acquisition of the 6B capsule ( Figure 1A ) . In contrast , the wildtype 6B strain grew well in the MLM but loss of capsule or replacement with the 7F capsule reduced and delayed growth ( Figure 1B ) . However , such growth differences were not apparent in the rich BHI+FCS medium ( Figure 1C ) . In order to extend this observation to more serotypes , but exclude an effect of the chromosomal background , growth patterns were studied for capsule switch mutants of two 7F strains , an 18C strain and a 19F strain in the same 6B background . Growth was measured quantitatively by measuring the length of the lag phase ( until reaching an OD450 nm of 0 . 3 ) and the maximum OD450 nm . The results showed that serotype 6B wildtype strain had a shorter lag phase and grew to a higher OD450 nm than wildtype strains of serotypes 7F , 18C and 19F in MLM ( Figure 2A ) . Following replacement of the 6B capsule with 7F , 18C or 19F capsules , growth of capsule switch mutants in MLM was reduced relative to the 6B wildtype . Growth was enhanced for capsule switch mutants of serotypes 7F and 18C strains relative to their donor strains and growth was similar for the 19F capsule switch mutant and the donor strain ( Figure 2B ) . This effect was not seen in BHI+FCS medium where growth of all wildtype strains was similar ( with the exception of the serotype 18C strain which had a prolonged lag phase in this medium ) ( Figure 2C ) . In BHI+FCS , the capsule switch mutants grew to a lower OD than the wildtype 6B strain with the exception of the recipient of the 18C capsule which grew as well as the 6B wildtype ( Figure 2D ) . In order to determine the effect of additional serotypes , the growth of 10 strains in which the capsule operon had been replaced by the Janus cassette was compared with that of their 10 parent strains ( Table S1 ) and with that of 81 capsule switch mutants in which a new capsule operon ( serotypes 4 , 6B , 7F , 9V , 14 , 15 , 18C , 19F , 23F ) was inserted in place of the Janus cassette ( Table S2 ) . Change in growth due to the capsule type was calculated as the difference in time to reach an OD450 nm of 0 . 3 between the non-encapsulated Janus mutant and the strain of the same genetic background but with a new capsule . The average values of these changes in growth in MLM due to capsules of different serotypes are shown in Figure 3A . The greatest growth delays in MLM were due to capsules of serotypes 15 , 7F , 4 and 18C and , to a lesser extent , serotype 14 . Capsules of serotypes 19F , 23F , 9V and 6B seemed to cause less or no delay in growth . The delay in growth due to capsule was less in BHI+FCS medium and differed less between the serotypes ( Figure 3B ) . The average values of changes in growth due to different capsule types ( see above and Figure 3 ) were plotted against the percentage carriage prevalence of serotype determined by the epidemiological study of Brueggemann et al . [5] . Figure 4A shows that there is an inverse correlation between delay of growth due to capsule type and percentage carriage prevalence ( p = 0 . 0114 ) , with serogroup 9 as somewhat of an outlier . The delay in growth due to capsule was less in BHI+FCS medium and differed less between the serotypes ( data not shown ) . A significant inverse correlation was also found when the growth delay was plotted against the percentage carriage prevalence data of a Swiss population [6] ( p = 0 . 0387 ) and data from an international study [7] ( p = 0 . 0379 ) ( Figure S2 ) . In a similar manner , the difference in maximum OD450 nm due to the capsule type in MLM was plotted against percentage carriage prevalence data obtained from the study of Brueggemann et al . [5] and a significant a positive correlation was found ( p = 0 . 0114 ) ( Figure 4B ) . Using the prevalence values of the Swiss study [6] a significant correlation was also observed ( p = 0 . 0361 ) and using the prevalence values of Weinberger et al . [7] the same trend was observed but narrowly missed being statistically significant ( p = 0 . 0511 ) ( Figure S3 ) . Figure 5 shows the colonization efficiency of different wildtype strains and mutants in the nasopharynx of mice over the course of a week . By day 7 after inoculation , the wildtype serotype 6B strain had established a higher level of colonization than the wildtype 7F strain ( p = 0 . 0017 ) . However , in the mutant in which the 7F capsule had been removed ( 208 . 41 Janus ) , colonization was significantly greater than that of its parental serotype 7F strain ( p = 0 . 0008 ) . In contrast , when the 6B capsule was replaced with a 7F capsule ( mutant 106 . 66cps208 . 41 ) colonization decreased significantly compared with that of the 6B parental strain ( p = 0 . 0063 ) . For the serotype 6B strain , loss of the capsule ( resulting in 106 . 66 Janus mutant ) was a disadvantage for colonization ( p = 0 . 0011 ) . As a measure of the relative metabolic burden of the capsule , the fraction of 3H-labelled glucose taken up and detected in the capsule was determined . Figure 6 shows the results for wildtype strains and capsule switch mutants pooled by serotype and normalized for colony forming units and thickness of capsule . Strains with 7F and 18C capsules incorporated a higher proportion of glucose into their capsules than the high colonization prevalence serotypes 6B and 19F in the MLM ( p = 0 . 0068 ) but in the BHI+FCS medium there was no significant difference between the serotypes ( p = 0 . 9493 ) . Next , we investigated whether the relationship between capsule type and growth pattern may be determined by the amount of capsule produced since this poses a metabolic burden to the bacterial cell . Capsule thickness was determined by electron microscopy ( EM ) , FITC-dextran exclusion assay and quantitative HPLC analysis of cell bound capsular polysaccharides in wildtype strains of serotypes 7F ( two strains ) , 18C , 6B and 19F and their six capsule switch mutants ( 6B background with the two different 7F capsules , 18C or 19F capsule and both 7F backgrounds with the 6B capsule ) . Examples of the EM images are shown in Figure 7 and for FITC-dextran assay in Figure 8 for a 7F serotype and a 6B serotype . By EM , it could be seen that in BHI+FCS medium strains 208 . 41 ( serotype 7F ) ( Figure 7A ) and 106 . 66 ( serotype 6B ) ( Figure 7B ) expressed similar amounts of capsule . In MLM thicker capsule can be distinguished for 106 . 66 ( 6B ) ( Figure 7D ) than 208 . 41 ( 7F ) ( Figure 7C ) . Also by FITC-dextran assay , in BHI+FCS medium strains 208 . 41 ( Figure 8A ) and 106 . 66 ( Figure 8B ) expressed similar amounts of capsule . In MLM strain 106 . 66 ( 6B ) ( Figure 8D ) bacteria are bigger than strain 208 . 41 ( Figure 8C ) . For quantitative analysis , data was pooled for wildtypes strains and capsule switch mutants of the same serotype as shown in Figures 7E , 8E and 9 . Comparisons were made between the low carriage prevalence serotypes 7F and 18C and the high prevalence serotypes 6B and 19F . Comparing capsule thickness in the two media , for the 7F/18C group it was clear that the amount of capsule was much reduced in the nutrient-restricted MLM compared with BHI+FCS ( p<0 . 0001 ) when determined either by EM ( Figure 7E ) or FITC-dextran exclusion assay ( Figure 8E ) and the same trend was seen by HPLC ( p = 0 . 0763 ) ( Figure 9 ) . For the 6B/19F group by FITC-dextran assay there was also more capsule in BHI+FCS than MLM ( p<0 . 0001 ) ( Figure 8E ) but by EM ( Figure 7E ) and HPLC ( Figure 9 ) the difference was not significant ( p = 0 . 0664 and p = 0 . 2877 respectively ) indicating that the nutrient-restricted medium has a less dramatic effect on reduction of capsule thickness in the high prevalence serotypes 6B and 19F . Comparing capsule thickness between the serotypes within the same medium , in the nutrient-restricted MLM there was significantly more capsule in the 6B/19F group whether measured by EM ( Figure 7E ) or FITC-dextran assay ( Figure 8E ) ( p<0 . 0001 ) or by HPLC ( Figure 9 ) ( p = 0 . 0144 ) . However , in BHI+FCS medium capsule thickness was greater in the 7F/18C group when measured by EM ( Figure 7E ) ( p<0 . 0001 ) but greater in the 6B/19F group when measured by FITC-dextran assay ( Figure 8E ) ( p<0 . 0001 ) and almost equivalent when measured by HPLC ( Figure 9 ) ( p = 0 . 9766 ) which may mean that in this rich medium capsule thickness is similar for the different serotypes . To address whether the differences observed in amount of capsule were regulated by transcription of the capsule operon , expression of the first gene , cpsA , was measured by real-time RT-PCR and normalized against 16S rRNA gene expression for the same strains as described in the capsule thickness experiments above . Figure 10 shows that after pooling data for wildtype and capsule switch mutants of the same serotype , for all serotypes tested cpsA expression was greater in the BHI+FCS medium than in the MLM ( for 6B/19F p<0 . 0001 , for 7F/18C p = 0 . 0375 ) . In MLM cpsA expression was lower for the 6B/19F serotypes than the 7F/18C ( p = 0 . 0016 ) . There was no significant difference between the 7F and 18C versus the 6B and 19F in the BHI+FCS ( p = 0 . 8669 ) . The reduction in cpsA expression when growing in the MLM as compared to BHI+FCS was particularly marked for the high carriage prevalence 6B and 19F serotypes ( p<0 . 0001 ) and to a lesser extent for the low carriage prevalence 7F and 18C serotypes ( p = 0 . 0375 ) . Therefore , expression levels of the cpsA gene agreed with capsule thickness in terms of being higher in the rich BHI+FCS medium as compared to the MLM . However , in MLM where differences between high and low carriage strains were most apparent , the high carriage prevalence serotypes 6B and 19F downregulated cpsA expression more than the low prevalence serotypes 7F and 18C although capsule thickness was higher in the high carriage prevalence strains than in the low carriage prevalence strains .
Although it has long been observed that some capsule serotypes of S . pneumoniae are most commonly isolated from the nasopharynx while others are predominantly found in normally sterile sites in patients with invasive disease [5]–[7] , the reason for this difference has remained unclear . The role of the capsule is thought to be to protect the bacteria from phagocytosis following invasion [3] , [4] and in the nasopharynx to repel mucus and so aid colonization [12] . It seems that the capsule type either enables a pneumococcus to reside for a long time in the nasopharynx ( high colonization prevalence serotype ) or causes it to be cleared quickly from the nasopharynx necessitating invasion for its survival ( low colonization prevalence serotype ) . We speculate that this is because high colonization prevalence serotypes can thrive in the nasopharynx where as low colonization serotypes can only survive after invading and reaching a nutritionally richer environment . Weinberger et al . [7] have suggested that polysaccharide capsule serotypes differ in terms of the metabolic demand placed on the bacteria for their synthesis . They described an inverse correlation between the number of carbons and high energy bonds and colonization prevalence . Here we aimed to answer the question of whether serotypes predicted to have a low metabolic demand for their synthesis have high colonization prevalence because they have a growth advantage . Therefore , we compared growth phenotype of different serotypes and found that capsules predicted to be a low metabolic burden do have a growth advantage in terms of a short lag phase and a higher maximum optical density . These experiments were performed in a modified version of Lacks medium which we cannot be sure is an accurate representation of the nutrient conditions in the nasopharynx but rather we assume is closer to the nasopharyngeal conditions than rich media such as brain heart infusion broth or Todd Hewitt broth enriched with yeast extract often used to culture S . pneumoniae . Our version of the medium is limited in amino acids and peptides compared with the original Lacks medium in an attempt to mimic the nasopharyngeal environment which albumin concentration measurements predict to be more protein restricted than serum [11] . The growth advantage of having a capsule predicted to be of low metabolic burden correlated with ability to colonize the nasopharynx in a mouse model although a limitation of the in vivo study was that it was restricted to serotype 7F and 6B wildtype strains and their mutants . In addition , colonization was only monitored during the first week after colonization while colonization in the human nasopharynx occurs over a much longer period . A further limitation is that we have not determined whether differences in adherence to the epithelial cells play a role in the serotype-specific difference in colonization efficiency along with difference in growth . Also , adding experimental evidence to the prediction made by Weinberger we saw that in the nutrient-restricted medium more energy was required to synthesize the capsules of the low colonization prevalence serotypes than the high colonization prevalence serotypes . In addition , we found that in the nutrient-restricted medium , serotypes with high colonization prevalence were able to make thicker capsules than the low colonization prevalence serotypes , as determined by electron microscopy , FITC-dextran exclusion assay and HPLC . This seems compatible with the idea that these capsules are less metabolically demanding for the bacteria and so more can be made . A thicker capsule could give these bacteria an advantage in resisting phagocytosis and clearance by mucus in the nasopharynx . Interestingly , the observation that high colonization serotypes make more capsule was not reflected in expression of the first gene of the capsule operon , cpsA , which in fact was slightly greater in the low colonization serotypes . This is in agreement with our previous study [10] which found an inverse correlation in BHI medium between cpsA expression and colonization prevalence . This raises the possibility that there is post-transcriptional control of capsule expression . A limitation of the current study is that we have not yet determined the mechanism of post-transcriptional control but the role of tyrosine phosphorylation of the product of cpsD , on capsular polysaccharide synthesis has already been described [13] , [14] . CpsC and cpsD may regulate amount of capsule by affecting chain length of the polysaccharide polymers [13] , [14] . The relationship between CpsD phosphorylation and amount of capsule produced appears to be affected by the genetic background of the bacteria suggesting that a factor outside the capsule operon is also important in the control of capsule synthesis [14] . In this study , although we describe a relationship between capsule type and capsule thickness we do not exclude that there is also control of capsule expression by factors outside of the capsule operon . However , cpsA expression was affected by the nutrient availability in the medium: In the nutrient rich ( BHI+FCS ) medium there was more cpsA expression and more capsule production than in the nutrient-restricted ( MEM ) medium suggesting that regulation of capsule expression at the level of transcription also occurs . In conclusion , in a nutrient-restricted environment , which might reflect the environment in the nasopharynx , serotypes that have a capsule which is not metabolically demanding to synthesize may be able to grow well and sooner and make a thicker capsule than serotypes with a metabolically demanding capsule . This regulation may be , to a large extent , by a post-transcriptional mechanism and give them an advantage in nasopharyngeal colonization . This may explain why some serotypes such as 6B , 19F , are 23F more often found colonizing the nasopharynx than serotypes such as 7F , 15 and 18C .
All mouse experiments were conducted following guidelines from University of Liverpool ethical review and animal welfare committee . The mouse experiments were carried out under the authority of the UK Home Office Animals Scientific Procedures Act 1986 . UK Home Office project Licence number PPL 80/2111 , personal licence number PIL 70/13633 , study approved by University of Liverpool Animal Welfare and Ethics Committee . Outbred , female MF1 mice ( Harlan , UK ) at 8–12 weeks of age were used to model pneumococcal nasopharyngeal carriage [15] , [16] . Mice were mildly anaesthetized with 2 . 5% ( v/v ) Isofluorane USP ( Isocare ) over oxygen ( 1 . 4–1 . 6 litres/min ) and 10 µl sterile PBS containing 1×105 S . pneumoniae was equally distributed between both nostrils . At predetermined times , groups of 5 animals were sacrificed by cervical dislocation and nasopharyngeal tissue , was dissected [17] , placed into 5 ml sterile PBS , weighed , disrupted with an Ultra-Turrax T8 homogeniser ( IKA ) and numbers of pneumococci determined by colony counting [18] . Clinical isolates of Streptococcus pneumoniae were selected from two nationwide surveillance programs collecting nasopharyngeal and invasive isolates [6] , [19] . Strains and mutants used for this study are listed in Tables S1 and S2 . For 10 wildtype clinical isolates the capsule operon was removed and replaced with a Janus cassette , as described below , rendering these strains non-typeable . These strains are listed in Table S1 and represent 7 different serotypes and 8 different genetic backgrounds . Capsule operons from other strains were introduced into these non-typeable mutants to give capsule switch mutants as described below . These mutants are listed in Table S2 . In total , 81 different capsule switch mutants were used in this study representing 10 different serotypes . For the construction of capsule switch mutants the bicistronic cassette Janus was used [20] , [21] . Primers used for mutant construction are listed in Table S3 . The Janus cassette in the form of a PCR construct of dexB-Janus-aliA was kindly provided by K . Trczinski , ( Harvard School of Public Health , Boston , USA ) . The allele rpsL str1 , which was kindly provided by D . Morrison ( University of Illinois , Chicago , USA ) was amplified with forward primer DAM350 and reverse Primer DAM351 by using Fast Taq DNA polymerase ( Roche Molecular Biochemicals , Rotkreuz , Switzerland ) according to the manufacturer's instructions . Amplification was performed by using the following cycling conditions: primary denaturation for 5 min at 94°C , followed by 30 cycles consisting of 94°C for 30 s , 50°C ( annealing temperature ) for 30 s and 72°C for 2 min ( extension time ) and then the last cycle for 10 min at 72°C . The Janus cassette was amplified with forward primer dexBstart2 and reverse Primer aliAend2 by using the Expand Long Template PCR system ( LRPCR ) ( Roche Molecular Biochemicals , Rotkreuz , Switzerland ) . Amplification was performed by using the following cycling conditions: primary denaturation for 2 min at 92°C , followed by 10 cycles consisting of 92°C for 10 s , 65°C for 30 s , and 68°C for 17 min and then 20 cycles in which each extension cycle was prolonged by 20 s . PCR products were purified with a QIAquick PCR purification kit ( QIAGEN , Basel , Switzerland ) . The dexB-Janus-aliA PCR product was used to transform pneumococcal clinical isolates with selection for Kmr to make the unencapsulated strains , which were used as recipients . Chromosomal DNA of the “capsule donor” clinical isolates was used to transform the recipients with selection for Smr to create capsule switch mutants . The structure of the cps locus and surrounding regions of the capsule switch mutants were confirmed by RLFP analysis of the following PCR products: 1430-1402 ( dexB-cps-aliA locus ) digested with RsaI , TTM07-09 ( pbp2x-dexB flanking cps upstream region ) and TTM08-10 ( aliA-pbp1a flanking cps downstream region ) both digested with Tsp509I [21] . Restriction patterns were compared with recipient and donor strain . Transformation was performed as described previously [22] using 1 µg of DNA consisting of the rpsL str1 DNA fragment , dexB-Janus-aliA PCR product , or 2 µg of the chromosomal DNA of the donor strain . Aliquots of the cultures were then spread onto CSBA plates containing 300 µg/ml streptomycin or 500 µg/ml kanamycin . The plates were incubated for 24 h prior to subculture of single colonies on CSBA plates . After serotyping , successful transformants were stored for further evaluation at −80°C using Protect bacterial preservers ( Technical Service consultants , Heywood , UK ) . Two media were used to study the growth behaviour of the bacteria . To represent a rich nutritional environment , brain heart infusion broth ( BHI ) ( Becton Dickinson and Company , le Pont de Claix , France ) supplemented with 5% foetal calf serum ( FCS ) ( Biochrom KG , Berlin , Germany ) was used . This is referred to in the text as BHI+FCS medium . To represent a more nutritionally limited environment a modified version of Lacks medium was used [23] . The principal modification from the original medium is that the amino acid and peptide components are at one quarter of the concentration of the original medium . The components of modified Lacks medium ( MLM ) are listed in Table S4 . Strains were streaked onto Columbia sheep blood agar ( CSBA ) plates and incubated at 37°C in a 5% CO2-enriched atmosphere overnight then subcultured in growth medium to OD600 0 . 5 , centrifuged at 3000 g for 5 min and resuspended in growth medium . Growth was monitored in sterile flat-bottomed 96-well microtitre plates ( Nunclon Surface , Nunc , Denmark ) based on the method of Brewster [24] . In brief , 200 µl bacteria culture was grown per well and OD450 nm measured every 30 minutes using a VERSAmax microplate reader ( Molecular Devices ) over 20 hours . The plate was shaken automatically for 5 seconds before each reading . The problem of condensation affecting the readings was avoided by pre-treating the lids of the 96-well plates with 3 ml 0 . 05% Triton X-100 in 20% ethanol and allowing them to air-dry before use . Serial dilutions of 100 µl liquid culture in phosphate buffered saline ( PBS ) were plated out onto CSBA plates at least in duplicates and incubated overnight at 37°C with 5% CO2 atmosphere . The next day CFUs were counted with an in house automated colony counter ( Brugger S . et al . , unpublished data ) and the bacterial load was calculated . Strains were grown overnight on Columbia blood sheep agar ( CSBA ) plates at 37°C with 5% CO2 atmosphere . Several colonies were inoculated into 10 ml of growth medium ( BHI+FCS or MLM ) . The next day , 100 µl of the overnight culture in BHI+FCS was inoculated into 10 ml of fresh BHI+FCS medium and 100 µl of the overnight culture in MLM was inoculated into 10 ml of fresh MLM , both supplemented with 1 µCi D-glucose-3H ( U ) ( ARC , St . Louis , MO ) . The subcultures were grown to an optical density ( OD ) at 600 nm wavelength between 0 . 4–0 . 5 representing mid-log phase of growth . Bacteria were harvested by centrifugation at 4000× G for 10 minutes at 4°C and washed with 5 ml phosphate buffered saline ( PBS ) . After ultracentrifugation with 20000× G for 30 minutes at 4°C the bacterial pellet was dissolved in 5 ml double distilled water , buffer saturated phenol was added to a final concentration of 1% and incubated overnight at room temperature . The released capsular polysaccharides were separated from the cells and detritus by centrifugation with 20000× G for 30 minutes at 4°C . The supernatant containing the capsular polysaccharides was decanted and the cells resuspended in 5 ml of double distilled water . Radioactivity was determined in the collected cell and capsule fraction ( see above ) . Therefore , 500 µl of each fraction was mixed with 1500 µl Microscint 40 scintillation liquid and counts per minute were measured averaging 3 minutes per sample with a Tri-Carb liquid scintillation counter ( Perkin Elmer , Waltham , MA ) . A capsule/cell beta counts ratio was calculated for each tested strain to compare the energy distribution for capsule assembly and cellular metabolism . High performance liquid chromatography: Isolation of polysaccharide capsule was done using a procedure adapted from previously described methods [25] , [26] . Bacteria were harvested by centrifugation and washed twice with phosphate buffer saline ( PBS ) and double distilled water . After ultracentrifugation at 20000 g for 30 minutes at 4°C the pellet was dissolved in a 1% buffer-saturated phenol solution . This mixture was incubated overnight at room temperature to release cell bound capsular polysaccharides . The capsular polysaccharides were separated from the cells by ultracentrifugation at 20000 g for 30 minutes at 4°C . Polysaccharides were precipitated by adding sodium acetate and ethanol to a final concentration of 7 . 2% and 60% , respectively and incubation for at least 30 minutes at 4°C . The polysaccharides were collected by centrifugation at 20000 g for 30 minutes and remaining nucleic acids and proteins were digested by the sequential addition of 250 U of Benzonase nuclease ( Merck , Darmstadt , Germany ) ( 4 hours incubation ) and 40 µl of a 20 mg/ml proteinase K solution ( Roche , Mannheim , Germany ) ( incubated overnight ) . Resulting low-molecular weight contaminants were removed by centrifugation in an Amicon Ultra 30 kDa cut off membrane centrifugal filter unit ( Millipore , Billerica , MA ) at 4000 g for 10 minutes at 4°C . The retained polysaccharides were dried with heat in speed vac vacuum centrifuge . After resolution , extracted capsular polysaccharides were hydrolysed as described previously ( ZITAT Saddic/ANumula ) . After complete hydrolysis , the dried hydrolysed polysaccharides ( i . e . monosaccharides ) were labelled with anthranilic acid ( Fluka , Buchs , Switzerland ) to enable fluorescence detection in HPLC separation . Labelling was performed as described previously [27]–[29] . Chromatographic separation was adapted from a previously described method [29] using an AS2000A autosampler ( Merck Hitachi , Darmstadt , Germany ) with an injection volume of 20 µl per sample . Separation of the monosaccharide was done with a flow rate of 0 . 85 ml/min as follows: 6% solvent B isocratic for 35 minutes followed by a linear gradient from 6 to 12% solvent B over 20 minutes . Subsequently , a ten minute wash with 100% B and 100% A for 15 minutes followed before re-equilibrating the system with 6% B for minutes . Total run time was 85 minutes with data collection for 60 minutes . A Luna 5 µm , C18 , 150×4 . 6 mm column ( Phenomenex , Torrance , CA ) was used for separation and column temperature was maintained at 35°C using a L5025 column oven ( Merck Hitachi ) . Anthranilic acid labelled monosaccharides were detected by fluorescence ( F-1080 Fluorescence Detector , Merck Hitachi ) at an excitation of 360 nm and an emission of 425 nm . Peaks were identified by comparison of retention times with monosaccharide standards and re-running after spiking to verify increase in the peak of interest . Standard curves were generated for monosaccharides of interest in at least three independent experiments . Amounts of monosaccharides per bacterial cells were calculated using peak heights and/or areas taking into account dilution factors , sample and injection volumes used and relating to counted CFUs ( see above ) . Bacteria were cultured overnight in BHI+FCS medium or MLM until OD600 nm 0 . 3 then 200 µl subcultured into 10 ml of fresh medium and cultured again until OD600 nm 0 . 3 . After addition of 20 ml RNAprotect ( Qiagen ) , RNA was extracted and expression of the first gene of the capsule operon , cpsA , was determined by real-time RT-PCR as described previously [10] . Student's t-test was used to calculate p values using the software GraphPad Prism ( Version 5 , GraphPad Software , Inc . ) . This software was also used to calculate linear regression . A value of p≤0 . 05 , two-tailed , was considered significant .
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Streptococcus pneumoniae bacteria are responsible for serious human infections including meningitis , pneumonia and bacteraemia and are a common cause of otitis media ( ear infection ) in children . However , they most often reside harmlessly in the infant nasopharynx . An association has long been observed between the type of polysaccharide capsule surrounding the bacteria and harmless colonization versus invasive disease . Here we suggest that capsule types that are costly for the bacteria to make are produced in lower quantities and their production limits the growth of the bacteria in nutrient-restricted conditions . In contrast , bacteria with capsules that require less energy can produce more capsule and grow more successfully . This may be an explanation for why S . pneumoniae with certain capsule types can be effective long-term colonizers of the nasopharynx while others need a richer nutritional environment to flourish and so are most often associated with invasive disease . This information may be of use when considering which capsules types to target in future vaccines .
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[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"medicine",
"bacterial",
"diseases",
"infectious",
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"biology",
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2012
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Capsule Type of Streptococcus pneumoniae Determines Growth Phenotype
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The Global Program to Eliminate Lymphatic Filariasis ( LF ) has a target date of 2020 . This program is progressing well in many countries . However , progress has been slow in some countries , and others have not yet started their mass drug administration ( MDA ) programs . Acceleration is needed . We studied how increasing MDA frequency from once to twice per year would affect program duration and costs by using computer simulation modeling and cost projections . We used the LYMFASIM simulation model to estimate how many annual or semiannual MDA rounds would be required to eliminate LF for Indian and West African scenarios with varied pre-control endemicity and coverage levels . Results were used to estimate total program costs assuming a target population of 100 , 000 eligibles , a 3% discount rate , and not counting the costs of donated drugs . A sensitivity analysis was done to investigate the robustness of these results with varied assumptions for key parameters . Model predictions suggested that semiannual MDA will require the same number of MDA rounds to achieve LF elimination as annual MDA in most scenarios . Thus semiannual MDA programs should achieve this goal in half of the time required for annual programs . Due to efficiency gains , total program costs for semiannual MDA programs are projected to be lower than those for annual MDA programs in most scenarios . A sensitivity analysis showed that this conclusion is robust . Semiannual MDA is likely to shorten the time and lower the cost required for LF elimination in countries where it can be implemented . This strategy may improve prospects for global elimination of LF by the target year 2020 .
The Global Program to Eliminate Lymphatic Filariasis ( GPELF ) was launched in 2000 with the aim of eliminating lymphatic filariasis ( LF ) as a public health problem by 2020 [1] . The recommended strategy is to treat entire at-risk populations annually with a single dose of ivermectin and albendazole ( IVM+ALB ) in sub-Sahara Africa or with diethylcarbamazine and albendazole ( DEC+ALB ) in other regions for a minimum of 5 years [2] . Mapping studies suggest that mass drug administration ( MDA ) is needed in 72 endemic countries [3] . As indicated in the GPELF 2010 progress report , progress toward LF elimination varies widely between countries [3] . Some countries started their MDA programs early and may have already interrupted LF transmission , while other countries lag behind [3] . Nineteen countries had not yet started MDA , and geographical coverage was incomplete in 24 others . Reasons cited for slow progress in some areas included major logistic challenges , political instability , conflict , and co-endemicity with Loa loa [4] . Also , results from ongoing MDA programs have sometimes been disappointing . Sentinel site data collected after 5 years of annual MDA show that microfilaria ( mf ) prevalence had dropped to 0% in about two-thirds of sentinel sites sampled . However , mf rates had decreased by less than 50% in 10% of the sites sampled [4] . With the goal of LF elimination by 2020 in mind , it is now important and timely to study whether elimination programs can be accelerated . A straightforward option would be to increase the frequency of MDA from once per year ( annual ) to twice per year ( semiannual ) . While increasing MDA frequency might be expected to shorten the time required for elimination , the magnitude of this effect is uncertain . Only one study directly compared the impact of annual and semiannual MDA and this was for brugian filariasis: semiannual MDA with DEC alone caused a more rapid decline in mf prevalence than annual treatment . However , the duration of this study was too short to support conclusions regarding elimination [5] . Results from other studies of semiannual MDA are difficult to interpret , because they did not provide results from a control area with annual MDA [6] , [7] , [8] . For decision-making , it is also important to consider how costs per year and overall costs for LF elimination programs are likely to change if MDA frequency is increased . Of course , costs per year will increase , but they will not necessarily double , and the cumulative cost for the entire program may even decline . The costs of twice yearly MDA have not been formally studied for LF or other neglected tropical diseases . However , they can be projected from detailed cost data by activity and cost item that are available for yearly MDA for LF and soil-transmitted helminthiasis [9] , [10] , [11] , [12] . We have used the well-established simulation model LYMFASIM to estimate the number of treatment rounds and duration of MDA programs that would be needed to eliminate LF with annual and semiannual MDA in different settings . Simulations were performed for typical endemic areas in West Africa ( with IVM+ALB treatment and Anopheles transmission ) and in India ( with DEC+ALB treatment and Culex transmission ) with different pre-control endemicity levels and MDA coverage rates . In addition , we have compared projected costs of annual or semiannual MDA , both per year and for the total required duration of LF elimination programs .
We estimated the costs of MDA for LF programs with annual and semiannual treatment from the perspective of the endemic country government . The cost analysis covers financial and economic costs . The financial costs are the costs of all inputs purchased in cash for MDA , including purchased MDA drugs , materials and supplies , ministry of health personnel salaries , and per diem payments for community drug distributors [11] . Economic costs also include the costs of donated drugs for MDA ( India: albendazole; Burkina Faso: ivermectin , albendazole ) . Costs were calculated for a target population of 100 , 000 eligible persons in three steps . We studied the extent to which key assumptions affect conclusions regarding the relative cost of the two MDA schedules ( once or twice yearly MDA ) in a univariate sensitivity analysis . On the cost side , we assessed the effect of changing the discount rate to 0% or 6% instead of 3% , the effect of including the cost of donated drugs , and we considered the scenario where drugs are only bought for people who are actually treated instead of for all eligibles ( with the idea that any remaining drugs would be stored and used in a later round ) . These factors do not influence the number of rounds required , but they may affect the total costs of annual and semiannual treatment programs and influence policy decisions . With respect to the simulations , we examined the impact of changing assumptions regarding the efficacy of drugs on adult worms . This may affect the total number of treatment rounds ( and total costs ) required for LF elimination programs with annual or semiannual MDA . The fraction of worms assumed to be killed or permanently sterilized after each treatment was varied with low , medium ( baseline ) and high values ( 50% , 65% , and 80% for DEC+ALB , and 20% , 35% and 50% for IVM+ALB ) . Further , we studied the impact of including variability in this parameter , so that the fraction of worms killed or sterilized varies randomly between individuals in each treatment cycle and within individuals in different treatment cycles . The variation is described by a beta distribution with the mean equal to the baseline fraction of worms killed/sterilized and standard deviation equal to 0 . 3 .
Figure 1 shows an example of model-predicted trends in mf prevalence . The presented trends are for a West African area with a pre-control mf prevalence of 20% . Six rounds of annual MDA with IVM+ALB were provided starting at time 0 . Coverage was 70% and drug efficacy was quantified according to our baseline assumptions . The figure displays the trend of 25 runs that were all conducted with the same input assumptions . Variation in the outcomes is due to stochasticity . In this example , 1 out of 25 runs showed recrudescence after stopping MDA; one other run seemed to be moving to elimination , but this was not yet achieved . The probability of elimination in this case was 23/25 ( 92% ) . Figure 2 illustrates how the model-predicted probability of LF elimination increases with the number of MDA rounds provided . Results are shown for the India and West Africa model variants , for annual and semiannual MDA , and for different coverage levels . Table 3 shows the expected costs per treatment round by activity and cost item for annual and semiannual MDA in both regions . Providing semiannual instead of annual MDA reduces the cost per MDA round . This cost reduction is 11% for India and 18% for West Africa , if costs of donated drugs are excluded from the analysis . The cost reduction is smaller if donated drug costs are included ( 7% for India and only 1% for West Africa ) . Table 4 shows the number of treatment rounds for achieving a 99% probability of elimination , under our baseline assumptions . This number is highly dependent on treatment coverage and pre-treatment mf prevalence rates , but it does not depend much on the frequency of treatment ( annual or semiannual ) . In most circumstances , therefore , the total duration of semiannual MDA is about half of that for annual MDA . In very unfavorable circumstances ( areas with high baseline infection rates and very low MDA coverage ) , one extra MDA round may be required to reach elimination with semiannual MDA . The total costs of MDA programs depend on the cost per round , the required number of MDA rounds , and thereby also on local circumstances and coverage rates . Table 4 shows estimated total costs for LF elimination programs , assuming an annual discount rate of 3% for future costs . In this analysis , which does not count the cost of any donated drugs , projected costs of semiannual MDA are almost always lower than costs of annual MDA . In West Africa , this is even true when semiannual MDA requires one more MDA round , because of the large reduction in cost per round . In the single India scenario where semiannual MDA required one more round than annual MDA , the projected total program costs were comparable for annual and semiannual MDA . Table 5 and Table 6 summarize the results of the sensitivity analyses for India and West Africa . The tables show the ratio of total program costs for semiannual MDA over annual MDA under varied assumptions . This ratio shows which approach is less expensive ( with values <1 indicating that semiannual MDA is cheaper and vice versa ) , and it provides an indication of the relative cost difference . Changing the discount rate ( 0% or 6% ) had little impact on the projected total costs of semiannual and annual MDA programs and their ratios . Its effect increased with the duration of MDA , and a higher discount rate tends to favor the slower annual MDA programs . But the total program cost of semiannual MDA was lower or comparable to the cost of annual MDA in all scenarios . Including the costs of donated drugs changed the outcome of the cost analysis significantly . The costs per treatment round increased by a large amount ( by an amount that was the same for annual and semiannual treatment ) , and the relative difference was reduced . While semiannual MDA remained cheaper in most Indian scenarios , it became slightly more expensive in the West African scenarios . The highest increase ( 17% ) was seen in the West African scenario with the highest endemicity ( pre-control mf prevalence of 27% ) , because here semiannual MDA would require one more round than annual MDA . Whether drugs are purchased for all eligibles in every round or for the percentage of people treated only ( assuming that previously unused drugs were not wasted/expired ) , hardly affected the ratio of total program cost of semiannual over annual MDA . Model assumptions about the percentages of adult worms killed ( or permanently sterilized ) by a single treatment affected the total number of treatment rounds needed to achieve elimination and therefore the estimated total program costs . However , this did not have a major impact on ratios of total program cost for semiannual vs . annual MDA programs ( Table 6 ) . Adding random variation in the percentage of adult worms killed ( or permanently sterilized ) sometimes led to an extra treatment round in either annual or semiannual MDA , but nevertheless semiannual MDA was still favored in this analysis .
Cost calculations were based on observed data from 1996 and 2002 [9] , [11] , which were then adjusted to reflect current day practices and prices . The absolute cost estimates are subject to various assumptions . For the current purpose , though , the main interest is in the relative cost of semiannual vs . annual MDA , which is much less dependent on the assumptions . Key assumptions in the cost projections did not affect the conclusion that the cost of LF elimination with semiannual MDA is lower than or similar to the cost of programs with annual MDA . A high discount rate ( reflecting a strong preference to delay cost to the future ) favors annual MDA programs , in which the expenses are spread over a longer period and postponed further into the future . However , the efficiency gains of semiannual MDA mostly compensate for this . If the high costs of donated drugs are included in the cost estimates , the relative difference in cost per round diminishes and becomes negligible in West Africa . In West Africa , therefore , the efficiency gain no longer compensates for the effect of discounting or the need for an extra treatment round in semiannual MDA . But this situation only occurs when many MDA rounds are required because of unfavorable transmission conditions ( as in our high endemic West African scenario ) . Slightly increased program costs may be justified in such situations , because here the positive impact of increasing MDA frequency on total program duration is very strong . We did not test the impact of future inflation with different annual inflation rates , but this would work in favor of shorter duration semiannual MDA programs , and it would tend to strengthen our conclusions . Estimates of the required duration of MDA in different settings were obtained by computer simulation , because empirical evidence from LF elimination programs is still limited . Many countries have made great strides , and some have stopped MDA , but no country that had ongoing transmission of LF in 2000 has been verified to have interrupted transmission of the infection using MDA [4] . Modeling is a powerful tool for decision making in infectious disease control [30] , but predictions are subject to uncertainty [31] . An important uncertainty in our study concerns the efficacy of drugs . The sensitivity analysis showed that more treatment rounds would be required if the employed drugs are less effective than assumed and vice versa , while adding random variability in the percentage of worms killed by treatment did not influence the predicted outcomes . In any case , these assumptions equally affected predictions for semiannual and annual MDA programs and did not significantly affect the relative cost difference between the two strategies . Field studies are needed to confirm projected cost reductions that can be achieved with semiannual MDA in both regions and to assess any indirect effects that might affect the relative efficiency of annual vs . semiannual MDA . For example , the likelihood that unused medication is stored and used in subsequent rounds may be higher in semiannual than in annual MDA programs . Also , it is possible that increased treatment frequency will increase coverage rates ( e . g . due to higher population awareness ) and reduce systematic non-compliance ( e . g . due to the fact that MDA does not always take place in the same season ) . Such changes could reduce the number of MDA rounds needed for elimination and further increase the efficiency of semiannual vs . annual MDA programs . But the opposite could also occur if insufficient effort is made to maintain high coverage rates . The efficiency gain in cost per treatment round achieved by shifting from annual to semiannual MDA was somewhat different for India and West Africa . This reflects differences in program organization and costing structure in the two regions [9] , [11] . For example , the West African cost estimates included central administrative costs , laboratory costs , and adverse reaction monitoring , while these costs were not counted in the estimates for India . In general , the efficiency gain achieved is dependent on strategic choices ( e . g . on activities to repeat and available budgets ) , health systems , program organization , and the local cost of different inputs . Results could be somewhat different in other settings . In the supporting information text S1 , we show how the relative difference in total program costs depends on the relative difference in cost per treatment round , the required number of treatment rounds and applied discount rate . The duration of MDA varies between regions because of differences in exposure patterns to mosquitoes , characteristics of the vector , timing of MDA , immigration of people , etc . Simulation results are therefore not directly generalizable to other areas , but this is not pertinent to the comparison of annual and semiannual MDA durations . This becomes clear when one compares results projected in this study for LF elimination programs in India and West Africa; although there are important differences between these models that result in very different estimates for the number of MDA rounds needed for elimination ( generally higher in Africa ) , the basic conclusion that doubling MDA frequency halves the required duration of LF elimination programs and reduces total program costs is valid for both of these regions and it should also apply to other regions . Besides the total program costs , there are other important factors to consider in deciding whether MDA frequency should be increased . Increasing treatment frequency leads to a faster decline in the incidence of LF infection . This should increase the likelihood of achieving LF elimination by the target year of 2020 , which is very relevant for countries that have not yet started their MDA programs . Incidence of clinical manifestations will also decline faster , which results in larger population health gain in terms of the total number of DALYs averted and results in increased productivity . Quantification of these extra benefits was beyond the purpose of this study . Increasing the treatment frequency and reducing program duration may also be beneficial for other reasons . E . g . , shorter programs may be more politically attractive to health officials , and they would also be expected to have reduced risks of interruption due to natural disasters , political instability , or wars . Shorter programs may also reduce the risk of emergence of resistance to anthelmintics during LF elimination programs . Since albendazole and ivermectin also affect other diseases than LF , increasing the treatment frequency would increase their impact on diseases like soil-transmitted helminths and other NTD's – albeit for a shorter period . Potential barriers for increasing the frequency of MDA are the increased cost per year and practical difficulties that may be associated with semiannual MDA . Increased annual drug requirements may exceed supplies of donated drugs . Also , more frequent MDA might overwhelm countries' capacities for delivering MDA to endemic populations , in view of already heavily burdened health systems and many competing health priorities [32] . Semiannual MDA may not be feasible in all areas due to weather , seasonal migration of populations , or logistical considerations . Other factors may play a role when LF elimination is integrated with programs for control of other neglected tropical diseases ( NTDs ) . That is to say , how would accelerated LF elimination affect control programs for other NTDs ? Poor-performing programs , with very low treatment coverage , require relatively many treatment rounds . Increasing the treatment frequency from annually to semiannually would reduce the total program duration by about half , but not the number of treatment rounds . However , investments or strategies that increase coverage rates will improve results of annual or semiannual MDA , thereby reducing the number of treatment rounds required and the total costs ( see Table 4 ) . In summary , computer simulations suggest that the frequency of MDA – annual vs semiannual – does not strongly influence the total number of treatment rounds required to achieve LF elimination . The costs per year are higher with semiannual MDA , but total program costs ( excluding donated drugs ) are projected to be lower or about the same when semiannual MDA is used . The few situations where the total program costs of semiannual MDA are slightly higher are also challenging situations for LF elimination where semiannual MDA may improve the odds of success . Therefore , we expect semiannual MDA to be superior to annual MDA in most endemic settings . Considering the GPELF goal of LF elimination by 2020 , semiannual MDA should be considered as a means of accelerating LF elimination in areas where it can be implemented .
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The Global Program to Eliminate Lymphatic Filariasis ( LF ) employs annual mass drug administration ( MDA ) of antifilarial drugs to reduce infection rates in populations and interrupt transmission . While this program is working well in many countries , progress has been slow in others , and some countries have not yet started MDA programs . We used computer simulation modeling and cost projections to study how increasing MDA frequency from once to twice per year would affect program duration and costs . Our results suggest that semiannual MDA is likely to reduce the time required to eliminate LF by 50% and reduce total program costs ( excluding the cost of donated drugs ) in most situations . For these and other reasons , we expect semiannual MDA to be superior to annual MDA in most endemic settings . Semiannual MDA should be considered as a means of accelerating LF elimination in areas where it can be implemented , because this may improve prospects for global elimination of LF by the target year 2020 .
|
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2013
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Modeling the Impact and Costs of Semiannual Mass Drug Administration for Accelerated Elimination of Lymphatic Filariasis
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CD1d-restricted invariant natural killer T ( iNKT ) cells represent a heterogeneous population of lipid-reactive T cells that are involved in many immune responses , mediated through T-cell receptor ( TCR ) –dependent and/or independent activation . Although numerous microbial lipid antigens ( Ags ) have been identified , several lines of evidence have suggested the existence of relevant Ags of endogenous origin . However , the identification of their precise nature as well as the molecular mechanisms involved in their generation are still highly controversial and ill defined . Here , we identified two mammalian gangliosides—namely monosialoganglioside GM3 and disialoganglioside GD3—as endogenous activators for mouse iNKT cells . These glycosphingolipids are found in Toll-like receptor-stimulated dendritic cells ( DC ) as several species varying in their N-acyl fatty chain composition . Interestingly , their ability to activate iNKT cells is highly dependent on the ceramide backbone structure . Thus , both synthetic GM3 and GD3 comprising a d18:1-C24:1 ceramide backbone were able to activate iNKT cells in a CD1d-dependent manner . GM3 and GD3 are not directly recognized by the iNKT TCR and required the Ag presenting cell intracellular machinery to reveal their antigenicity . We propose a new concept in which iNKT cells can rapidly respond to pre-existing self-molecules after stress-induced structural changes in CD1d-expressing cells . Moreover , these gangliosides conferred partial protection in the context of bacterial infection . Thus , this report identified new biologically relevant lipid self-Ags for iNKT cells .
Type I or invariant natural killer T ( iNKT ) cells are a subset of “innate-like” αβ T lymphocytes that rapidly produce large amounts of cytokines and chemokines and orchestrate the ensuing innate and adaptive immune responses during infection , inflammatory disorders , and cancer [1] . iNKT cells recognize self and exogenous lipid antigens ( Ags ) presented by the quasimonomorphic CD1d molecule expressed by Ag-presenting cells [2] . In contrast to the highly diverse T-cell receptor ( TCR ) repertoire of peptide-specific/major histocompatibility complex ( MHC ) -restricted conventional αβ T cells , iNKT cells express a semiconserved TCR composed of a unique and invariant TCRα chain , using TCR TRAV11 to TRAJ18 rearrangement in mice ( TRAV10 to TRAJ18 in humans ) , paired with a limited array of Vβ chains [1] . Accordingly , the antigenic diversity and specificity of iNKT cells was intuitively believed to be limited . Over the last decade , numerous studies have attempted to identify cognate Ags involved in iNKT cell thymic selection and/or in their activation during infection and stressful conditions in the periphery ( for reviews , [2 , 3] ) . To date , several candidates have been proposed to act as self-Ags for iNKT cells , including β-linked glycosphingolipids ( GSLs ) ( isoglobotrihexosylceramide [iGb3] [4 , 5] , gangliosides [6 , 7] and β-glucosylceramide [β-GlcCer] [8] ) , and phospholipids [9] . However , the physiological activity of these compounds has either been called into question or demonstrated to have weak and/or subset-specific activity . For example , iGb3 activates some mouse and human iNKT [4] and has been proposed to be responsible for iNKT cell development [5] . However , mice deficient for the enzyme involved in direct iGb3 anabolism ( A3galt2 ) present no iNKT cell defect [10] . In addition , iGb3 is virtually undetectable in mouse peripheral tissues [11] , and the human gene encoding iGb3 synthase appears to be nonfunctional [12] . β-GlcCer was also proposed as a self-Ag for both human and mouse iNKT cells [8] . However , it was subsequently reported that this activity was restricted to mammalian α-linked monoglycosylceramides contaminants [13 , 14] . Initially thought to be absent in mammals , small quantities of α-linked monoglycosylceramides ( e . g . , α-galactosylceramide [α-GalCer] ) that may act as iNKT cell self-Ags have been identified [14] . Although the origin of mammalian α-GalCer is currently uncertain , further evidence suggests that some bacterial communities from the gut microbiota , including Bacteroides fragilis , can produce this particular hexosylceramide [15 , 16] . In addition , diet could also provide a source of these Ags , as recently demonstrated by the presence of α-linked monohexosylceramides in bovine milk [17] . Thus , the repertoire of self-lipids for iNKT cells lacks consensus and is incompletely characterized . The possibility that the precise structure of the lipid chain of iNKT self-Ags could be important for their reactivity to the TCR has only been partially investigated . Nevertheless , some GSLs with particular ceramide structures have been shown to carry variable antigenic capacity [8 , 14] . Ceramide metabolism is tightly regulated by multiple enzyme families , including ceramide synthases ( de novo synthesis ) [18] and ceramidases ( degradation ) [19] . The impact of inflammation on the regulation of these enzymes is currently unknown , as well as their potential involvement in iNKT cell biology . We and others have demonstrated that Toll-like receptor ( TLR ) engagement on/in Ag-presenting cells resulted in lipid metabolism perturbations uncovering self-Ag ( s ) for iNKT cells [20 , 21] . Specifically , we observed that TLR7/9-mediated activation of dendritic cells ( DCs ) resulted in the accumulation of charged β-linked GSLs able to activate mouse iNKT cells in a CD1d-dependent manner [21] , but the precise nature of these activating compounds remained unknown . Here , we highlighted two particular ganglioside species found in TLR-stimulated DCs that act as iNKT cell activators . Importantly , reactivity of the iNKT cells to gangliosides is highly dependent on the specific structure of the ceramide backbone , particularly the N-acyl fatty acid chain length and its geometry . Both synthetic d18:1-C24:1 GM3 and d18:1-C24:1 GD3 gangliosides activate iNKT cells in vitro and in vivo in a dose- and CD1d-dependent manner . These synthetic gangliosides are unlikely to engage iNKT TCR in their native forms and require unidentified intracellular molecular changes to become antigenic . Thus , we propose a new concept in which iNKT cells can rapidly respond to pre-existing self-molecules after stress-induced structural changes in CD1d-expressing cells . Collectively , our data bring to light a class of self-lipid Ags that can activate iNKT cells and that may be relevant during inflammatory and/or pathological settings .
We previously reported that the activating iNKT cell Ag ( s ) that are present in DCs following TLR9 triggering was/were charged β-GlcCer derivative ( s ) [21] . Amongst the four biosynthetic pathways in vertebrates that stem from β-GlcCer , the ganglioside family contains most of the charged GSLs [22] ( Fig 1A ) . In order to determine whether this class of GSLs was responsible for iNKT cell activation in our system , we used DCs generated from mice deficient in GA2/GM2/GD2/GT2 synthase ( B4galnt1 ) or GM3 synthase ( St3gal5 ) , two key enzymes involved in ganglioside anabolism ( Fig 1A ) . Cytosine-phosphate-guanine oligodinucleotide ( CpG ODN ) -stimulated DCs were used as a source of lipids for in vitro testing . Remarkably , while the lipid fraction from B4galnt1−/− DCs , which lack all complex gangliosides , retained its capacity to activate iNKT cells , the lipid extract from St3gal5−/− DCs failed to do so ( Fig 1B ) . Normal-phase high-pressure liquid chromatography ( NP-HPLC ) analysis revealed that the activating fraction ( CpG ODN-stimulated B4galnt1−/− DCs ) contains only two detectable species corresponding to the gangliosides GM3 ( 95% ) and GD3 ( 5% ) , which are absent from the fraction isolated from CpG ODN-stimulated St3gal5−/− DCs ( Fig 1C ) . This suggested that the charged activating lipid ( s ) in CpG ODN-stimulated DCs is/are simple ganglioside ( s ) . Since gangliosides are charged through incorporation of N-acetyl-neuraminic acid , we next tested the impact of sialidase treatment on the activity of the lipid ( s ) . Interestingly , this treatment led to a significant reduction of its biological activity ( Fig 1D ) . Altogether , these results indicate that the activating GSL ( s ) in TLR9-stimulated DCs include ( s ) simple sialylated ganglioside ( s ) . In line with earlier studies [23] , we previously suggested that TLR9 triggering in DCs resulted in metabolic changes in the general GSL pathway [21] . For instance , TLR9 engagement in DCs induced a modulation of gene expression in the GSL biosynthetic pathway , especially in key enzymes involved in the anabolism of simple gangliosides [21] . Accordingly , NP-HPLC analysis indicated that CpG ODN treatment led to a subtle increase of GM3 and GD3 in DCs ( Fig 2A ) . Since the precise structure of the ceramide moiety is important for the antigenic properties of GSLs [8 , 14 , 24] , we evaluated the composition of the lipid backbone of GM3 and GD3 contained in resting versus TLR9-stimulated wild-type ( WT ) DCs . As ceramide anabolism and catabolism is dependent , among others , on ceramide synthases ( CerS ) and ceramidases , respectively [19 , 25] , we assessed the expression of these enzymes in DCs at the transcriptional level . The transcripts of two ceramidases ( Asah1 and Naaa ) were detected in resting DCs . Of note , CpG ODN stimulation did not affect Asah1 transcript expression , whereas it led to a significant reduction for Naaa expression ( Fig 2B ) . In addition , we detected the mRNA expression of three ceramide synthases ( Cers2/5/6 ) in resting DCs ( Fig 2B ) specialized in the synthesis of C16 ( Cers5 and Cers6 ) and C22/C24 ( Cers2 ) ceramides [25] . Importantly , TLR9 triggering in DCs led to Cers2 and Cers6 up-regulation but Cers5 down-regulation ( Fig 2B ) . In line with this gene expression profile , identification of the N-acyl chain heterogeneity of GM3 and GD3 by mass spectrometry in resting DCs revealed the presence of various species differing in length and degree of unsaturation ( Fig 2C ) . We detected the presence of at least six ( assuming d18:1 sphingosine: C16:0 , C18:0 , C20:0 , C22:0 , C24:0 , and C24:1 ) and three ( C18:0 , C24:0 , and C24:1 ) species for GM3 and GD3 , respectively , with variable abundance ( Fig 2C ) . In line with our transcriptional analysis , TLR9 triggering led to a modulation in the proportion of specific ceramide forms . Specifically , CpG ODN stimulation preferentially favored C24:1 and C16:0 acyl chains in GM3 , while reducing C22:0 and C24:0 species ( Fig 2C ) . Meanwhile , CpG ODN led to an increased proportion of C24:1 acyl chain in GD3 , paralleled with a reduction in C16:0 and C24:0 species ( Fig 2C ) . Importantly , commercial gangliosides from bovine buttermilk , which have been widely used to test GSL antigenicity to iNKT cells , differ greatly in their N-acyl chain length composition compared to DC-derived GSLs ( Fig 2D and S1 Table ) . For instance , C16:0 and C24:1 that are the main ganglioside species in mouse DCs are barely found in bovine buttermilk . On the other hand , C22:0 , C23:0 , and C24:0 gangliosides were abundant in bovine buttermilk , while they were minimally represented or undetectable in mouse DCs . Taken together , these results show that TLR9 stimulation induces not only increased production of GM3 and GD3 in DCs but also leads to an exquisite tuning of their ceramide backbone composition , both of which may have important biological consequences . Based on these observations , we synthesized GM3 and GD3 with either a d18:1-C16:0 or a d18:1-C24:1 ceramide backbone ( Fig 3A ) . The structures and synthetic schemes of the four gangliosides are illustrated in Supporting information ( S1 Fig and S11–S14 Figs ) . With respect to the stereo-controlled construction of GM3 and GD3 , we first produced a stereo-controlled β-GlcCer ( Fig 3B and S15 Fig ) , which was then used as a building block for gangliosides assembly . β-GlcCer from commercial sources usually comes with α-contaminants ( 13 , 14 ) . On the contrary , NP-HPLC and hydrophilic interaction liquid chromatography-tandem mass spectrometry ( HILIC-MS2 ) analyses indicate that there are no such contaminants in our stereo-controlled β-GlcCer compound ( Fig 3C and S2 Fig ) , as compared to a synthetic mixture containing α anomers ( S2 Fig ) . To demonstrate further the absence of α-hexosylceramide contaminants in our products , we probed , in a cell-free system , the structure of CD1d/synthetic GSL complexes using the L363 monoclonal antibody ( mAb ) that specifically recognizes CD1d/α-glycosylceramide interactions [14] . While it allowed detection of CD1d/α-GalCer and CD1d/commercial β-GlcCer complexes , L363 failed to bind to CD1d/ganglioside and CD1d/stereo-controlled β-GlcCer complexes ( Fig 3D ) . Thus , we demonstrated the synthesis of β-linked ganglioside species for further iNKT antigenicity assessment . Since previous crystallographic studies suggested that the length of the fatty acid chain influences the binding of lipids into the A′ and C′ pockets of CD1d molecules [26] , we evaluated the capacity of synthetic GM3 and GD3 to bind to mouse CD1d molecules using microscale thermophoresis [27] . Surprisingly , d18:1-C16:0 GM3 and d18:1-C16:0 GD3 showed limited or no interaction with CD1d ( Fig 4A and 4B ) . In contrast , C24:1 GM3 and C24:1 GD3 bound to CD1d with affinities of 102 . 5 ± 2 . 3 μM and 69 . 1 ± 1 . 5 μM , respectively ( Fig 4C and 4D ) , which are approximately 70–100-fold weaker compared to CD1d/α-GalCer ( C26:0 ) interactions . Of note , the neutral C24:1 β-GlcCer weakly interacted with CD1d ( KD = 2 . 54 mM ) ( S3 Fig ) compared to C24:1 gangliosides , suggesting that additional hexose residues and/or charged residues significantly influenced the binding capacity of C24:1 GSLs into the CD1d groove . Taken together , our results demonstrate that C24:1 GM3 and C24:1 GD3 gangliosides are able to stably bind to mouse CD1d molecules . To assess ganglioside antigenicity , we cocultured DCs and iNKT cell hybridomas in the presence of synthetic GM3 and GD3 . Both C24:1 GM3 and GD3 activated iNKT hybridomas in a CD1d- and dose-dependent manner , as measured by interleukin 2 ( IL-2 ) production ( Fig 5A and S4 Fig ) . In line with our affinity measurements , gangliosides bearing a C16:0 ceramide backbone showed little to no biological activity ( Fig 5A ) . Consistent with a previous report [14] , C24:1 β-GlcCer failed to activate iNKT cell hybridomas . Of note , iNKT cell hybridomas failed to respond to commercial gangliosides extracted from bovine buttermilk ( S5 Fig ) . In addition , C24:1 gangliosides induced the proliferation of splenic iNKT cells ( Fig 5B ) but not conventional T cells in vitro ( S6 Fig ) . Since C24:1 ceramide biosynthesis is largely dependent on the activity of CerS2[28] , we tested the ability of CpG ODN to uncover iNKT cell endogenous ligands in Cers2-deficient DCs . Interestingly , while CpG ODN-stimulated Cers2-competent DCs activated the iNKT cell hybridoma DN32 . D3 , Cers2-deficient DCs failed to do so ( Fig 5C ) . As previously reported [29] , it is noteworthy to mention that DCs from CerS2-null mice expressed reduced amounts of surface CD1d ( approximately 50% ) ( S7A Fig ) . However , this only minimally affected their ability to present exogenous α-GalCer and C24:1 gangliosides to iNKT cell hybridomas ( Fig 5C and S7B Fig ) . Collectively , these results indicate that C24:1 GM3 and C24:1 GD3 gangliosides are activating molecules for mouse iNKT cells in a CD1d-dependent manner . As some GSLs require endosomal lipid transfer factors to be efficiently loaded onto CD1d [30 , 31] , the importance of the intracellular pathway in antigenic capacity of C24:1 gangliosides was investigated . Glutaraldehyde fixation of DCs prior to culture severely impaired the capacity of gangliosides to activate DN32 . D3 , whereas α-GalCer partially retained its stimulating properties ( Fig 6A ) . The response to α-Gal ( α1–2 ) GalCer , a glycolipid that requires intracellular processing to reveal its antigenicity [24] , was completely abolished by DC fixation ( Fig 6A ) . In addition , the preincubation time of DCs with gangliosides strongly influenced their activating properties ( S8 Fig ) . In the same vein , pretreatment of DCs with bafilomicyn A or concanamycin , two lysosomal acidification inhibitors , prior to ganglioside addition also abrogated iNKT cell activation in response to gangliosides ( Fig 6B ) . To test whether C24:1 gangliosides could be recognized in their native form by the iNKT TCR , DN32 . D3 were cultured in presence of immobilized CD1d:ganglioside complexes . In this setting , we failed to detect any iNKT cell activation , as judged by cytokine release ( Fig 6C ) . Moreover , mouse CD1d tetramers loaded with either GM3 or GD3 did not bind to iNKT cell hybridomas ( Fig 6D ) , suggesting that the native compounds are not directly recognized by the iNKT TCR . To assess whether intracellular processes such as hydrolysis and/or putative anomerization could convert β-linked gangliosides to α-linked GSLs , resulting in iNKT cell activation , we used L363 mAb in our assay . L363 mAb dose-dependently abolished DN32 . D3 reactivity toward α-GalCer but only partly inhibited activity of gangliosides ( approximately 50%–60% with the highest dose ) ( Fig 6E ) . Albeit at lower levels , it is noteworthy that iGb3 , a self-GSL that activates iNKT cells when presented by CD1d in a β-conformation [32] , was also significantly inhibited ( approximately 20% ) in the presence of high concentrations of L363 mAb ( Fig 6E ) . Moreover , L363 binds to approximately 7% of resting DCs , suggesting the presence of small amounts of α-hexosylceramide:CD1d complexes at cell surface ( Fig 6F ) . While α-GalCer loading increased L363 binding to DCs , gangliosides did not ( Fig 6F ) . In conclusion , we suggest that gangliosides do not activate iNKT cells in their native forms and require the intracellular machinery to be presented into their antigenic conformation to the iNKT TCR . Activation of lung iNKT cells by intranasal inhalation of α-GalCer was shown to promote host defense mechanisms against respiratory bacterial ( including pneumococcal ) infections [33] . To evaluate ganglioside biological activity in vivo , we administered C24:1 GM3 and C24:1 GD3 intranasally . Interestingly , both lipids dose-dependently activated lung iNKT cells to produce interferon gamma ( IFNγ ) and IL-17A ( Fig 7A ) . Unlike α-GalCer , ganglioside administration did not result in a strong T-helper ( Th1 ) -biased response by iNKT cells , as assessed by IFNγ/IL-17A ratio ( Fig 7A ) . Similar to α-GalCer , iNKT cell activation was accompanied by the transactivation of gamma delta ( γδ ) T cells and NK cells as well as the recruitment of neutrophils ( Fig 7B–7D ) . In line with in vitro experiments , C16:0 ganglioside species’ inhalation failed to activate lung iNKT cells ( S9 Fig ) . Importantly , CD1d blockade abrogated lung iNKT cell activation in response to C24:1 gangliosides ( S10 Fig ) . Analysis of cytokine release in lung homogenates from WT and Cd1d1−/− mice indicated that C24:1 ganglioside-induced immune response was dependent on the CD1d molecule ( Fig 7E ) . Finally , we investigated the effects of ganglioside inoculation on the host protective response against Streptococcus pneumoniae . As depicted in Fig 7F , all vehicle-treated mice died within 5–6 days following pneumococcal infection . In agreement with our previous findings [33] , mice pretreated with α-GalCer did not present any observable clinical signs of disease and survived the infection . Interestingly , inoculation of synthetic GM3 or GD3 partially protected mice ( around 50% ) against lethal S . pneumoniae-induced pneumonia ( Fig 7F ) . In sum , exogenous administration of C24:1 GM3 and C24:1 GD3 activates iNKT cells in vivo and protects against lethal respiratory bacterial infection .
Despite strong evidence suggesting the existence of self-lipid Ags for TCR-dependent iNKT cell selection and activation during inflammation , their precise nature remains unclear . Here , we have identified two particular species of the ganglioside family , which can be detected in TLR9-stimulated DCs , as molecules endowed with “indirect” antigenic capacities for iNKT cells . Synthetic d18:1-C24:1 β-linked GM3 and GD3 gangliosides can activate mouse iNKT cells in vitro and in vivo in a CD1d-dependent fashion . However , our data indicate that these molecules are not recognized in their native forms but require further intracellular changes to engage iNKT TCRs . The contribution of the lipid moiety for iNKT cell antigenicity has mainly been explored with synthetic α-linked glycolipids . Nevertheless , some mammalian GSLs with particular ceramide structures present variable antigenic properties . For instance , mammalian α-GalCer , GlcCer , and sulfatide with a ceramide d18:1-C24:1 backbone have greater antigenic properties compared to other subspecies [8 , 14] . Similarly , despite sharing a similar polar head group , we observed that d18:1-C24:1 gangliosides activate iNKT cells , while their d18:1-C16:0 counterparts failed to do so , an effect which may be due to their low capacity to bind to mouse CD1d molecule . Our data also highlighted the importance of sialic acid residues for binding to CD1d . While both C24:1 GM3 and GD3 interact with CD1d with a KD of μM range , the neutral C24:1 GlcCer presents a 10-fold lower affinity . Thus , the negative charge conferred by the sialic acids is likely to facilitate the loading into CD1d . At this stage , it remains unclear whether the sialic acid residue ( s ) participate ( s ) directly in the antigenicity of the compounds; however , we mainly observed in vitro and in vivo that GD3 had a higher activity on iNKT cells compared to GM3 . While the use of B4galnt1- and St3gal5-deficient mice allowed us to suggest the activating capacity of GM3 and GD3 , the existence of multiple charged ganglioside subspecies that may act as iNKT activators such as GM1 , GM2 , or GD1a cannot be definitely excluded . It has been shown that some cancer cells contain gangliosides ( GD3 and N-glycolyl-GM3 ) that can modulate iNKT cell activity [6 , 7 , 34] . In this context , the tumor Ag N-glycolyl-GM3 has been shown to interact with CD1d to induce moderate human iNKT cell proliferation[6] . GD3 antigenicity toward iNKT cells was only observed in preimmunized mice and was subset specific [7] . However , other studies have demonstrated that GD3 had inhibitory effects on TCR-dependent iNKT cell activation although confirming its binding to CD1d [35 , 36] . In these studies , authors have used bulk gangliosides ( either purified or commercial ) , which may contain contaminants as well as various ceramide species , resulting in a potential competition between inhibitory and activating molecules . Moreover , we observed that C24:1 gangliosides are almost absent in commercial bovine buttermilk–derived GM3 . Amongst the high diversity of molecules bound to the CD1d molecules under steady state , it is interesting to note that GM3 could be eluted from both human and mouse CD1d , whereas GD3 was virtually undetectable [37 , 38] . However , the authors were only able to determine the structure for up to 25% of the total pool [37] , which may explain the absence of GD3 . In line with previous studies [23 , 38] , we observed that TLR triggering on DCs influences GSL abundance and diversity , including gangliosides . However , TLR-signaling ability to generate CD1d-restricted endogenous Ags is unlikely to be solely attributed to the neosynthesis of activating ligands . In fact , the time needed to generate new glycosphingolipids does not fit with the rapid response of iNKT cells . Here , we propose a concept in which TLR9 signaling affects ceramide metabolism to favor a pool of preexisting iNKT cell Ag precursors . Deciphering the respective contribution of C24:1 ceramide synthesis ( de novo or salvage ) versus catabolism of other species that may represent competitors for CD1d binding ( C20:0-C24:0 ) in TLR9-dependent mechanism will be of the utmost importance . In addition , whether or not the triggering of other TLR members points to a similar mechanism will also need further investigation . Our data also indicate a critical contribution for the intracellular machinery to efficiently present ganglioside to iNKT cell TCRs . Surprisingly , despite the lack of detectable α-GSL contaminants in our synthetic lipids , we observed that when used at high concentrations ( μg/ml ) , the L363 mAb partially inhibited the antigenicity of both GM3 and GD3 , suggesting a requirement for DC-derived α-glycosylceramides in these settings . Therefore , we can envision several nonmutually exclusive scenarios , including i ) the involvement of putative specific endogenous anomerases , ii ) the synergistic activity of endogenous α-glycosylceramides [14] with the C24:1 β-gangliosides , iii ) the catabolism of gangliosides to simpler structure ( s ) with direct antigenic properties [39] , or iv ) that the iNKT TCRs reshape the primary β-linked sugar of the gangliosides into a new conformation that mimics α-linked glycolipids , as previously described for iGb3 [32] . The presence of traces of α-contaminants ( below the threshold of our analytic procedures ) in our synthetic lipids could also explain some of our findings , i . e . , the inhibitory effect of L363 on ganglioside activity in our biological assay . However , while L363 is strongly active on α-GalCer at concentrations as little as 10 ng/ml , its effect on gangliosides is only observable with 100-fold higher concentrations . In addition , iGb3 activity is also partially inhibited with highest doses of L363 mAb . Interestingly , this observation can only be made in the coculture setting since L363 was neither able to bind immobilized CD1d:ganglioside complexes or ganglioside-pulsed DCs . Moreover , loading of CD1d tetramers with gangliosides did not allow detection of iNKT cells . Based on this , we believe that this finding is unlikely due to contamination but rather to a certain degree of nonspecificity for the L363 mAb against β-GSLs or to a yet-to-be-defined biological phenomenon , as stated above . Can GM3 and/or GD3 represent self-Ags during iNKT cell development ? Porubsky and colleagues demonstrated that St3gal5−/− and St8sia1−/− mice present a normal iNKT cell compartment [40] . Thus , simple gangliosides do not appear to be critical in iNKT cell positive selection or maturation/differentiation . However , in line with its involvement in iNKT cell antigenicity , we recently demonstrated the critical involvement of CerS2 , an enzyme involved in the generation of very long–chain sphingolipids , including d18:1-C24:1 GSLs in iNKT cell development and survival [29] . Globally , ceramide synthases and ceramidases have been shown to display variable substrate specificity . In this context , a tight regulation of ceramide metabolism could be critical in either favoring or hindering self-iNKT cell Ag formation . Thus , a better characterization of the role of enzymes of the ceramide metabolism including other families such as β-Glucocerebrosidase , sphingomyelinases , and hexosaminidases in iNKT cell development and/or activation is warranted . Since the level of expression of endogenous Ags must be low and tightly controlled under homeostatic conditions to prevent iNKT cell autoreactivity and development of autoinflammatory processes , the uncovering of gangliosides as iNKT cell activators makes sense . Indeed , while native gangliosides do not present direct antigenic properties on iNKT cells , this preexisting pool could be rapidly converted into bioactive Ags , resulting in rapid response of iNKT cells upon stressful conditions . Furthermore , gangliosides do not have a strictly host origin . Lipid-enriched diet might represent an exogenous source of gangliosides . Given the role of iNKT cells in diet-induced obesity and insulin resistance [41 , 42] , the contribution of dietary gangliosides in these diseases should be considered . In line with this , GM3 has been proposed to participate in insulin resistance in genetic models of diabetes [43] . Gut microbiota has been proposed to negatively regulate iNKT cell number and functions [44] , questioning the putative involvement of gangliosides in this mechanism . Since gangliosides have never been reported to be synthesized/produced by commensals , they are unlikely to directly contribute to this phenomenon . It is also worth noting that gangliosides are overexpressed in human milk with GM3/GD3 as major species in colostrum and GM3 in mature milk [45] . Since human iNKT cells developing during fetal life can mature in the small intestine [46] , encounters with milk-derived gangliosides shortly after birth may provide positive signals required in this process . A recent study elegantly demonstrated the presence of active α-linked monohexosylceramides Ags in bovine milk that could also play a part in these mechanisms [17] . Hence , the putative presence of active α-linked gangliosides would also be worth investigating . Finally , since we and others [45] observed that bovine and human milk have strong differences in ganglioside species , a comparative analysis of the iNKT cell compartment in breast-fed versus bottle-fed infants would be worthy of study . In conclusion , our study reinforces the current concept that “innate-like” T lymphocytes are able to sense fine modulation in cellular metabolism to become activated . We demonstrate how an innate signal can be detected by iNKT cells through subtle alterations of the ceramide and/or ganglioside metabolism and the generation of specific antigenic self-lipids .
All experiments were conducted on C57BL/6J genetic background mice , and the performance was in compliance with current national and institutional regulations and ethical guidelines ( B59-350009 ) and approved by the Comité d’Ethique en Experimentation Animale Nord-Pas de Calais ( C2EA-75 ) under the protocol number 2015121722376405 . All efforts were taken to minimize mouse usage to maximize necessary results; provide the best veterinary care; and minimize discomfort , distress , and surgery with anesthetic procedures and euthanasia . Euthanasia was performed using a lethal injection of pentobarbital . In survival experiments , mice were euthanized when reaching one of these endpoints: dehydration , loss of ability to ambulate , labored respiration , or weight loss ( >20% ) . 8- to 12-week-old male WT C57BL/6J mice were purchased from Janvier ( Le Genest-St-Isle , France ) . The generation of Cd1d1−/− C57BL/6J mice has been previously described [47] . Mice deficient of B4galnt1 ( EC 2 . 4 . 1 . 92 ) and St3gal5 ( EC 2 . 4 . 99 . 9 ) were provided by R . Proia ( National Institutes of Health , Bethesda , MD , United States of America ) . The generation of CerS2 null mice has been described in [28] . Mice were bred in our own facility in specific pathogen-free conditions . For S . pneumoniae infection , mice were maintained in a biosafety level 2 facility . Type B CpG ODN ( ODN 1826 ) was from Cayla ( Toulouse , France ) . α-GalCer was produced in house . Glyko Sialidase A was from PROzyme ( Hayward , CA , USA ) . mCD1d protein was purchased from Interchim ( Montluçon , France ) . Recombinant Soluble Mouse CD1d:Ig Fusion Protein ( CD1d dimer XI ) was from BD Biosciences ( Le Pont de Claix , France ) . Cell Trace Violet Cell Proliferation Kit was from ThermoFischer scientific . Anti-CD1d ( 19G11 ) and its isotype control ( LTF-2 , Rat IgG2b ) were from Bio X Cell ( West Lebanon , NH , USA ) . Bovine buttermilk GM3 and GD3 were from Biovalley ( Nanterre , France ) . The commercial d18:1-C24:1 β-GlcCer was from Interchim . The commercial α/β-GlcCer-mix ( 15/85 ) was from Avanti Polar Lipids . α-GalGalCer and iGb3 were kindly provided by Dr . Steven Porcelli ( Albert Einstein College of Medicine , New York City , NY , USA ) . L363 ( IgG2a ) mAb ( purified or PE-conjugated ) and isotype control were from eBiosciences ( San Diego , CA , USA ) . PBS-57 glycolipid-loaded and unloaded control CD1d tetramers ( APC- or PE-conjugated ) were from the National Institute of Allergy and Infectious Diseases Tetramer Facility ( Emory University , Atlanta , GA , USA ) . Monoclonal antibodies against mouse CD45 ( APC-Cy7-conjugated ) , CD3 ( Pacific Blue- or PerCP-Cy5 . 5–conjugated ) , TCRδ ( PerCP-Cy5 . 5-conjugated ) , NK1 . 1 ( PE-Cy7- or FITC-conjugated ) , Ly6G ( FITC-conjugated ) , CD11b ( PerCP-Cy5 . 5–conjugated ) , IFNγ ( AF647-conjugated ) , IL-17A ( PE-conjugated ) , and appropriated isotype controls were purchased from BioLegend ( San Diego , CA , USA ) and BD Pharmingen . Mouse ELISA kits are from R&D systems ( Minneapolis , MN , USA ) and eBioscience . Briefly , bone marrow ( BM ) precursors from various gene-targeted mice were cultured in complete IMDM medium supplemented with 10% FCS and 1% of supernatant from a granulocyte-macrophage colony-stimulating factor ( GM-CSF ) –expressing cell line ( J558-GM-CSF ) for 14 days . BMDCs ( >90% purity ) were stimulated or not with CpG ODN ( 2 μg/ml ) for 16 h . BMDCs were then collected and dry pellets were frozen ( −20°C ) until further treatment/analysis . Lipids were extracted from resting or CpG ODN-stimulated DCs , as previously described [21] . To remove all terminal sialic acid residues , lipids were dried under nitrogen and resuspended in 50 mM sodium acetate , 1 mg/ml sodium taurodeoxycholate , pH 5 . 5 . N-acetylneuraminate glycohydrolase ( sialidase , EC 3 . 2 . 1 . 18 , Arthrobacter ureafaciens recombinant expressed in E . coli ) , 50 mU , was added for 48 h in a total volume of 20 μl . All lipid fractions were desalted over C18 cartridges ( pre-equilibrated with 2 x 1 ml methanol and 2 x 1 ml water ) . The volume of digest was made up to 100 μl with water and then the sample was applied to the cartridge; the sample tube was washed in 2 x 100 μl chloroform: methanol: water 1:2 . 2:1 ( v/v/v ) and applied to the cartridge . The cartridge was then washed with 3 x 1 ml water and the lipids were eluted with 1 x 1 ml chloroform: methanol 98:2 ( v/v ) , 2 x 1 ml chloroform: methanol 1:3 ( v/v ) , and 1 x 1 ml methanol and mixed thoroughly . Purified lipid fractions were dried under nitrogen for in vitro testing after validating the digestion by subjecting 0 . 5% of the lipid fraction to ceramide glycanase digestion , anthranilic acid labeling , and NP-HPLC analysis , as previously described [48] . Total RNA from resting or CpG ODN-treated DCs were isolated with the Nucleospin RNA Plus extraction kit ( Macherey-Nagel , Hoerdt , France ) , and cDNA were synthesized from 1 mg of total RNA with random hexamer primers and Superscript III ( Invitrogen , Cergy Pontoise , France ) according to standard procedures . cDNAs were used as templates for PCR amplification with the SYBR Green PCR Master Mix ( Molecular Probes , Leiden , the Netherlands ) and the ABI PRISM 7700 Sequence Detector ( Applied Biosystems , Foster City , CA ) . Primers , which are listed in S2 Table , were designed by the Primer Express Program ( Applied Biosystems ) and used for amplification in triplicate assays . PCR amplification of Gapdh was performed to control for sample loading and to allow normalization between samples . ΔCt values were obtained by deducting the raw cycle threshold ( Ct values ) obtained for Gapdh mRNA , the internal standard , from the Ct values obtained for investigated genes . For graphical representation , data are expressed as relative expression of mRNA levels . Aliquots corresponding to 20 , 000 BMDCs/μL were mixed with internal lipid standards for analysis by LC MS/MS using an Aquity I-class UPLC and a Xevo TQ-S “triple-quadrupole” instrument , both from Waters . Using a CORTECS HILIC column ( 2 . 1 mm x 100 mm; 1 . 7 μm , Waters ) , gangliosides were measured in negative mode with a gradient between 80% solvent A ( 90% acetonitrile ) and 100% solvent B ( 50% acetonitrile ) , both containing 10 mM ammonium formate as additive . Gangliosides were analyzed with the MS/MS-transitions [GM3 − H]− > [NeuAc − H]− and [GD3 − 2H]2− > [NeuAc − H]− by multireaction monitoring ( MRM ) at optimized collision energies of 50 eV and 30 eV , respectively . Transitions reflect by majority GM3/GD3 species with d18:1 long chain base ( C18-sphingosine ) and C16 to C24 fatty acyl chain length , as C18-sphingosine is the dominant sphingoid base . GM3 ( d18:1; C19:0 ) was used as internal standard . Qualitative measurements in positive mode were performed with the MS/MS-transitions [GM3 + H]+ > [Sph ( d18:1 + H − 2H2O]+ and [GM3 + H]+ > [Sph ( d20:1 + H − 2H2O]+ by multireaction monitoring ( MRM ) . The general synthesis of GM3 and GD3 are based on the strategies reported by Akira Hasegawa [49] and Tomoya Ogawa [50] . Synthesis of GD3 ( S11 Fig ) began with regioselective glycosylation of lactosyl diol ( 2 ) with dimeric thioglycoside ( 1 ) using N-iodo-succinimide ( NIS ) and triflic acid ( TfOH ) as promoter at −25°C , affording sole tetrasaccharide ( 3 ) containing α-sialyl- ( 2→8 ) -sialic acid unit α-glycosidically linked to O-3 of D-galactose residue in the oligosaccharide chains . The tetrasaccharide ( 3 ) , after palladium on carbon catalyzed hydrogenation and O-acetylation , was converted into tetrasaccharide ( 4 ) . Further desilylation and anomeric hemiacetal activation gave trichloroacetimidate ( 5 ) , which was coupled with either d18:1-C16:0 ceramide ( 6 ) or d18:1-C24:1 Ceramide ( 7 ) , giving glycosyl ceramides ( 8a ) or ( 8b ) , respectively . Theses glycosides were then transformed via global deacetylation and hydrolysis of methyl esters into GD3 . The general synthesis of GM3 followed a similar strategy ( S12 Fig ) . Briefly , thioglycoside ( 9 ) was regioselectively coupled with lactosyl diol ( 2 ) to generate trisacchride ( 10 ) . Sequential hydrogenation and O-acetylation of ( 10 ) gave peracylated compound ( 11 ) , which was further subject to desilylation to afford hemiacetal in proximal sugar . By treatment with trichloroacetonitrile , the obtained hemiacetal was converted into Schmidt donor ( 12 ) , which was then coupled with either d18:1-C16:0 Cer ( 6a ) or d18:1-C24:1 Cer ( 7a ) to afford ( 13a ) or ( 13b ) , respectively . These protected triglycosylceramides were each transformed into the targeted gangliosides GM3 via O-deacetylation and saponification of the methyl ester . A stereo-controlled synthetic route was adopted for the production of GD3 and GM3 ( S13 and S14 Figs ) in pure β form according to the report by Shunichi Hashimoto [51] . This strategy features coupling of trisaccharide ( 22 ) or disaccharide ( 27 ) with a stereocontrolled building block ( 17 ) , which excludes possibility of α isomer contamination . In this regard , a disarmed donor ( 14 ) was chosen in the glycosylation reaction to form β-glycoside in terms of neighboring participation effect , and β-glycosylceramide was thus prepared ( S15 Fig ) . Removal of chloroacetyl protecting group of 16 gave acceptor ( 17 ) , which was used as generic acceptor for the assembly of GD3 and GM3 subsequently . Compound 17 was further subject to saponification and Birch reduction to afford pure β GlcCer . Assembly of stereo-controlled GD3 tetrasaccharide was implemented by coupling of ( 17 ) with ( 22 ) , followed by global deprotection and reduction . The stereo-controlled synthesis of GM3 was completed in the same fashion as stereo-controlled GD3 . The stereo-controlled GD3 and GM3 matched up with GD3 and GM3 synthesized with general method by 1HNMR spectra comparison ( S16 and S17 Figs ) . Comparison of controlled GD3 or GM3 with GD3 or GM3 made with conventional method in biological assays using type I natural killer T cell ( NKT ) hybridomas indicated no differences in their antigenic capacities . Separation and identification of α- and β-GlcCers was conducted with the recently published HILIC-MS2 method [15] . 1H NMR , HPLCs , and LC-MS profiles of synthesized GD3 and GM3 are included in ( S18–S21 Figs ) . Synthetic chemistry of gangliosides will be published separately . Quantitative analysis of the interaction between mCD1d and glycolipids was performed by Microscale Thermophoresis ( MST ) using a Monolith NT115 instrument ( NanoTemper Technology GmbH Munich , Germany ) . Recombinant mCD1d protein ( MW = 33 . 7 kDa ) was labeled with a reactive RED dye ( NT-647 ) by N-hydroxysuccinimide ( NHS ) coupling ( NanoTemper red-NHS kit ) following the manufacturer's protocol . Briefly , mCD1d ( 15 μM ) was mixed with the dye ( 45 μM ) and incubated for 30 min in the labeling buffer ( 130 mM NaHCO3 , 50 mM NaCl , pH 8 . 2 ) at room temperature . Unincorporated dye was removed by gel filtration with a Sephadex G-25 column , and mCD1d was finally collected in PBS 25 mM , 0 . 05% Tyloxapol to a final concentration of 2 . 5 μM . The thermodynamic affinity constant characterizing the molecular interaction of mCD1d with each analyzed glycolipid was determined by performing a titration of the corresponding nonfluorescent glycolipids against a constant concentration of NT-647-labeled mCD1d ( 125 nM ) . Briefly , C16:0 GM3 , C24:1 GM3 , C16:0 GD3 , C24:1 GD3 , and C24:1 β-GlcCer were solubilized in DMSO . A 10-fold factor dilution of these stock solutions was performed in PBS 25 mM , 0 . 05% Tyloxapol to reach a final maximal proportion of DMSO equal to 5% . Thus , titration of each glycolipid was performed by serial dilutions of these solutions in PBS 25 mM , 0 . 05% Tyloxapol to which an equal volume of NT-647-labeled mCD1d was added . Then , reaction mixture was loaded into Premium capillaries and subsequently analyzed by MST using 60% MST power with a laser-on time of 30 sec per samples and an intensity of the light-emitting diode ( LED ) of 20% . Fluorescence time trace for each glycolipid concentration was recorded for each interaction . All analysis was performed in triplicate by using the NanoTemper MO . affinity analysis software version 2 . 1 and thermodynamic dissociation constant ( KD ) characterizing the molecular interaction between mCD1d , and each glycolipid was determined by plotting the temperature-dependent change of the normalized fluorescence ( ΔFnorm = FHot/Fcold , with Fcold = fluorescence intensity before the IR-Laser is on [area marked in blue] , and Fhot = fluorescence intensity 1 sec before the laser is off [area marked in red] ) with the corresponding concentration of each unlabeled glycolipid . The resulting binding curves were fitted using a 1:1 binding model to determine the average KD values . To investigate iNKT cell reactivity , 1 x 105 BMDCs were cultured with 1 x 105 mouse iNKT hybridomas DN32 . D3 and/or 2C12 in presence of CpG ODN , purified glycolipids or vehicle in complete RPMI media supplemented with 5% FCS for 24 h . In some cases , neutralizing or control Abs were added during the coculture . To fix DCs , cells were exposed to glutaraldehyde ( 0 . 05% in PBS ) for 3 min and then extensively washed . To investigate the activity of lipids extracted from DCs , 1 x 105 DCs were exposed to lipid fractions ( 1/500 of total extracts from 5 x 107 BMDCs ) or vehicle alone for 16 h , washed , and then cocultured with liver MNCs ( 5 x 105 ) in the presence or absence of recombinant mIFNβ ( 1000 U/ml ) . Coculture supernatants were collected , and cytokine production was measured by ELISA ( R&D Systems ) . To probe the presence of α-monohexosylceramides in our synthetic compounds , glycolipids were loaded on CD1d dimers for 16 h ( at a 200 molar excess of lipids except for α-GalCer that was loaded at a 20 molar excess ) in presence of 0 . 5% of Tyloxapol . Then , CD1d:glycolipid complexes were coated on a flat bottom 96-well plate for 4 h in presence of PE-labelled L363 mAb or appropriate Ig control . Fluorescence at 578 nM was measured on a microplate reader TECAN infinite ( Männedorf , Switzerland ) . To test the presence of α-monohexosylceramide:CD1d complexes in ganglioside-loaded DCs , WT-derived BMDCs were loaded for 16 h with the various synthetic gangliosides ( 6 . 5 μM ) , extensively washed , and labeled with the L363 mAb or Ig control . α-GalCer ( 0 . 2 nM ) was used as a positive control . Unloaded CD1d tetramers were loaded for 16 h with a 5 ( α-GalCer ) or 50 ( gangliosides ) molar excess of glycolipids in presence of 0 . 5% of Tyloxapol . Unloaded or loaded CD1d tetramers were then probed against NKT hybridomas . Mice were IN injected with α-GalCer ( 500 ng/mouse ) or ganglioside species ( 5 or 25 μg/mouse ) in 50 μl of saline . In some cases , mice were IP pretreated ( 4 h ) with an anti-CD1d mAb ( 19G11; 500 μg/mouse ) or its isotype control ( LTF-2 ) . Twelve hours later , lungs were harvested , and leukocyte suspensions were prepared by classical procedures . Briefly , perfused lungs were harvested and finely minced in a Petri dish . Then , lung pieces were enzymatically digested ( 20 min at 37°C ) in saline containing 1 mg/ml of collagenase type VIII ( Sigma-Aldrich ) and 1 μg/ml of DNase type I ( Roche ) . After washes , pellets were resuspended in a 20% Percoll gradient and centrifuged ( 2 , 000 rpm at RT for 15 min ) . Cells in pellet were collected and washed in PBS containing 2% FCS . Erythrocytes were removed using a red blood cell lysis buffer ( Sigma-Aldrich ) . Lung mononuclear cells were incubated in complete RPMI 1 , 640 medium in presence of Golgi Plug/Golgi Stop ( BD Biosciences ) ( 2 h at 37°C ) . After washes , cells were labelled with the appropriate dilutions of the various mAbs ( 30 min at 4°C ) in PBS containing 2% FCS . Cells were then washed , and fixed using an intracellular fixation buffer ( eBioscience , CliniSciences , Montrouge , France ) . Next , fixed lung mononuclear cells were treated with a permeabilization buffer ( eBioscience ) according to the manufacturer’s instructions . Cells were stained with anti-IFNγ and anti-IL-17A mAbs or corresponding isotype controls and acquired on a Fortessa ( Becton Dickinson , Rungis , France ) cytometer . Analyses were performed using the FlowJo software ( Treestar , OR , USA ) . S . pneumoniae serotype 1 clinical isolate E1586 sequence type ST304 has been described elsewhere [33] . Mice were anesthetized and administered IN with 50 μl PBS containing live bacteria ( 1 x 106 cfu ) . Twelve hours prior infection , mice were IN injected with 500 ng of α-GalCer ( 0 . 58 nmol ) or 25 μg of gangliosides ( 20 nmol for GM3 and 16 nmol for GD3 ) in 40 μl of saline . Then , mice were monitored daily for illness and mortality for a period of 9 days . Results are expressed as the mean ± SEM . All statistical analysis was performed using GraphPad Prism software ( San Diego , CA , USA ) . The statistical significance was evaluated using nonparametric ( paired or unpaired ) Mann–Whitney U or Kruskal–Wallis ( followed by a Dunn’s post-test ) tests to compare the means of biological replicates in each experimental group . Survival rates after S . pneumoniae challenge were analyzed using a log-rank test . Results with a P value of less than 0 . 05 were considered significant . ns , not significant; *P < 0 . 05; **P < 0 . 01; ***P < 0 . 001 .
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Invariant natural killer T ( iNKT ) cells are a population of unconventional T lymphocytes that activate rapidly during inflammation due to their innate-like features . They are unconventional since they do not react to peptidic antigens ( Ags ) presented by classical major histocompatibility complex ( MHC ) molecules; instead , they recognize lipid-based Ags in the context of the MHC class I-like molecule CD1d . While numerous Ags of microbial origins have been described , their endogenous Ags are far less understood and remain a matter of strong debate . Here , we report that engagement of an innate receptor on the Ag-presenting cells leads to modulation of their lipid metabolism . This results in an enrichment of particular glycosphingolipid species that differ in both the nonpolar tail and polar head structures . Among those , two species have the potential to activate iNKT cells in a CD1d-dependent manner after further intracellular modifications . Based on these data , we propose a concept that iNKT cells can rapidly respond to pre-existing self-molecules after stress-induced changes in CD1d-expressing cells . Given the presence of closely related molecules in some pathological conditions such as cancer , it will be interesting to evaluate the biological relevance of these Ags in disease states .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"glycolipids",
"medicine",
"and",
"health",
"sciences",
"immune",
"physiology",
"immune",
"cells",
"sphingolipids",
"antigen-presenting",
"cells",
"biological",
"cultures",
"immunology",
"dendritic",
"cells",
"hybridomas",
"research",
"and",
"analysis",
"methods",
"immune",
"system",
"proteins",
"lipids",
"animal",
"cells",
"proteins",
"antigens",
"cell",
"lines",
"biochemistry",
"signal",
"transduction",
"t",
"cell",
"receptors",
"cell",
"biology",
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"interferons",
"biology",
"and",
"life",
"sciences",
"immune",
"receptors",
"cellular",
"types",
"glycobiology"
] |
2019
|
TLR9-mediated dendritic cell activation uncovers mammalian ganglioside species with specific ceramide backbones that activate invariant natural killer T cells
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Type I interferon response plays a prominent role against viral infection , which is frequently disrupted by viruses . Here , we report Bcl-2 associated transcription factor 1 ( Bclaf1 ) is degraded during the alphaherpesvirus Pseudorabies virus ( PRV ) and Herpes simplex virus type 1 ( HSV-1 ) infections through the viral protein US3 . We further reveal that Bclaf1 functions critically in type I interferon signaling . Knockdown or knockout of Bclaf1 in cells significantly impairs interferon-α ( IFNα ) -mediated gene transcription and viral inhibition against US3 deficient PRV and HSV-1 . Mechanistically , Bclaf1 maintains a mechanism allowing STAT1 and STAT2 to be efficiently phosphorylated in response to IFNα , and more importantly , facilitates IFN-stimulated gene factor 3 ( ISGF3 ) binding with IFN-stimulated response elements ( ISRE ) for efficient gene transcription by directly interacting with ISRE and STAT2 . Our studies establish the importance of Bclaf1 in IFNα-induced antiviral immunity and in the control of viral infections .
Herpesviridae is a family of large DNA viruses with an ability to establish persistent infection in hosts . The viruses have evolved multiple strategies to establish persistent infection and combat host defenses; among these , the interferon ( IFN ) antiviral response is most prominent . Members of the family are causative agents of a variety of human and animal diseases and are further grouped into the three subfamilies , including alpha- , beta- and gammaherpesviruses [1] . The alphaherpesvirus subfamily is neurotropic , including the genera simplexvirus and varicellovirus . Pseudorabies virus ( PRV ) and herpes simplex virus type 1 ( HSV-1 ) belong to the alphaherpesvirus subfamily and the genera varicellovirus and simplexvirus , respectively . They are often used as model viruses to study alphaherpesvirus biology . PRV is a swine pathogen that causes the economically important Aujeszky's disease [2 , 3] . HSV-1 is a human restricted virus , resulting in various mucocutaneous diseases , such as herpes labialis , genital herpes , herpetic whitlow , and keratitis [4] . It also causes serious encephalitis in a small portion of the infected individuals [4] . Viral infection is defended by hosts at multiple levels , including intrinsic , innate and adaptive immunity . The type I Interferon ( IFN-I ) response plays a central role in innate immunity against viral infection . IFN-I positions cells in a potent antiviral state by inducing the synthesis of hundreds of antiviral proteins encoded by IFN-stimulated genes ( ISGs ) . This process is initiated by binding of IFN-I to its receptor subunits ( IFNAR1 and IFNAR2 ) , which leads to the activation of the Janus Kinases ( JAKs ) , JAK1 and TYK2 . Activated JAKs then phosphorylate signal transducer and activator of transcription ( STAT ) 1 and 2 , leading to the formation of a trimeric complex , referred to as IFN-stimulated gene factor 3 ( ISGF3 ) , which is comprised of STAT1/STAT2 and IFN regulatory factor 9 ( IRF9 ) . ISGF3 translocates to the nucleus and binds to IFN-stimulated response elements ( ISRE ) in the DNA to initiate the transcription of ISGs [5–7] . Many of the gene products have potent antiviral functions [8] . Viruses have , in turn , evolved various strategies to antagonize the functions of IFN , which might be particularly important for herpesviruses to establish persistent infection in hosts [9–11] . Key molecules in IFN signaling are targeted by various components of alphaherpesviruses . For example , PRV or HSV-1 utilize their encoded dUTPase UL50 to induce IFNAR1 degradation and inhibit type I IFN signaling in an enzymatic activity-independent manner [12] . Increasing evidence indicates that IFN signaling is subject to extensive regulation and that additional coregulators are required to modulate the transcription of ISGs . For instance , the methyltransferase SETD2 promotes IFNα-dependent antiviral immunity via catalyzing STAT1 methylation on K525 [13]; RNF2 increases the K33-linked polyubiquitination of STAT1 at position K379 to promote the disassociation of STAT1/STAT2 from DNA and suppress the transcription of ISGs [14] . The molecules that participate in IFN-induced transcription could be potential targets of herpesviruses . Thus , identifying novel components in IFN signaling and their interactions with viral molecules will provide a deeper understanding of IFN signaling and its interaction with viral infection . US3 is a conserved Ser/Thr kinase encoded by every alphaherpesvirus identified thus far [15] . It critically participates in the pathogenicity of viruses in vivo and is involved in the nuclear egress of viral capsids [16 , 17] . As a viral kinase , US3 expression impacts host cells in many aspects , including cytoskeletal alteration [18–20] , the inhibition of histone deacetylase 1 and 2 ( HDAC1/2 ) [21 , 22] , and , more notably , disruption of various host defense mechanisms . US3 prevents host cells from apoptosis [23–25] , disrupts the antiviral subnuclear structures promyelocytic leukemia nuclear bodies ( PML-NBs ) [26] , down-regulates major histocompatibility complex ( MHC ) class I surface expression [27] , and interferes with the IFN response [28–31] . Bclaf1 ( Bcl-2 associated transcription factor 1; also called Btf for Bcl-2 associated transcription factor ) was initially identified in a yeast two-hybrid system as a binding protein for adenovirus E1B 19K protein [32] . It contains homology to the basic zipper ( bZip ) and Myb domains and binds DNA in vitro [32] . Bclaf1-knockout mice are embryonic lethal due to defects in lung development [33] . Bclaf1 participates in diverse biological processes , including apoptosis [32] , autophagy [34] , DNA damage response [35 , 36] , senescence [37] , cancer progression [38 , 39] and T cell activation [40] . Recently , a role for Bclaf1 in herpesviral defense is emerging , and more strikingly , Bclaf1 is targeted by multiple viral components . The betaherpesviruse human cytomegalovirus ( HCMV ) dispatches both viral proteins ( pp71 and UL35 ) and a microRNA to diminish cellular Bclaf1 levels [41] . Bclaf1 is also identified as a target of several latently expressed microRNAs of the gammaherpesviruse Kaposi’s sarcoma-associated herpesvirus ( KSHV ) [42] . The fact that multiple mechanisms have been utilized by the members of beta- and gammaherpesviruses to suppress the expression of Bclaf1 indicates that this protein has a very important antiviral function . However , whether Bclaf1 is also involved in alphaherpesvirus infection and the molecular mechanism of its antiviral function are not known . In this study , we examined the role of Bclaf1 in alphaherpesvirus infection and found that Bclaf1 is also degraded during PRV and HSV-1 infection through US3 . More importantly , we revealed Bclaf1 as a critical regulator in the IFN-induced antiviral response . On the one hand , Bclaf1 maintains a mechanism that allows STAT1/STAT2 to be efficiently phosphorylated in response to IFN; on the other hand , it interacts with ISGF3 complex in the nucleus mainly through STAT2 and facilitates their interactions with the promoters of ISGs . These results reveal a critical role for Bclaf1 in IFN signaling and a strategy employed by alphaherpesvirus to disable it .
To examine the effect of alphaherpesvirus infection on Bclaf1 , we infected porcine cells with PRV and human cells with HSV-1 . We observed a dramatic decrease in Bclaf1 levels in all the cells examined at the time points when substantial viral proteins were expressed , including porcine kidney PK15 ( Fig 1A ) , swine testis ( ST ) ( S1A Fig ) cells and human HEp-2 ( Fig 1B ) cells . Bclaf1 reduction appeared to occur more rapidly during HSV-1 infection . Since Bclaf1 is degraded in the proteasome upon HMCV infection , we examined if this was the case for PRV and HSV-1 . We treated the cells with the proteasome inhibitor MG132 for 8 h at 1 h after viral adsorption . Compared with the control , the MG132 treatment blocked PRV and HSV-1 infection induced down-regulation of Bclaf1 and had minimal effect on viral protein expression ( Fig 1C and 1D ) . Real-time PCR showed that Bclaf1 mRNA levels were not changed or slightly increased during PRV and HSV-1 infection despite its protein levels were drastically decreased ( S1B and S1C Fig ) . These results suggest that both PRV and HSV-1 infection trigger a targeted and proteasome-dependent degradation of Bclaf1 . To determine the viral protein responsible for the Bclaf1 degradation , we utilized a panel of gene deletion PRVs , particularly EP0 , US3 and UL50 deleted strains , since these viral proteins are involved in the degradation of various proteins [12 , 26 , 43] . Infecting cells with WT and the gene deletion PRVs showed that only the PRV ΔUS3 strain lost the ability to degrade Bclaf1 ( Fig 1E ) . Indeed , although the Bclaf1 levels in the cells infected with PRV WT decreased over time up to 24 h post infection , those in the PRV ΔUS3 infected cells remained unchanged in the PK15 cells ( Fig 1F ) and even increased in the ST cells ( S1D , S1E and S1F Fig ) . Similarly , the deletion of US3 from HSV-1 also abolished its ability to decrease Bclaf1 in the HEp-2 cells ( Fig 1G ) . Collectively , these data indicate that US3 is essential for PRV- and HSV-1-induced Bclaf1 down-regulation . It also suggests that certain cells may respond to PRV and HSV-1 infection by increasing Bclaf1 , which is concealed by US3 mediated Bclaf1 down-regulation . To determine if US3 alone is sufficient to induce Bclaf1 degradation , we ectopically expressed PRV or HSV-1 US3 in HEK293T cells . The expression of US3 but not the empty vector or UL50 markedly reduced endogenous Bclaf1 ( S1G Fig ) , which was rescued by MG132 treatment ( Fig 1H ) . These results suggest that US3 induces the proteasomal degradation of Bclaf1 . The degradation of Bclaf1 upon PRV/HSV-1 infection by US3 suggests that Bclaf1 may possess an important antiviral function , which is inhibited by US3 but should be in action against US3 deficient viruses . Thus , to determine the role of Bclaf1 in viral infection , we focused on the differential properties between WT and ΔUS3 PRV infected cells . Although one well-known function of US3 is antiapoptosis , and Bclaf1 has been shown to be involved in it , we observed a similar level of apoptosis induced by ΔUS3 PRV infection in the Bclaf1 knockdown and control cells ( S2A Fig ) . The dramatic difference we observed between the WT and ΔUS3 PRV/HSV1 was that the latter was more susceptible to interferon . The deletion of US3 in PRV/HSV-1 significantly decreased viral productions in PK15 ( PRV ) and HEp-2 ( HSV-1 ) cells treated with IFNα , while having no or a slight influence on viral growth in the absence of interferon treatment ( Fig 2A , 2B , 2C and 2D ) . As expected , Bclaf1 was not degraded in ΔUS3 PRV/HSV-1 infected cells . In addition , IFNα induced ISG15 expression was much higher in HSV-1 ΔUS3 infected cells than that of WT HSV-1 infected cells ( Fig 2C ) . To determine whether Bclaf1 was involved in interferon mediated viral suppression , we depleted Bclaf1 using siRNAs in PK15 and HEp-2 cells or utilized a Bclaf1 knockout HeLa cell line and then infected the cells with ΔUS3 PRV/HSV-1 treated with or without IFNα . Compared with their respective controls , the expression of viral proteins and viral productions in Bclaf1 knockdown or knockout cells was significantly increased when treated with IFNα ( Fig 2E , 2F , 2G and 2H and S2B , S2C Fig ) , whereas IFNα induced ISG15 expression was reduced ( Fig 2G and S2B Fig ) . Altogether , our data supports that Bclaf1 enhances the IFNα-induced antiviral function against ΔUS3 PRV/HSV-1 infection . We then examined the effect of Bclaf1 depletion on IFNα-induced gene transcription . Using an ISRE luciferase reporter assay , real time PCR and Western analysis , we showed that the IFNα-induced luciferase activity and upregulation of mRNAs and proteins of the examined ISGs were all much lower in HeLa Bclaf1-KO cells than those in control HeLa cells ( Fig 3A , 3B and 3C ) . Knockdown of Bclaf1 in HEp-2 cells ( Fig 3D ) or in PK15 cells ( S3 Fig ) using siRNAs also reduced IFNα-induced transcription . The deficiency of ISG induction in Bclaf1-KO HeLa cells after IFNα treatment was partially restored by the overexpression Bclaf1 ( Fig 3E ) . Collectively , these data suggest that Bclaf1 enhances IFNα-induced transcription . To understand the exact role of Bclaf1 in the IFN signaling , we analyzed the signaling events that might be impaired in Bclaf1-deficient cells . We observed reduced courses of phosphorylation for STAT1 and STAT2 in response to IFNα in Bclaf1-KO HeLa cells ( Fig 4A ) and Bclaf1-silenced HEp-2 cells ( Fig 4B ) compared with relative control cells . Fractionation experiments demonstrated that the IFNα-induced nuclear translocation of STAT1/STAT2 in the Bclaf1-knockdown cells was reduced accordingly ( Fig 4C ) . Thus , the loss of Bclaf1 impairs the IFNα-induced phosphorylation of STAT1/STAT2 . Because the majority of Bclaf1 localized in the nucleus , the mechanism for Bclaf1 to influence this step is likely indirect , possibly through altering the expression levels of the components essential for STAT1/STAT2 phosphorylation . However , no obvious difference in the major components , including Receptor , JAK1 , TYK2 , STAT1 and STAT2 , between the Bclaf1 knockdown or knockout cells and the WT controls was observed ( S4C and S4D Fig ) . In addition , we performed Chromatin Immunoprecipitation ( ChIP ) assays to see whether depletion of Bclaf1 affected the binding of ISGF3 to the promoters of ISGs . We treated HeLa WT and Bclaf1-KO cells with IFNα and performed ChIP assay using STAT1/STAT2/IRF9 antibodies respectively . The results showed that IFNα-induced binding of STAT1/STAT2/IRF9 to the promoters of ISGs was also greatly decreased in Bclaf1-KO HeLa cells ( Fig 5A ) and Bclaf1-silenced HEp-2 cells ( S5 Fig ) compared with that in relative control cells . Because Bclaf1 predominantly localized in the nucleus , we reasoned that Bclaf1 should exert its function in the nucleus and that the reduced STAT1/STAT2 phosphorylation by IFNα upon Bclaf1 reduction could be an indirect consequence . Therefore , we focused on the aspect that Bclaf1 may enhance the binding of ISGF3 to the promoters . To exclude the possibility that the impaired binding between STAT1/STAT2 to the ISGs promoters in the Bclaf1-knockdown cells was due to the reduced nuclear STAT1/STAT2 in these cells , we performed a DNA pull-down assay to directly measure whether STAT1/STAT2/IRF9 binding to the promoters was enhanced by Bclaf1 . An ISRE DNA was synthesized , biotin-labeled , and added into equal amounts of purified STAT1/STAT2/IRF9 as well as increased concentrations of purified Bclaf1 followed by a streptavidin-bead pull-down . The addition of Bclaf1 drastically increased the binding of STAT1/STAT2/IRF9 to Bio-ISRE in a dose-dependent manner , and Bclaf1 was present in the Bio-ISRE pull-down complex ( Fig 5B ) . Purified Bclaf1 was pulled down by Bio-ISRE but not by Bio-GFP ( Fig 5C ) , suggesting that Bclaf1 was directly bound to ISRE specifically . The ChIP assay confirmed that Bclaf1 was bound to the promoter regions of ISGs in HeLa cells ( Fig 5D ) , which appeared to be constitutive and was not induced by IFNα treatment . To further characterize the DNA sequence required for binding with Bclaf1 , we replaced entire ISRE consensus sequence ( Mut1 ) or the core sequence of 5’-TTCNNTTT-3’ [44] ( Mut2 ) with a sequence from GFP . We also mutated the TTT motif near the 3’ end of the ISRE by chancing TTT to TAT ( Mut3 ) . DNA pull-down assays demonstrated that Mut1 and Mut2 failed to interact with Bclaf1 , whereas Mut3 still could ( Fig 5E ) , indicating Bclaf1 binds with the core sequence of ISRE and the TTT motif is not required . In aggregate , these data demonstrated that Bclaf1 bound with ISRE specifically and promoted the association of ISGF3 with DNA . To understand the molecular mechanism by which Bclaf1 facilitates ISGF3 binding to ISGs promoters , we performed co-IP assays to examine the interaction between Bclaf1 and ISGF3 , which is composed of STAT1 , STAT2 and IRF9 . We constructed a HEp-2 cell line that endogenously expresses Flag-Bclaf1 by adding a Flag to the Bclaf1 gene using the CRISPR/Cas9 technique and is referred as HEp-2-Flag-Bclaf1 . Fractionation of the cells followed by co-IPs using a Flag-antibody showed that Flag-Bclaf1 interacted with STAT1 , STAT2 and IRF9 in the nucleus where it mainly localized ( Fig 6A and 6B ) . Reversely , endogenous Bclaf1 was also detected in the immuno-complexes of STAT1 , STAT2 or IRF9 after IPs of nuclear extracts of HeLa cells using their respective antibodies ( S6 Fig ) . The interaction between Bclaf1 and STAT1/STAT2/IRF9 occurred in the absence of IFNα treatment and was increased after IFNα treatment , correlating with more STAT1/STAT2/IRF9 being translocated into the nucleus ( Fig 6A and 6B and S6 Fig ) . We further determined the regions in Bclaf1 that mediated its association with STAT1 , STAT2 or IRF9 by co-expressing various Flag tagged Bclaf1 fragments with Ha tagged STAT1 , STAT2 or IRF9 in HEK293T cells and performing co-IPs , and identified the region 236–620 responsible for binding to these proteins ( Fig 6C ) . To examine whether the interaction between Bclaf1 and STAT1/STAT2/IRF9 is required for its ability to enhance IFNα transcription , we overexpressed Bclaf1 full-length and the indicated fragments in HEp-2 followed by IFNα treatment . mRNA measurements showed that the IFNα-induced IFIT1 transcription was enhanced by full-length Bclaf1 and Bclaf1-F2 ( 236–620 ) , and not by the fragments that failed to bind with STAT1/STAT2/IRF9 ( Fig 6D ) . Taken together , these results suggest that Bclaf1 interacts with ISGF3 complex in the nucleus , which is important for Bclaf1 to enhance the activation of ISRE after IFNα stimulation . Next , we set out to determine how Bclaf1 interacts with ISGF3 . We first examined the direct interactions between Bclaf1 and the components of ISGF3 by mixing bacterially purified His-STAT1 , -STAT2 or -IRF9 with GST-Bclaf1 F2 followed by GST pull-down assays . Western analysis showed that only His-STAT2 was able to be pulled down specifically by GST-Bclaf1 F2 , whereas the other two were not ( Fig 7A ) . These results hinted that STAT2 is the crucial component connecting ISGF3 to Bclaf1 . In supporting this , co-IP assays showed that the interaction between Bclaf1 and STAT1 or IRF9 was enhanced by STAT2 , and not by IRF9 or STAT1 upon overexpression in 293T cells ( Fig 7B and 7C ) . Moreover , the interaction between Bclaf1 and STAT1 or IRF9 at endogenous levels was decreased upon STAT2 knockdown in HEp-2-Flag-Bclaf1 cells treated with IFNα ( Fig 7D ) . In addition , in vitro DNA pulldown assays demonstrated that in the absence of STAT2 Bclaf1 lost its ability to recruit the components of ISGF3 to ISRE ( Fig 7E ) . Collectively , these data indicate that STAT2 is the key component mediating the binding of Bclaf1 to ISGF3 complex . To investigate whether Bclaf1 possesses an antiviral function in vivo , we examined whether Bclaf1 was decreased in PRV-infected animals . We infected mice with PRV for 6 days and observed a marked decrease in Bclaf1 protein in lungs and brains of infected mice compared with that of control mice , indicating PRV degrades Bclaf1 in vivo ( Fig 8A ) . Next , we injected mice with control siRNAs or siRNAs against Bclaf1 and then infected the mice with PRV ΔUS3 . Western analysis showed that Bclaf1 was silenced effectively in lungs of the mice treated with Bclaf1 siRNAs on 3 days post infection ( dpi ) ( Fig 8B ) . As expected , viral proteins and titers were increased in the lungs of knockdown mice compared with that of control mice on 3 dpi ( Fig 8B and 8C ) . Although Bclaf1 levels restored on 6 dpi , more viral proteins and titers were detected in knockdown group compared with the controls ( Fig 8B and 8C ) . Moreover , hematoxylin-and-eosin staining showed greater inflammatory damage in the lungs of Bclaf1-knockdown mice , such as hyperemia , edema , leakage and swelling , relative to those in the lungs of control mice , after PRV ΔUS3 infection for 6 days ( Fig 8D ) . These data demonstrate that Bclaf1 exhibits antiviral function in vivo .
The IFN response is critical in the control of viral infection and is often evaded or antagonized by various viruses . Most identified strategies used by viruses to evade ISG expression emphasize on the known signaling molecules in the IFN pathway targeted by various viral components . Here , we revealed a novel positive regulator , Bclaf1 , in IFN signaling and its degradation by the viral protein US3 during alphaherpesvirus PRV and HSV-1 infection . The evidence supporting Bclaf1 as a critical regulator in IFN-mediated antiviral response includes the following: 1 ) IFNα-induced ISG transcription is greatly compromised in Bclaf1 knockdown or knockout cells; 2 ) Bclaf1 is required for the efficient phosphorylation of STAT1 and STAT2 induced by IFNα; 3 ) Bclaf1 binds with ISRE and facilitates the binding of ISGF3 complex to promoters of the ISGs; 4 ) Bclaf1 interacts with ISGF3 through STAT2; 5 ) Bclaf1 is degraded by US3 during PRV and HSV-1 infection; and 6 ) In the absence of US3 , PRV and HSV-1 become more sensitive to IFNα treatment , which is partly due to the unreduced level of Bclaf1 in the cells . These findings establish Bclaf1 as a critical positive regulator in IFN signaling and indicate its importance in host innate immunity against herpesvirus infection , which may be more broadly against other viruses as well . We demonstrated that Bclaf1 was involved in two critical steps in IFN signaling , including the efficient phosphorylation of STAT1 and STAT2 and binding of the transcriptional complex to ISGs promoters ( Fig 8E ) . At present , the mechanism by which Bclaf1 regulates STAT1/STAT2 phosphorylation is unknown . STAT1/STAT2 phosphorylation is catalyzed by JAK1 and TYK2 activated by IFN-induced receptor dimerization , which occurs rapidly in the membrane . We found Bclaf1 knockdown reduced interactions between endogenous JAK1 and STAT1/STAT2 ( S4A and S4B Fig ) . Because Bclaf1 primarily localizes in the nucleus , we think the mechanism for Bclaf1 to influence this step is likely indirect . Emerging evidence indicates that the modification states of these components , prior to IFN engagement , also affect STAT1 and STAT2 phosphorylation by JAKs [13 , 14 , 45–48] . For instance , Chen et al showed that methyltransferase SETD2-mediated methylation of STAT1 significantly enhanced STAT1 phosphorylation by JAK1 [13] . The result that the lack of Bclaf1 decreases STAT1/STAT2 phosphorylation without affecting the expression of upstream components suggests that Bclaf1 may be involved in pre-existing modifications of STAT1/STAT2 by regulating relevant enzymes . Although the JAK-STAT pathway is well established , the regulation of the STAT1/STAT2/IRF9-mediated transcription of ISGs in the nucleus is not fully understood . We demonstrated that Bclaf1 is an important positive regulator in this process . Although epigenetic modifications and chromatin-remodeling , in the context of the promoter region , are important avenues for the regulation of transcription [49 , 50] , Bclaf1 appears to function by enhancing the recruitment of ISGF3 complex to the promoter of the ISGs by simultaneously binding to the promoter of the ISGs and this complex . Bclaf1 constitutively bound to the promoter of the ISGs without being enhanced by IFNα . It also interacted with ISGF3 in the nucleus , which was not regulated by IFNα-induced STAT1/STAT2 phosphorylation . However , as more and more STAT1/STAT2/IRF9 entered the nucleus following the IFNα treatment , more STAT1/STAT2/IRF9 was found to bind to Bclaf1 and the promoter of the ISGs as well . Thus , one conceivable role of Bclaf1 in ISGF3 mediated transcription is acting as a mediator attracting ISGF3 to its prebound ISGs promoters for efficient transcription . A similar mode of action is also observed in Bclaf1-regulated C/EBPβ transcription [37] . Bclaf1 has a DNA-binding ability [32] , and we found that the binding between Bclaf1 and the promoter of the ISGs was likely to be a direct event . It would be interesting to further elucidate how Bclaf1 interacts with the promoter of the ISGs . US3 is a potent alphaherpesviral kinase involved in antagonizing a wide range of host antiviral mechanisms . Here , we uncovered a strategy for US3 to impair IFN-mediated antiviral activity , which is to degrade Bclaf1 . Bclaf1 was degraded by both genera of alphaherpeviruses and was also inhibited by members of beta- and gammaherpesviruses , indicating that the disruption of Bclaf1 might be a general mechanism for all herpesvirus infections . Since a key feature of herpesviruses is the establishment of a persistent infection and reactivation upon stress , Bclaf1 may participate in these processes . To establish persistent infection , herpesviruses employ multiple strategies to counteract the antiviral activity of IFN [51 , 52] , and the disruption of Bclaf1 might be an integral part of sabotaging IFN signaling by herpesviruses . In addition , Bclaf1 possesses other antiviral functions , such as restriction of HCMV replication and inhibition of KSHV reactivation . Others and our studies have highlighted an important role of Bclaf1 against herpesviruses infection , and it may be broadly for other viruses as well .
MG132 was purchased from APExBIO ( 133407-82-6 ) . Streptavidin beads ( 3419 ) were purchased from Cell Signaling Technology . Flag M2 beads ( A2220 ) and 3xFlag peptide ( F4799 ) were purchased from Sigma . Human IFNα was purchased from PEPROTECH ( 300-02AA ) . Glutathione agarose was purchased from GE Healthcare ( 17-0756-01 ) . Porcine IFNα was described previously [12] . Biotin 3’ End DNA Labeling Kit was purchased from Thermo Scientific ( 89818 ) . The following antibodies were used for co-Immunoprecipitation ( co-IP ) : anti-Bclaf1 ( 1:100 , sc-135845 , Santa Cruz ) , anti-Flag ( 1:200 , F1804 , Sigma ) , anti-STAT1 ( 1:100 , 14995 , Cell Signaling Technology ) , anti-STAT2 ( 1:50 , 72604 , Cell Signaling Technology ) , and anti-IRF9 ( 1:50 , 76684 , Cell Signaling Technology ) . The following antibodies were used for Chromatin Immunoprecipitation ( ChIP ) : anti-Bclaf1 ( 1:50 , sc-135845 , Santa Cruz ) , anti-IRF9 ( 1:50 , 76684 , Cell Signaling Technology ) , anti-STAT1 ( 1:50 , 14995 , Cell Signaling Technology ) and anti-STAT2 ( 1:50 , 72604 , Cell Signaling Technology ) . The following antibodies were used for immunoblot analysis: anti-Bclaf1 ( 1:500 , sc-135845 , Santa Cruz ) , anti-Flag ( 1:2000 , F1804 , Sigma ) , anti-α-Tubulin ( 1:8000 , PM054 , MBL ) , anti-HA ( 1:1000 , sc-805 , Santa Cruz ) , anti-GFP ( 1:1000 , sc-9996 , Santa Cruz ) , anti-ISG15 ( 1:500 , sc-166755 , Santa Cruz ) , anti-PKR ( 1:1000 , 12297 , Cell Signaling Technology ) , anti-STAT1 ( 1:1000 , 14995 , Cell Signaling Technology ) , anti-STAT2 ( 1:1000 , 72604 , Cell Signaling Technology ) , anti-P-STAT1 ( Tyr701 ) ( 1:1000 , 9167 , Cell Signaling Technology ) , anti-P-STAT2 ( Tyr690 ) ( 1:1000 , 88410 , Cell Signaling Technology ) , anti-IRF9 ( 1:1000 , 76684 , Cell Signaling Technology ) , anti-JAK1 ( 1:500 , 3344 , Cell Signaling Technology ) , anti-TYK2 ( 1:1000 , 14193 , Cell Signaling Technology ) , anti-Histone H3 ( 1:2000 , 17168-1-AP , Proteintech ) , and anti-caspase3 p17 ( 1:1000 , sc-166589 , Santa Cruz ) . The antibodies against PRV TK , PRV US3 , PRV EP0 , PRV UL50 , and HSV-1 VP5 were described previously[12 , 53 , 54] . Mouse polyclonal antibodies against PRV UL42 and HSV-1 US3 were raised in mice individually with the N-terminal region of each protein as antigens . HEK293T cells ( human embryonic kidney , ATCC #CRL-3216 ) , HeLa cells ( ATCC #CCL-2 ) , HEp-2 cells ( a kind gift from Dr . Xiaojia Wang which was described previously [55] ) , PK15 cells ( ATCC #CCL-33 ) , ST cells ( swine testis , ATCC #CRL-1746 ) , and Vero cells ( ATCC #CCL-81 ) were cultured in medium supplemented with 10% ( v/v ) FBS at 37°C and 5% CO2 . The PRV Bartha-K61 , recombinant PRV UL50-knockout virus ( PRV ΔUL50 ) , PRV EP0-knockout virus ( PRV ΔEP0 ) and KOS strain of HSV-1 were described previously [53 , 54] . The recombinant PRV US3-knockout virus ( PRV ΔUS3 ) and the HSV-1 US3-knockout virus ( HSV-1 ΔUS3 ) were generated in this paper ( see below ) . The PRV US3 gene was amplified from the Bartha-K61 genome , and the HSV-1 US3 gene was amplified from the KOS genome . Both PRV and HSV-1 US3 were cloned into the pRK5 vector with an N-terminal Flag tag . pRK5-Flag-PRV UL50 , pRK5-Flag-HSV-1 UL50 and pRK5-Flag-Bclaf1 were previously described [12 , 37] . Bclaf1 truncations were amplified by PCR from pRK5-Flag-Bclaf1 and were cloned into the pRK5 vector with an N-terminal Flag vector . pRK5-Ha-STAT1/STAT2/IRF9 were constructed by amplifying STAT1/STAT2/IRF9 ORFs by PCR from cDNA synthesized from the total RNA of IFNα-stimulated HeLa cells and cloning it into the pRK5 vector with an N-terminal Ha tag vector . Total RNA was extracted using TRIzol ( Invitrogen ) following the manufacturer’s protocol . A total of 0 . 8 μg total RNA from different treatments was reverse transcribed using M-MLV reverse transcriptase ( Promega ) with an oligo ( dT ) 18 primer . Real-time PCR was performed using an UltraSYBR Mixture ( Beijing CoWin Biotech , Beijing , China ) and a ViiA 7 real-time PCR system ( Applied Biosystems ) . Sample data were normalized to GAPDH expression . Specific primers used for RT–PCR assays are listed in S1 Table . Cells were harvested and lysed in lysis buffer ( 50 mM Tris-Cl at pH 8 . 0 , 150 mM NaCl , 1% Triton X-100 , 10 mM DTT , 1× complete protease inhibitor cocktail tablet and 10% glycerol ) . The nuclear and cytoplasmic extracts from cells were prepared using a Nuclear and Cytoplasmic Protein Extraction Kit ( Beyotime Biotechnology , Shanghai , China ) following the manufacturer’s instructions . Equalized extracts were used for the immunoprecipitation and immunoblot analysis , which were described previously [56] . Bclaf1-KO HeLa cells or control HeLa cells were seeded in 24-well plates and were then transfected with 100 ng of ISRE-luciferase reporter plasmids plus 20 ng of pRL-TK plasmids as an internal control . After 24 h of incubation , the cells were stimulated with PBS or IFNα , and whole-cell lysates were collected to measure the luciferase activity with a dual luciferase reporter assay kit ( Promega ) . PRV or HSV-1 were propagated and tittered in Vero cells . To infect , the cells were incubated with PRV or HSV-1 for 1 h , washed with PBS , and incubated in DMEM supplemented with 5% FBS until the times indicated . For the MG132 ( ApexBio ) treatment , a final concentration 20μM of MG132 was added into culture medium at 1 h post infection to allow efficient viral entry . The Viral yield was determined by tittering in the Vero cells . Briefly , infected cell supernatants were cleared of cell debris by centrifugation . The Vero cells were infected in duplicate or triplicate with serial dilutions of supernatants for 1 h in serum free DMEM , washed with PBS , overlaid with 1× DMEM/1% agarose , and incubated at 37°C until plaque formation was observed ( 72 h-96 h ) . The cells were stained with 0 . 5% neutral red for 4 h-6 h at 37°C , and the plaques were counted . PRV ΔUS3 was generated according to methods described previously [53] . Briefly , PK15 cells were cotransfected with the viral genome and the CRISPR/Cas9 system containing two targeting sgRNAs for US3 . After PRV-mediated CPE was prominently observed , the supernatants were collected , and the plaque assay was performed for subcloning the viruses . Single colonies were determined via sequencing and a Western blot with PRV US3 antibodies . For generation of HSV-1 ΔUS3 , HEK293T cells were transfected using the CRISPR/Cas9 system containing the targeting sgRNA for US3 , and 24 h later , the cells were infected with HSV-1 ( KOS ) at an MOI of 1 . Viruses in the supernatants were collected at 48 h post infection and was subcloned via plaque assays . Single colonies were determined via sequencing and a Western blot with HSV-1 US3 antibodies . Oligonucleotides used in this study are listed in S1 Table . Animal care and protocols were approved by Animal Welfare Committee of China Agricultural University . To detect Bclaf1 degradation in PRV-infected animals , six 6-week-old specific-pathogen-free ( SPF ) BALB/C mice were randomly divided into two groups . Mice in group 1 were intraperitoneally injected with 106 . 3 PFU of PRV . Mice in group 2 were injected with PBS as uninfected controls . The moribund and survived mice ( experiments were terminated at 6dpi ) were humanely euthanized and the organs including Lungs and brains were isolated for protein detection by western blot . To evaluate the antiviral function of Bclaf1 in vivo , siRNAs against Bclaf1 and control siRNAs were delivered into 6-week-old SPF BALB/C mice via intravenous injections using in vivo-jetPEI ( Polyplus ) according to the manufacturer’s protocol . Briefly , siRNAs and in vivo-jetPEI complexes were generated following the manufacturer’s protocol and injected into the tail veins of mice with a sterile syringe ( 1 . 0 mL ) and a 30-gauge needle . 24 h later , mice were injected again . Then the transfected mice were intraperitoneally injected with 106 . 4 PFU of PRV ΔUS3 or PBS 24 h after the second injection . The moribund and survived mice at 3dpi and 6dpi were humanely euthanized and the Lungs were used for protein and titer detection or stained with hematoxylin-eosin solution . HeLa cells were seeded into a 6-well dish to achieve 70% confluency and were transfected with CRISPR/Cas9 plasmids containing a target sequence complimentary to the fourth exon of Bclaf1 , and 48 h later , the cells were diluted and seeded into a 96-well dish at 0 . 5 cell/well in complete DMEM media . Wells that contained a single colony were expanded until enough cells were available for total protein extraction and determining Bclaf1 via a Western blot . Oligonucleotides used in this study are listed in S1 Table . To add a Flag tag to the endogenous Bclaf1 , HEp-2 cells were seeded into a 6-well dish to achieve 70% confluency and were transfected with CRISPR/Cas9 plasmids containing a target sequence complimentary to the intron that was prior to the ATG of Bclaf1 plus a donor plasmid containing homologous arms and Puro-P2A-3×Flag sequences . After 48 h , medium containing 2 . 5 mg/ml puromycin was added to select for tagged cells , and 48 h later , the cells were diluted and seeded into a 96-well dish at 0 . 5 cell/well in complete DMEM media . Wells that contained a single colony were expanded until enough cells were available for total protein extraction and determining Flag-Bclaf1 via a Western blot . Oligonucleotides used in this study are listed in S1 Table . siRNAs against Bclaf1 ( 1# 5’-GGTTCACTTCGTATCAGAA-3’ ) , ( 2# 5’-TTCTCAGAATAGTCCAATT-3’ ) , ( mouse 5’-GCTACTTCTGGTGATATTT-3’ ) and STAT2 ( 5’-CCCAGUUGGCUGAGAUGAUCUUUAA-3’ ) were transfected using Lipofectamine RNAiMax ( Invitrogen ) at a final concentration of 20 nM following the manufacturer’s instructions . The ChIP assay was performed using a ChIP-IT Express enzymatic system ( Active Motif , Carlsbad , CA , USA ) following the manufacturer’s instructions . Briefly , cells were crosslinked with 1% formaldehyde and neutralized with 0 . 125 M glycine . Purified chromatin was digested to ~ 500 bp by enzymatic shearing . Anti-Bclaf1 , anti-STAT1 , anti-STAT2 , anti-IRF9 or control IgG antibodies were used for immunoprecipitation . After reverse crosslinking , the DNA samples were analyzed by PCR followed by 3% agarose gel electrophoresis . Specific primers used are listed in S1 Table . Flag-STAT1 , Flag-STAT2 , Flag-IRF9 and Flag-Bclaf1 were purified from overexpressed HEK293T cells stimulated with ( STAT1/STAT2/IRF9 ) or without ( Bclaf1 ) IFNα by immunoprecipitation using M2 beads ( Sigma ) . The biotinylated ISRE ( 5’-GAGACTCAGTAGTTTCACTTTCCATCGTCCAGT-3’ ) DNA oligos were synthesized by a Biotin 3´ End DNA Labeling Kit ( Thermo Scientific ) and were then annealed and incubated with the purified indicated Flag-tagged proteins for 30 min in binding buffer ( 10 mM Tris , 1 mM KCl , 1%NP-40 , 1 mM EDTA , 5% glycerol ) at room temperature . Then , streptavidin beads ( Cell Signaling ) were added for incubation at 4°C for 1 h . After three washes with binding buffer , the ISRE-binding proteins were eluted by boiling and analyzed by immunoblotting . Purified His-STAT1/STAT2/IRF9 protein was incubated with GST-tagged Bclaf1 truncated proteins or GST control protein in PBS buffer with glutathione agarose ( GE Healthcare ) for 1 h at 4°C . The incubated proteins were then washed and immunoblotted using anti-His or GST antibodies . Statistical analyses were performed using GraphPad Prism software to perform Student’s t test or analysis of variance ( ANOVA ) on at least three independent replicates . P values of <0 . 05 were considered statistically significant for each test .
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Alphaherpesvirus , such as Pseudorabies virus ( PRV ) and Herpes simplex virus type 1 ( HSV-1 ) , can establish persistent infection and cause various diseases in hosts . Interferon ( IFN ) response is hosts’ first defense system against viral infection . Here , we report alphaherpesvirus induces degradation of a host protein , Bclaf1 , via its expressed viral protein US3 upon infection . We further show that Bclaf1 is a novel regulator of IFN pathway by enhancing the IFN induced transcriptions of anti-viral genes . In the absence of Bclaf1 , IFN induced anti-viral activity is greatly reduced . Our study highlight the importance of Bclaf1 in IFN mediated antiviral function and reveal a strategy employed by alphaherpesvirus to counteract hosts’ defense .
|
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"hela",
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2019
|
Bclaf1 critically regulates the type I interferon response and is degraded by alphaherpesvirus US3
|
Non-Typhoidal Salmonella ( NTS ) is an important cause of invasive bacterial disease and associated with mortality in Africa . However , little is known about the environmental reservoirs and predominant modes of transmission . Our study aimed to study the role of domestic animals in the transmission of NTS to humans in rural area of The Gambia . Human NTS isolates were obtained through an active population-based case-control surveillance study designated to determine the aetiology and epidemiology of enteric infections covering 27 , 567 Gambian children less than five years of age in the surveillance area . Fourteen children infected with NTS were traced back to their family compounds and anal swabs collected from 210 domestic animals present in their households . Identified NTSs were serotyped and genotyped by multi-locus sequencing typing . NTS was identified from 21/210 animal sources in the households of the 14 infected children . Chickens carried NTS more frequently than sheep and goats; 66 . 6% , 28 . 6% and 4 . 8% respectively . The most common NTS serovars were S . Colindale in humans ( 21 . 42% ) and S . Poona in animals ( 14 . 28% ) . MLST on the 35 NTS revealed four new alleles and 24 sequence types ( ST ) of which 18 ( 75% ) STs were novel . There was no overlap in serovars or genotypes of NTS recovered from humans or animal sources in the same household . Our results do not support the hypothesis that humans and animals in close contact in the same household carry genotypically similar Salmonella serovars . These findings form an important baseline for future studies of transmission of NTS in humans and animals in Africa .
Non-Typhoidal Salmonella ( NTS ) are important causes of invasive bacterial diseases and are associated with substantial mortality . In rural Gambia , NTS was the second most important blood culture isolate after Streptococcus pneumoniae in children with invasive bacterial disease [1] . Similarly , a study in an urban hospital of The Gambia revealed that NTS represented 8 . 6% of the bacteraemia cases [2] . NTS also cause serious dysentery and septicaemia particularly in young infants [3] , [4] . However , little is known about environmental reservoirs and predominant modes of transmission especially in the African context [5] , [6] . Various sources including farm animals , pets and reptiles have been potentially implicated in the transmission of NTS between animals and humans but their direct involvement in the transmission has never been demonstrated [7] . The asymptomatic Salmonella carrier state in poultry has serious consequences on food safety and public health due to the risks of food poisoning following consumption of contaminated products . Salmonella enterica serovar Enteritidis can persist in the caecum or ovaries of chickens without triggering any clinical signs . Salmonellosis in young chickens may cause high mortality as a result of severe diarrhoea and dehydration , and include a greater risk of evolving into a carrier state in the surviving animals [8] , [9] , [10] . Small ruminants , such as sheep and goats , are also potential carriers of Salmonella [11] , [12] . In The Gambia , like in most of the African countries , the majority of the population lives in rural areas and depends on agriculture and livestock . In these areas , the most common animals kept in the compound are chickens , sheep and goats . Therefore , farmers and their families often live in close contact with their livestock and even under the same roof and are thus at increased risk of contracting zoonotic infections , particularly children who often play on the ground . Domestic animals may thus play an important role in the maintenance and transmission of NTS to humans at the community level . Investigating the relatedness between human and animal strains of NTS could provide useful information about the epidemiology of these pathogens . Our study seeks to assess the contribution of domestic animals to the transmission of NTS to humans and to study the genetic relatedness between human and animal strains . This study will also provide useful information about the prevalence of NTS in domestic animals in rural areas of The Gambia .
The study protocol and consent form were approved by the Ethics Committees of the Joint Gambian Government/MRC Ethics Committee and the Ethics Committees of the London School of Hygiene and Tropical Medicine , UK and by the Institutional Review Board of the University of Maryland , Baltimore . For any patient eligible for the study written informed consent was obtained prior to their enrollment after the objectives and risks and benefits associated with participation . 95% of eligible subjects agreed to participate . If the participant was illiterate , a witness who was present throughout the consent procedure completed the necessary portions and signed the consent form; the parent/participant marked the consent form ( either fingerprint or other notation ) cases . If the person was literate , then he/she read and signed the consent form . All animals used in this study were handled by professional veterinary staff in strict accordance with good animal practice as defined by the Gambian Government/ITC code of practice for the care and use of animals for scientific purposes . All animal work were conducted with ethical approval from both the Joint Gambian Government/ MRC Ethics Committee and the Ethics Committees of the London School of Hygiene and Tropical Medicine , UK . Animal owners gave their written informed consent to examine and take rectal swabs from their animals and gave permission to publish questionnaire results from this study . Human NTS was obtained through active population-based case-control surveillance between December 2007 and February 2009 which was designed to determine the aetiology and epidemiology of enteric infections in Gambian children less than five years of age as part of the Gates Enteric Multicentre Study ( GEMS ) . The entire surveillance area ( figure 1 ) including all the compounds was mapped under GPS coordinates . This surveillance area represented a total population of 152 , 393 of which 27 , 567 are less than five years of age . Children under five years of age who presented with severe diarrhoea ( i . e . , diarrhoea with dehydration , dysentery , or requiring hospitalization ) within 3 days of onset of diarrhea were eligible to participate . For each enrolled child with diarrhoea , one healthy control child without diarrhoea was randomly selected from the community in which the case resided , matched to the case by age , gender , and time of presentation . After providing informed consent from the parent/guardian of each case or control a single , fresh , whole stool specimen was collected from cases and controls and cultured to detect bacteria species ( Aeromonas spp . , Campylobacter spp , Salmonella Typhi , NTS , Shigella spp , Vibrio spp , diarrheagenic E . coli strains ) , viral ( Rotavirus , Adenovirus , Astrovirus , Norovirus , Sapovirus , ) and protozoa ( Cryptosporidium spp Entamoeba histolytica , Giardia lamblia ) . During the surveillance period , 495 diarrhoea cases were identified and NTS were isolated from eight patients and six from community healthy controls . The 14 children were enrolled in this study . and traced back to their family compounds and five apparently healthy animals per species ( chicken , sheep and goat ) residing in the same household as the child were randomly enrolled within a week of isolating NTS from humans . Anal swabs were collected from 210 household contact animals and 21 NTS strains were isolated from the faeces . Stool specimens were transported in buffered glycerol saline ( BGS ) to the laboratory and processed within 6 hours of collection . Stools were plated on Xylose Lactose Desoxycholate ( XLD ) and MacConkey ( MAC ) agar and incubated at 36°C for 24 hours . Suspected non lactose fermenter colonies were subjected to biochemical reactions using Analytical Profile Index 20 Enteric ( API 20E ) according to manufactures' instructions ( BioMerieux SA , REF 20 100/20 160 ) . Serotyping was done by slide agglutination using Salmonella polyvalent and monovalent O and H antisera ( Diagnostic Pasteur , Paris , France ) according to the Kauffmann-White classification scheme [13] . Antimicrobial susceptibility tests were performed on Muller-Hinton agar ( Oxoid , USA ) using the agar diffusion method with the Bio-Rad discs ( Marne-La-Coquette , France ) according to the guidelines of the Antibiogram Committee of the French Society for Microbiology ( CA-SFM ) [14] . Strains were tested with 22 antimicrobial disks ( Bio-Rad ) : amoxicillin ( 25 mg ) , amoxicillin ( 20 mg ) plus clavulanic acid ( 10 mg ) , ticarcillin ( 75 mg ) , cephalotin ( 30 mg ) , cefoxitin ( 30 mg ) , cefotaxime ( 30 mg ) , ceftazidime ( 30 mg ) , tobramycin ( 10 mg ) , amikacin ( 30 mg ) , nalidixic acid ( 30 mg ) , pefloxacin ( 5 mg ) , norfloxacin ( 10 mg ) , trimethoprim ( 1 . 2 mg ) plus sulfamethoxazole ( 23 . 75 mg ) , tetracycline ( 30 mg ) , chloramphenicol ( 30 mg ) , gentamicin ( 10 mg ) , trimethoprim ( 300 mg ) , ciprofloxacin ( 5 mg ) , spectinomycin ( 100 mg ) , streptomycin ( 10 mg ) , sulfonamides ( 200 mg ) , and nitrofurantoin ( 300 mg ) . Diameters of the inhibition zones were measured with OSIRIS version 3 . 6xE2 . 0 ( Bio-Rad ) , and results were interpreted as susceptible , intermediate , or resistant according to the recommendations of the CA-SFM [14] . All these tests were carried out at the MRC microbiology laboratory which is enrolled in the external quality assurance programme of the United Kingdom National External Quality Assessment Scheme [15] . MLST was performed on the 35 Salmonella isolates as previously described [3] . The seven genes targeted were aroC , dnaN , hemD , hisD , purE , and thrA . Amplification of all genes was carried out in a 25 µl reaction mixture of the following items: 10xBuffer with 1 . 5 mm MgCl2 ( 2 . 5 µl ) ; 2 mM dNTP'S ( 0 . 5 µl ) ; 12 . 5 mM forward primer ( 1 µl ) ; 12 . 5 mM reverse primer ( 1 µl ) ; 5 U/µl Qiagen Hotstart Taq Polymerase ( 0 . 25 µl ) ; Template ( cell lysate ) ( 2 µl ) and 17 . 75 µl sterile DNA free water . PCR cycling conditions were as follows: 10 min at 94°C , followed by 32 cycles of 94°C for 1 min , 55°C for 1 min and 72°C for 1 min , and a final extension at 72°C for 5 min . 2 µl aliquots of PCR products were separated on 1% agarose gel electrophoresis , and visualized with ethidium bromide staining and UV illumination , and using a gel documentation system . PCR products were purified using Qiagen kit ( Qiagen ) . Sequencing was done on both strands with BigDye Terminator Cycle Sequencing kit ( Applied Biosystems , UK ) . The labelled fragments were separated by size using 3130xl Genetic Analyser ( Applied Biosystems , UK ) . Sequences were edited and complementary sense antisense fragments were aligned using the Laser Gene DNA star 7 . 1 software . Finally , the sequences were submitted to the MLST database website [16] and assigned to existing or novel allele or sequence type numbers defined by the database . Tests of association were done using Fisher's exact test in Stata 11 ( StataCorp . 2009 . Stata Statistical Software: Release 11 . College Station , TX: StataCorp LP ) . Stata provides one-sided p-values only for Fisher's exact test unless the table is 2×2 and results with p-values of less than 0 . 05 for the one-sided test were considered statistically significant . The parameters were grouped for the purpose of the analysis . The secondary diagnosis was categorized into three groups for the Fisher's exact test: group 1 , children co-diagnosed with another disease other than malaria; group 2 , children co-diagnosed with malaria and group 3 , children who were not co-diagnosed with any other disease . The age was also categorized into 2 groups: less than or equal to 18 months ( the average age ) , and more than 18 months . The mapping of the case locations was done using Arc Gis 9 . 3 software . To perform the cluster analysis of the serovars , MLST data were analysed with Bionumerics software ( version 4 . 0; Applied Maths , Sint-Martens-Latem , Belgium ) . Analysis using a hierarchic unweighed pair group method ( UPGMA ) with averaging was used to generate a dendrogram describing the relationship among Salmonella serovars ( figure 2 ) .
A diversity of serovars was found in both the human and animal population . None of the compounds showed similar serovars in both humans and animals ( table 1 ) . Nevertheless , one serovar namely S . Moualine was simultaneously found in a diarrheic child and in a chicken but in different compounds: C1 in Banico Allunhare and C8 in Koina ( table 1 , figure 1 ) . MLST revealed a single locus variant at sucA between those 2 strains ( table 1 ) . In compounds 9 ( C09 ) , 10 ( C10 ) , 11 ( C11 ) , 12 ( C12 ) , 13 ( C13 ) and 14 ( C14 ) , no Salmonella was isolated from animals ( Table 1 ) . The most prevalent serovars were S . Colindale in the human population ( 21 . 42% ) and S . Poona in the animal population ( 14 . 28% ) . The proportion of Salmonella isolated was higher in the chicken population than in other species: 66 . 6% , 28 . 6% and 4 . 8% in chickens , sheep and goats , respectively . In the same compound , a diversity of Salmonella serovars were circulating at animal level especially in chickens; the number varying from 2 to 3 different serovars: in compound 1 ( C1 ) , S . Moualine , S . Tornow and S . Poona; in compound 6 ( C6 ) , S . Schwarzengrund and S . Stanleyville and in compound 7 ( C7 ) , S . Offa , S . Give and S . Poona ( table 1 ) . The mean age of children enrolled in the study was ( to the nearest integer ) 18 months and the age varied between 9 and 26 months ( table 2 ) . All children who presented with diarrhoea except one were secondarily diagnosed with another disease such as malaria ( 4 children ) or other disease symptoms including fever , or cough ( 3 children ) ( table 2 ) . There was a significant association ( p-value<0 . 01 ) between expressing clinical signs of salmonellosis , i . e . diarrhoea and being co-diagnosed with a secondary disease ( table 3 ) . Age was not associated with the expression of clinical signs of salmonellosis ( p-value = 0 . 16 ) . All serovars were fully susceptible to all antibiotics tested except one of each the following serovars: Salmonella enteritica serovar Poona , Salmonella enteritica serovar Johannesburg , Salmonella enteritica serovar Chile and Salmonella enteritica serovar Colindale which were resistant to streptomycin . Four new alleles were discovered: hemD ( 152 ) , hemD ( 153 ) , hisD ( 256 ) and purE ( 226 ) and eighteen novel sequence types ( ST ) ( table 1 ) . Similar serovars exhibited the same allelic profiles , except S . Moualine which had two different allelic profiles for the serovars isolated from humans and animals ( table 1 ) . The seven housekeeping genes were concatenated for all isolates and the UPGMA tree was constructed ( figure 2 ) . All Salmonella genotypes had at least 80% similarity and the majority varied between 99% and 100% . Salmonella genotypes causing diarrhoea in children were always clustered with animal genotypes . Both Salmonella enteritica serovar Moualine isolated from a chicken and a child was clustered ( figure 2 ) .
The serovar diversity of NTS within the human and animal population was high showing that Salmonella is carried by both humans and animals in the community . The serovars were also widely geographically distributed in our surveillance area located in a typical African rural setting in The Gambia . We showed that several Salmonella serovars were circulating in the chicken population within the same compound or household; such as compounds 1 ( C1 ) , 6 ( C6 ) and 7 ( C7 ) respectively in the following 3 villages: Baniko Allunhare , Bagadagy and Misra Ba Mariama ( figure 1 ) . The higher proportion of Salmonella serovars in the chicken population compared to the goats and sheep is not surprising as it is known that chicken is the most important reservoir of NTS and thus thought to be the major source of transmission to humans [17] . Salmonella can persist in the chicken cecum or ovaries without triggering clinical signs in the host . Salmonellosis in young chickens may cause high mortality as a result of severe diarrhoea and dehydration , and creates a greater risk of evolving into a carrier state in the animals which survive [8] , [9] , [10] . The asymptomatic Salmonella carrier state in poultry has serious consequences for food safety and public health due to the risks of food poisoning following consumption of contaminated products . Small ruminants , such as sheep and goats , are also potential carriers of Salmonella [11] , [12] . In Ethiopia , a study indicated that Salmonella is common in apparently healthy slaughtered sheep and goats . It also showed the presence of a wide range of Salmonella serovars in sheep and goats , which are of veterinary and public health significance [18] . The high rate of NTS clones circulating in the same compound could lead to mixed infection or carriage within the chicken population . This situation could result in extensive genetic diversity and variability due to frequent intraspecific recombination as it occurs with Helicobacter pylori [19] . This could have as consequence a wider range of clones and thus more difficulties to control NTS infections at animal level . The diversity of serovars that we observed in this study is different from what we previously reported [3] where Salmonella enterica serovar Enteritidis was the most common serovar ( 80 . 6% ) followed by Salmonella enterica serovar Typhimurium ( 8 . 0% ) among NTS isolated from children with pneumonia and/or septicemia patients . It appears that the epidemiology of NTS is changing in The Gambia or the serovars detected might be site or disease specific , i . e . gastroenteritis vs . systemic infections . MLST provides the best phylogenetic-relationship inference for the Salmonella genus [20] . Therefore , it may be invaluable for determination of the relationship among various Salmonella strains and serovars [21] . As expected , the similarity matrix ( figure 2 ) of the serovars revealed close genetic relationship ( >80% and the majority between 99 to 100% ) between human and animal serovars , showing the genetic homogeneity of Salmonella . A study done in Senegal has also revealed a high degree of similarity among Salmonella enteritica serovar Brancaster and Salmonella enteritica serovar Enteritidis serovars from poultry and from humans by the use of PFGE techniques [22] , but direct evidence of Salmonella transmission from poultry to humans could not be provided . The high degree of similarity between human and animal serovars supports the theory that Salmonella clones are stable [23] . The genetic tree has also revealed that all lineages contained isolates of mixed origin ( human and animal ) . From the present data , there is therefore no indication of clonal groups or lineages that are adapted to any specific host . These findings support the conclusions of other authors who used the same techniques ( MLST ) with Salmonella from human and veterinary sources in Denmark [24] or a different technique like Pulsed Field Gel Electrophoresis ( PFGE ) with isolates from humans and animals , food or the environment in close contact with humans , which was the case in Kenya [25] . Like in the Kenya study [25] , we also showed that there was no relatedness between NTS genotypes from humans and those from animals in close contact to humans , this other potential sources of transmission such as environmental or the human-to-human transmission need to be examined . A statistical significant association ( P<0 . 05 ) was observed between children expressing clinical signs of salmonellosis ( diarrhoea ) and co-diagnosis with malaria ( table 3 ) . The association between salmonellosis and immunocompromising diseases is well known in the African context . NTS infections are usually associated with opportunistic infections especially in immuno-compromised patients , e . g . HIV-infected adults [26] , [27] or with other diseases like malaria or anemia [1] , [5] , [28] , [29] . Children especially those less than 3 years old are the age-group at risk of expressing clinical signs of salmonellosis [1] , [26] . The contradiction between our observation and those of most other authors is certainly due to the small sample size and the small range of age groups in our study . All individuals were in fact less than three years old . Malaria has long been suspected to increase the risk of invasive NTS infection and might contribute to the seasonality of NTS disease , although the mechanism underlying the association between malaria and NTS is only partially understood [6] . All serovars were susceptible to most commonly used antibiotics for the treatment of clinical infections in The Gambia such as amoxicillin , amoxicillin plus clavulanic acid , trimethoprim plus sulfamethoxazole , tetracycline , streptomycin and chloramphenicol and also to cephalosporins of the third generation which are considered as the drugs of choice for invasive Salmonella infections in humans . This result is in contrast to previous studies done in urban [1] , [2] and rural [3] areas in The Gambia where Salmonella strains expressed multi-resistance to several commonly used antibiotics . This could be explained in part by the fact that the NTS isolated from those studies were from invasive cases ( pneumonia and sepsis ) whereas this study focused on non-invasive cases ( diarrhea ) . In addition , in those studies Salmonella enterica serovar Enteritidis was the most common serovar followed by Salmonella enterica serovar Typhimurium . While these serovars were not detected in this study as a result of temporal trends of childhood NTS infection in The Gambia {Mackenzie , 2010 #33} . There is lack of resistance of serovars to antibiotics in rural areas even to those commonly used in the hospitals to treat bacterial infections . This is due to the fact that in rural areas , NTS infections are not treated because patients are often not conducted to the hospital due to poor access to medical centers , lack of transport facilities and of financial resources . Our findings suggest that these drugs remain suitable for the treatment of salmonellosis in humans and animals . However , we have to interpret these results with caution because the sample size in our study was small . Our study showed that the use of serotyping data combined with MLST and phylogenetic analysis can provide important information about the epidemiology of NTS in humans and animals . However , our results do not support the hypothesis that humans and animals in close contact in the same household carry genotypically similar Salmonella serovars . Nevertheless these findings have stirred up the problem of the transmission of NTS in African context and suggest that poultry may play an important part in the epidemiology of Salmonella infections of this condition . A better control of malaria may lead to a reduction in the incidence of invasive NTS disease in The Gambia . Multidrug resistance has not yet been a problem in human and animal NTS isolates in this area of the country . Thus , commonly available drugs may still be used for the treatment of NTS infections in rural Gambia . Nevertheless , public authorities must be alert to detect any change in the behavior of Salmonella towards antibiotics , with a view of establishing appropriate control measures for use of these drugs in humans and animals .
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Salmonellosis is a neglected tropical disease causing serious dysentery and septicaemia particularly in young infants , elderly and immunocompromised individuals such as HIV patients and associated with substantial mortality in developing countries . Salmonellosis also constitutes a major public health problem as it is considered the most widespread bacterial zoonosis of food origin throughout the world . Many epidemiological data exist from developed countries concerning transmission of Non-Typhoidal Salmonella ( NTS ) but few are available from developing countries . In addition few studies in sub-Saharan Africa have considered the interface between humans and their environment in relation to animals present in the household and food hygiene . This study describes the prevalence of NTS among fourteen Gambian children and 210 domestic animals living in close proximity ( household ) to the children in a rural setting in The Gambia . We found that the domestic animals living in the same household as patients carried different NTS serovar and genotypes; indicating that zoonotic transmission does not occur in our setting . This study provides baseline data for future studies of transmission of NTS in rural Africa .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"biology",
"veterinary",
"science",
"agriculture"
] |
2011
|
Clonal Differences between Non-Typhoidal Salmonella (NTS) Recovered from Children and Animals Living in Close Contact in The Gambia
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The mechanisms involved in the recognition of microbial pathogens and activation of the immune system have been extensively studied . However , the mechanisms involved in the recovery phase of an infection are incompletely characterized at both the cellular and physiological levels . Here , we establish a Caenorhabditis elegans-Salmonella enterica model of acute infection and antibiotic treatment for studying biological changes during the resolution phase of an infection . Using whole genome expression profiles of acutely infected animals , we found that genes that are markers of innate immunity are down-regulated upon recovery , while genes involved in xenobiotic detoxification , redox regulation , and cellular homeostasis are up-regulated . In silico analyses demonstrated that genes altered during recovery from infection were transcriptionally regulated by conserved transcription factors , including GATA/ELT-2 , FOXO/DAF-16 , and Nrf/SKN-1 . Finally , we found that recovery from an acute bacterial infection is dependent on ELT-2 activity .
The course of human bacterial infections is controlled by a combination of immune responses , physiological changes , and , if necessary , antibiotic treatment . To recover from an infection and return to homeostasis , the host must activate mechanisms capable of controlling the damage caused by pathogen virulence factors , inflammation , and a potentially toxic antibiotic exposure . If these alterations in host physiology are not handled appropriately , the host risks entering a state of reduced fitness . This reduced fitness manifests in the form of recurrent infections , inappropriate wound healing , autoimmune diseases , and chronic inflammatory disorders . While the mechanisms involved in the recognition of microbial pathogens as such and the subsequent activation of the immune system have been extensively studied , the pathways involved in host recovery after an infection remain understudied . To examine the biological changes that take place during the recovery phase of an acute bacterial infection , we decided to use the nematode Caenorhabditis elegans as a simple model host . Various human bacterial pathogens , including Pseudomonas aeruginosa , Salmonella enterica , Staphylococcus aureus , and Enterococcus faecalis , have been shown to colonize and kill C . elegans using conserved virulence mechanisms [1]–[4] . Moreover , C . elegans responds to infections using an inducible innate immune system that is controlled by several evolutionary conserved signaling cascades including the p38-MAPK ( PMK-1 ) , insulin-IGF ( DAF-16 ) , GATA ( ELT-2 ) , and TGF-B ( SMA-6 ) pathways [5]–[8] . It is therefore likely that investigating C . elegans recovery from bacterial infection would shed light on host responses that reestablish homeostasis post-infection . In this study , we established a C . elegans-S . enterica pathogenesis system as a model of acute infection by infecting nematodes with S . enterica and subsequently resolving the infection by treatment with the antibiotic Tetracycline . Using this acute infection model , we profiled gene expression changes in the host over the course of the infection and during the recovery phase of the infection . We found that during recovery , certain components of the host innate immune response were dampened , while mechanisms involved in xenobiotic detoxification , redox regulation , and cytoprotection were activated . A large number of the genes altered during recovery corresponded to intestinal genes regulated by ELT-2 , which is a conserved GATA transcription factor that plays a key role in the control of intestinal functions in C . elegans . Further studies indicated that the recovery from acute S . enterica infection required ELT-2 , indicating that ELT-2 controls not only induction of innate immune response genes but also genes that play a crucial role in the resolution of an infection .
Although host responses that limit microbial infection have been extensively studied , the mechanisms involved in the recovery phase of an infection are incompletely characterized at both the cellular and physiological level . We decided to use Caenorhabditis elegans as a simple model host for assessing biological changes during the recovery phase from an acute infection . C . elegans is propagated in the laboratory by feeding them E . coli strain OP50 . E . coli is effectively disrupted by the C . elegans pharyngeal grinder and essentially no intact bacterial cells can be found in the intestinal lumen of young , immunocompetent animals . However , pathogenic bacteria such as Salmonella enterica are capable of killing C . elegans by infectious processes that correlate with the accumulation of bacteria in the intestine . As in mammalian hosts , a small inoculum of S . enterica is capable of establishing a persistent infection in C . elegans that does not require constant exposure to bacteria and cannot be prevented by transferring the infected animals to plates containing E . coli [2] , [9] . To determine whether a long-lasting , chronic S . enterica infection could be easily reversed by antibiotic treatment to model a short , acute infection we used fer-1 ( b232ts ) animals , which are fertilization defective at the restrictive temperature . This prevents losing track of the initially infected animals in the morass of progeny that would be otherwise generated following an acute infection . We first established that transferring S . enterica-infected animals to plates containing the bacteriostatic antibiotic Tetracycline and seeded with TetR E . coli was sufficient to significantly reduce bacterial burden ( Figure S1 ) . Subsequently , we decided to use 50 µg/ml Tetracycline treatment to reduce S . enterica burden to model an acute infection in C . elegans . We monitored bacterial accumulation over the course of a 120 hour infection in synchronized larval stage 1 ( L1 ) fer-1 ( b232ts ) animals continuously grown on plates seeded with S . enterica-GFP or transferred to Tetracycline-containing plates seeded with TetR E . coli . Consistent with previous findings indicating that C . elegans larvae are highly resistant to pathogen-mediated killing and that death does not occur during the first several days of an S . enterica infection [9] , [10] , we observed that only 4 . 1% of the animals exposed to S . enterica-GFP starting at the L1 stage were colonized 72 hours later ( Figure 1A ) . In contrast , at 96 and 120 hours post-exposure , 41 . 9% and 71 . 4% of the animals were colonized by S . enterica-GFP ( Figure 1B–C ) . We found that transferring animals from S . enterica at 72 or 96 hours to Tetracycline-containing plates for 24 hours reduced bacterial burden ( Figure 1B–C ) . Quantification of the number of live bacteria in animals that were infected with S . enterica-GFP for 72 hours and treated with Tetracycline for 24 hours showed a significant reduction of bacterial burden ( Figure 1D ) , confirming that Tetracycline treatment can prevent S . enterica from persistently colonizing the C . elegans intestine and causing a chronic infection . Even though our results indicate that Tetracycline can prevent S . enterica from causing a persistent colonization of the C . elegans intestine , it was unclear whether acute pathogenic challenge would damage the animal and translate into an associated reduction in survival . As shown in Figure 1E , we found that the survival of animals infected with S . enterica and then treated with Tetracycline is significantly higher than that of animals continuously infected ( Figure 1E; yellow vs . red lines ) . Also , survival of infected and then Tetracycline-treated animals is nearly equivalent to animals that were never infected ( Figure 1E , yellow vs . black lines ) . Treatment with Tetracycline in the presence of killed bacteria only increased C . elegans mean lifespan from 14 . 2 to 14 . 9 days ( Figure S2 ) . Taken together , these studies show that an S . enterica infection can be resolved by treating the animals with Tetracycline and indicate that this type of treatment can be used to model an acute S . enterica infection that progresses towards chronicity if the animals were to remain untreated . To investigate cellular mechanisms potentially involved in recovery after an infection , we utilized Agilent C . elegans gene expression microarrays to identify changes in gene expression during infection and changes that take place after the infection is reversed by treatment with Tetracycline ( Figure 2A , Tables S1 and S2 ) . Initially , we focused our analysis on animals that were infected with S . enterica for 96 hours vs . animals that were infected for 72 hours and treated with Tetracycline for 24 hours to resolve the infection . At 96 hours , 99% of the animals were alive in both conditions ( Figure 1E , Day 1 ) . Overall , 243 genes , or approximately 1% of the C . elegans genome , were altered more than 2-fold ( p<0 . 05 ) when comparing the 96-hour cohorts . Of these altered genes , 126 were down-regulated and 117 were up-regulated ( Table S2 ) . To identify related gene groups that are transcriptionally controlled by pathways potentially involved in the changes that take place after infection , we performed an unbiased gene enrichment analysis using the database for annotation , visualization and integrated discovery ( DAVID , http://david . abcc . ncifcrf . gov/ ) [11] . The 10 gene ontology ( GO ) clusters with the highest DAVID enrichment score are shown in Figure 2B and Table S3 . For the subset of down-regulated genes that respond to the resolution of the infection , the 2 top-scoring GO clusters , c-type lectins and lysozyme groupings , have previously been described as part of an inducible C . elegans immune response to a variety of pathogens [12]–[15] . For the subset of up-regulated genes that respond to the resolution of the infection , 4 of the top 10 highest scoring ontology clusters are associated with xenobiotic detoxification , redox regulation , or cytoprotection [16] , [17] . These results indicate that the activation of the innate immune system of C . elegans by S . enterica infection is attenuated once the infection is resolved and that certain cellular homeostatic pathways are activated during recovery . Since only 42% of the animals exposed to S . enterica for 96 hours exhibited visible bacterial colonization ( Figure 1B ) , we decided to examine gene expression profiles of animals that were infected for 120 hours , which exhibited an even higher degree of bacterial colonization ( Figure 1C ) . A comparison of gene expression profiles from animals that were infected with S . enterica for 120 hours vs . animals that were infected for 96 hours and treated with Tetracycline for 24 hours identified 57 and 72 genes that are down- or up-regulated greater than 2-fold ( p<0 . 05 ) , respectively ( Table S2 ) . Analysis of GO terms in these gene sets via DAVID gives a shorter but similar list of enriched gene clusters ( Figure 2B and Table S3 ) , confirming that , as the infection resolves , marker genes of immune activation are down-regulated while genes that correspond to cellular homeostatic pathways are up-regulated . Moreover , the significant overlap between 96 and 120 hour treatment gene sets indicates that the changes that take place after an infection is resolved are reproducible and that similar transcriptional profiles are elicited at different times ( Figure 2C and Table S4 ) . The smaller number of genes down- and up-regulated by the resolution of the infection at 120 hours compared to 96 hours could be a consequence of the higher heterogeneity of S . enterica colonization in the 120-hour population ( comparison of Figures 1B and 1C ) . It is also possible that as the infection progresses , the animals suffer irreversible damage that makes them less responsive to antibiotic treatment . To validate the results of the microarrays , we performed quantitative real-time PCR ( qRT-PCR ) on a subset of the 243 genes that change upon Tetracycline treatment of infected animals . This subset includes 11 up-regulated genes and 6 down-regulated genes that were either present in a high scoring GO cluster , were highly misregulated , or both . We performed qRT-PCR on RNA harvested from C . elegans that were subjected to the same conditions as in the microarray studies . As shown in Figure 3A and 3B , the changes in gene expression as assessed by qRT-PCR were comparable to those observed by microarray profiling . Further analysis indicated that 16 of the 17 genes had statistically significant expression changes during Tetracycline-mediated recovery from S . enterica infection ( Figure 3A and 3B ) . Thus , the microarray data accurately reflects the majority of gene expression differences between treated and non-treated animals . Resolution of S . enterica infection by treatment with Tetracycline results in the down-regulation of genes that are markers of innate immunity and the up-regulation of genes that function in xenobiotic detoxification , redox regulation , and cytoprotection ( Figure 2B ) . While the resolution of the infection may be responsible for altering the expression of these genes , it is also possible that Tetracycline is directly inducing these changes . To distinguish between these two possibilities , we compared changes in gene expression due to Tetracycline alone vs . changes in gene expression due to recovery from infection by treatment with Tetracycline . We found that expression of 8 out of 16 tested genes were significantly different ( Figure 3C–D ) , highlighting the role of these 8 recovery genes in pathways that are altered during the resolution of the S . enterica infection . Considering that the genome wide microarray shows that 243 genes change their expression upon recovery at 96 hours , we estimate that approximately 122 genes are regulated by recovery from infection independently of Tetracycline while the remaining genes are regulated by the inclusion of Tetracycline alone . This suggests that Tetracycline may be directly inducing gene expression changes in the host that may help clear an infection independently of its antimicrobial activity . To confirm the finding that a subset of genes is altered upon resolution of an S . enterica infection independent of Tetracycline , we performed equivalent experiments using the antibiotic Kanamycin . These studies indicate that Kanamycin alone did not alter the expression of 9 tested genes in uninfected animals ( Figure S3A–B ) . Furthermore , 8 of the 9 alterations in gene expression seen in infected animals treated with Kanamycin are similar to those seen in infected animals treated with Tetracycline ( Figure S3C–D ) . To provide further insight into the behavior of genes altered during antibiotic-mediated recovery , we examined gene expression profiles over the course of the 96-hour infection ( Table S1 ) . We focused our analysis on qRT-PCR-confirmed genes that are known markers of immune activation and genes that correspond to cellular homeostatic pathways . The expression of innate immunity genes diminished significantly after the infection was resolved by Tetracycline treatment ( Figure 3C and E ) . In contrast , genes involved in regulating cellular homeostasis were significantly up-regulated upon recovery from infection ( Figure 3D and F ) . As a control , the expression of 3 select housekeeping genes remained relatively constant both during the course of the infection and during recovery ( Figure 3G ) . Overall , these studies suggest that as the infection resolves , cellular homeostatic mechanisms are activated while elements of the immune response are attenuated . Several of the GO clusters identified in the set of genes up-regulated during resolution of the infection correspond to genes whose products are involved in detoxification . We therefore hypothesized that the reduction of the pathogenic insult during the recovery phase of an infection may trigger processes involved in detoxification and clearance of immune effectors that , while necessary to combat pathogens , can have deleterious effects on the host . Recently , it was demonstrated that reactive oxygen species ( ROS ) , a component of the C . elegans immune response to S . enterica and other pathogens [18]–[20] , contributes to infectious pathogenicity ( i . e . , damage to the host ) . Thus , we decided to study gsto-1 , which is an up-regulated gene that encodes an omega-class glutathione S-transferase that protects C . elegans from oxidative stress under non-infected conditions [21] . We found that survival of gsto-1 ( RNAi ) animals infected with S . enterica and treated with Tetracycline was not significantly different from that of control animals ( Figure S4 ) . The lack of a significant effect by gsto-1 RNAi could be attributed to incomplete RNAi or to functional redundancy among the multitude of detoxification genes that are up-regulated during recovery ( Figure 2B and Table S3 ) . The gsto-1 locus is transcriptionally regulated by the GATA transcription factor ELT-2 [21] , leading us to consider a role for ELT-2 in controlling the expression of a set of genes required for resolution of an infection . Consequently , we applied several in silico approaches to determine whether ELT-2-regulated genes are present in the genes whose expression changes by the resolution of an infection . We compared the set of genes altered during recovery to previously identified ELT-2-regulated gene sets and to other control data sets . The ELT-2-regulated gene sets were among the 10 data sets with the strongest overlap with our recovery gene set ( Figure 4A and Table S5 ) . As ELT-2 regulates the expression of genes in the C . elegans intestine via trans-acting activity at TGATAA ( extended GATA ) cis-regulatory motifs [22] , [23] , we looked for the presence of TGATAA binding sites in the putative promoter regions of the down- and up- regulated genes . Approximately 63% of the 243 genes regulated by recovery contain at least 1 TGATAA site within the 1 . 5 kb sequence upstream of their transcriptional start site ( Figure 4B ) . By comparison , only 54% of genes in 3 randomly selected gene sets ( n = 243 each ) have at least 1 TGATAA sequence in the equivalent 1 . 5 kb region ( Figure 4B ) . Additionally , we observed that at least 1 TGATAA site is present in the putative promoter region of 7 of the 8 recovery genes verified by qRT-PCR ( Table S6 ) . While gsto-1 does not contain a TGATAA site in this 1 . 5 kb region , it does have a single site 3 . 8 kb upstream of the transcriptional start site . Moreover , it has been experimentally demonstrated that ELT-2 regulates the transcription of gsto-1 [21] . Consistent with the post-developmental role of ELT-2 in the regulation of adult intestinal functions [5] , [6] , [24]–[26] , another in silico approach showed that 17 out of 32 ( 53% ) recovery genes with at least 1 TGATAA site and for which the data is available are expressed in the intestine ( Table S7 ) . Only 1 out of these 32 genes is expressed in the hypodermis where other GATA transcription factors function [27] . This analysis also showed that 4 out of 8 recovery genes verified by qRT-PCR are expressed in the intestine ( Table S7 ) . Taken together , our in silico analyses leads to the hypothesis that ELT-2 controls the expression of a subset of genes during the recovery phase of an infection . To further substantiate a role for ELT-2 in the transcriptional regulation of genes during recovery , we studied the effect of elt-2 RNAi on the expression of recovery genes . As ELT-2 is essential for C . elegans larval development [22] , RNAi was performed on late larval stage 4 ( L4 ) animals . This approach has been used successfully to inhibit elt-2 expression for at least 6 days [28] , [29] . As shown in Figure 4D , RNAi of elt-2 inhibited the expression of the 5 studied genes that are up-regulated during recovery from the infection by treatment with either Tetracycline ( Figure 3D ) or Kanamycin ( Figure S3B ) . Inhibition of elt-2 by RNAi also further down-regulated the expression of ilys-3 and lys-9 ( Figure 4C ) . However , RNAi of elt-2 does not result in the unselective down-regulation of recovery genes as acdh-1 is not down-regulated ( Figure 4C ) . In addition , certain ELT-2-controlled immunity and structural genes [12] , [28] are not significantly altered during recovery from S . enterica infection ( Figure S5A–B ) . We further confirmed by qRT-PCR that transcript levels of clec-67 , which is a known marker of immunity controlled by ELT-2 [12] , are not altered upon recovery ( Figure S5C ) . We conclude that expression of a specific intestinal gene program during resolution of an infection is dependent upon the action of the GATA transcription factor ELT-2 . To test whether ELT-2 is required for recovery after infection , we studied the survival of elt-2 ( RNAi ) animals infected with S . enterica and treated with Tetracycline . RNAi inhibition of elt-2 starting at the L4 stage did not alter the survival of animals growing on live E . coli ( Figure 5A; black lines ) , nor did it alter survival in the presence of Tetracycline ( Figure S6 ) . This data indicates that L4 elt-2 ( RNAi ) animals are not sick merely due to disruptions in basal immunity or intestinal function . However , RNAi of elt-2 prevented the recovery of infected animals by treatment with Tetracycline ( Figure 5B; yellow lines ) , highlighting the role of ELT-2 during the recovery phase of the infection . In agreement with previously published reports that ELT-2 regulates innate immunity [12] , [28] , RNAi of elt-2 did significantly reduce survival of animals continuously infected with S . enterica ( Figure 5A; red lines ) . To address whether genes crucial for immunity are generally required for recovery , we studied pmk-1 , which encodes a p38 mitogen-activated protein kinase that is a major regulator of innate immunity in C . elegans [14] , [30] , [31] . Even though RNAi of pmk-1 elicited sensitivity to S . enterica-mediated killing ( Figure 5C; red lines ) , it did not prevent the recovery of infected animals by treatment with Tetracycline ( Figure 5D ) . Taken together , these results indicate that ELT-2 is required for both early immune responses against pathogens and responses that are activated upon recovery from an infection by S . enterica .
Using gene expression profiling , in silico analysis , and reverse genetic approaches , we have defined a novel post-developmental role for the GATA transcription factor ELT-2 during the resolution of an infection . ELT-2 was originally identified as a key regulator of C . elegans intestinal specification during development [22] . However , it is now becoming clear that ELT-2 has an extensive post-developmental role in the regulation of a plethora of adult intestinal functions . Under the control of ELT-2 , the 20 cells of the adult intestine in C . elegans function in nutrient uptake , synthesis and storage of macromolecules , epithelial immunity , and host-microbial communication [5] , [6] , [24]–[26] . There are several additional GATA transcription factors encoded in the C . elegans genome , including ELT-4 and ELT-7 , which regulate intestinal gene expression programs [32] . Further studies will be required to determine their possible contribution to the recovery process . Owing to the multi-functional nature of the intestine and due to the fact that ELT-2 regulates nearly all intestinal genes [33] , it is not surprising that half of the genes in the C . elegans genome have putative ELT-2 binding sites ( Figure 4B ) . Thus , it is logical to conclude that the specification of different functional outputs that takes place in the C . elegans intestine during the complete course of an infection is controlled by additional co-factors that act together with ELT-2 . Recent work has demonstrated that GATA transcription factors , including ELT-2 , act coordinately with the insulin-IGF pathway transcriptional regulator DAF-16 in a cell-autonomous manner to regulate lifespan extension in C . elegans [34] . It is therefore plausible that DAF-16 acts with ELT-2 to co-regulate genes important for infection resolution . Indeed , we observed a significant enrichment of both ELT-2- and DAF-16-controlled targets in our set of genes altered during recovery from infection ( Figure 4A and Table S5 ) . An emerging theme is that coordinated transcriptional activity of DAF-16 and ELT-2 would be necessary for the modulation of cytoprotective pathways that , in turn , are required for a majority of cellular stress response pathways [16] . Another candidate factor that may regulate damage response genes in conjunction with ELT-2 and/or DAF-16 is the Nrf1/SKN-1 transcription factor . Previous work has demonstrated that signaling cascades downstream of reactive oxygen species ( ROS ) induce a cytoprotective SKN-1 pathway [35] , [36] . SKN-1 might regulate cytoprotective genes downstream of or in parallel to ELT-2 and/or DAF-16 to mediate resolution of an infection . Indeed , SKN-1-positively regulated targets are significantly enriched in the set of genes that are up-regulated during infection resolution ( Table S5 ) . Mounting evidence indicates that ELT-2 activity is modulated under a variety of environmental conditions or physiological states . ELT-2-mediated immunity to a variety of pathogens is activated by currently unknown mechanisms . Interestingly , a paper by Lee and colleagues demonstrates that the intestinal pathogen B . pseudomallei can actively target and degrade ELT-2 to prevent host immune responses [29] . We failed to observe any alterations in ELT-2 protein localization or abundance caused by S . enterica infection or recovery . These pathogens , which kill C . elegans at a distinctly different rate , must utilize different mechanisms to overcome the host immune system . Signals during the initial decline in infection may function to reprogram the transcriptional activity of ELT-2 from an innate immune program to a cytoprotective one . These unidentified signals may be bacterial- and/or host-derived . Specific bacterial-derived signals , such as those involved in biofilm formation or quorum sensing , may be the primary trigger for the ELT-2 switch [26] . These bacterial-derived signals might act directly on ELT-2 or they may transit through host-encoded genes . Alternatively , host-encoded regulators that normally function during development , such as the END-1/END-3 specification factors , might be re-activated during the resolution of infection to direct the transcription of detoxification genes by ELT-2 . Interestingly , the END-1/END-3 system lies downstream of the oxidative stress ( ROS ) response protein SKN-1 in the development of the C . elegans intestine [24] . Alternatively , changes in ELT-2 activity may be controlled by local chromatin remodeling in a manner similar to the regulation of DAF-16 transcriptional activity [37] . In summary , our results identified a new , key role for ELT-2 during recovery from a bacterial infection . We revealed that during recovery from an infection , genes that are markers of innate immunity are down-regulated , while the expression of genes involved in xenobiotic detoxification , redox regulation , and cytoprotection is enhanced . Interestingly , a number of genes encoding antibacterial factors ( ABFs ) are up-regulated during the course of the S . enterica infection ( Table S1 ) . However , the expression of abf genes is not down-regulated once the infection is resolved . This could be due to a mechanism used by C . elegans to maintain high levels of abf genes throughout reproductive adulthood . It is also possible that ABFs have a high specificity for damaging prokaryotic cells , having little or no impact on host cells . Thus , there would be no immediate need to reverse their up-regulation once the infection is resolved , unlike the case of lysozyme-encoding genes , which could potentially damage host cells . The ELT-2 interaction with the aforementioned co-factors may dictate the specificity of the expression profile during the different phases of an infection . A number of microbial killing pathways and cellular homeostatic pathways are controlled by the nervous system in infected C . elegans [38]–[46] . An important question that remains to be evaluated is whether the nervous system also plays a role in the control of the mechanisms involved in recovery after infections have been cleared .
C . elegans strain HH142 fer-1 ( b232ts ) was provided by the Caenorhabditis Genetics Center . C . elegans were maintained at 15°C on NGM—OP50 plates without antibiotics . The following bacterial strains were used for experiments: Escherichia coli strain OP50-1 [SmR] [47] , E . coli—dsRed strain OP50 [AmpR , CbR] [47] , E . coli strain HT115 [TetR] [48] , E . coli strain HT115 pL4440 [AmpR , TetR] [48] , E . coli strain DH5α pSMC21 [KanR] [49] , Salmonella enterica enterica serovar Typhimurium strain 1344 [SmR] [50] , S . enterica—GFP strain SM022 [SmR , KanR] [51] . Bacteria were grown overnight for 14 hours in 3 ml LB broth at 37°C . fer-1 ( b232ts ) animals were synchronized by treating gravid adults with sodium hydroxide and bleach . About 2 , 000 synchronized L1 animals were grown on full lawn S . enterica—GFP plates at 25°C for 36 , 72 , 96 , or 120 hours . At designated transfer time points , animals were rinsed off S . enterica—GFP plates , washed with M9 ( 4 changes ×15 minutes ) , concentrated , and then transferred to plates with or without 50 µg/ml Tetracycline that were seeded with E . coli HT115 or S . enterica-GFP . At designated visualization time points , animals were picked to an NGM—OP50 plate for 1 hour before being picked to a new NGM—OP50 plate . Animals were then visualized at 20× using a Leica MZ FLIII fluorescence stereomicroscope . In heavily colonized animals ( heavy ) GFP fluorescence was visible in the presence of halogen white light set at 60% , while in weakly infected animals ( weak ) GFP fluorescence was only visible in the absence of white light . Animals where GFP fluorescence was not detected even in the absence of white light were scored as not infected ( none ) . For the quantification of colony forming units ( CFUs ) , fer-1 ( b232ts ) animals were synchronized by treating gravid adults with sodium hydroxide and bleach . About 2 , 000 synchronized L1 animals were grown on full lawn S . enterica—GFP plates at 25°C for 72 hours . At the designated transfer time points , animals were rinsed off S . enterica—GFP plates , washed with M9 ( 4 changes ×15 minutes ) , concentrated , and then transferred to S . enterica-GFP or E . coli plus 50 µg/ml Tetracycline plates . At designated CFU time points , animals were picked to 3 NGM—OP50 plates ( 20 minutes each ) before being picked to a 1 . 5 ml eppendorf tube with 50 µl of PBS plus 0 . 1% Triton-X-100 . A total of 10 animals per condition were mechanically disrupted using a mini-pestle . Serial dilutions of the lysates were spread onto LB/Kanamycin ( 50 µg/ml ) plates to select for S . enterica—GFP cells and grown for 24 hours at 37°C . Bacteria – E . coli HT115 or S . enterica were grown overnight for 14 hours in 3 ml LB broth at 37°C . A total of 50 µl ( scoring ) or 500 µl ( exposure ) of the resulting cultures were spread onto modified ( 0 . 35% peptone ) NGM plates with or without 50 µg/ml Tetracycline and allowed to grow for 1–2 days at 25°C to produce a thick lawn . fer-1 ( b232ts ) animals were synchronized by treating gravid adults with sodium hydroxide and bleach . Synchronized L1 animals were grown on full lawn S . enterica—GFP plates at 25°C for 72 hours before being transferred to the appropriate ( treatment or not ) plates . The assays were performed at 25°C . Animals were scored every day and were considered dead when they failed to respond to touch . Animals were transferred to fresh plates every other day for the entire length of the experiment . Survival was plotted using Kaplan-Meier survival curves and analyzed by the logrank test using GraphPad Prism ( GraphPad Software , Inc . , San Diego , CA ) . Survival curves resulting in p values of <0 . 05 were considered significantly different . A total of 60 animals per condition per experiment were used . E . coli was grown as described above . A 50- µl drop of the bacteria was plated on a 3 . 5-cm plate of modified NGM agar containing 40 µg/ml fluoro-deoxyuridine with or without 50 µg/ml Tetracycline . A total of 100 animals per condition per experiment were used . The assays were performed at 25°C . Survival curves were analyzed as described above . gsto-1 ( RNAi ) . E . coli HT115 ( DE3 ) bacterial strains expressing double-stranded RNA [48] were grown for 9 hours in 5 ml LB broth containing Ampicillin ( 50 µg/ml ) at 37°C . The resulting cultures were seeded onto NGM plates containing Carbenicillin ( 50 µg/ml ) and isopropyl-1-thio-β-D-galactopyranoside ( 3 mM ) . dsRNA-expressing bacteria were allowed to grow for 2 days at 25°C to produce a thick lawn . fer-1 ( b232ts ) L4 animals were placed on RNAi or vector control plates for 5 days at 15°C until F1 animals developed . fer-1 ( b232ts ) F1 L4 animals were placed on a second RNAi or vector control plate and incubated for another 5 days at 15°C until adult F2 animals developed . Gravid F2 RNAi animals were picked to full lawn E . coli or S . enterica—GFP plates and allowed to lay eggs for 3 hours at 25°C to synchronize a third generation population . These third generation animals were kept on E . coli or S . enterica—GFP plates for 72 hours before being transferred to plates with or without 50 µg/ml Tetracycline that were seeded with E . coli or S . enterica-GFP . unc-22 ( RNAi ) was used as positive control in all experiments to account for RNAi efficiency . The gsto-1 ( mv_C29E4 . 7 ) RNAi vector was verified by DNA sequencing . A total of 60 animals per condition per experiment were scored for survival . elt-2 ( RNAi ) and pmk-1 ( RNAi ) . Production of RNAi plates was the same as described above . Gravid fer-1 ( b232ts ) animals were allowed to lay eggs for 3 hours at 25°C on NGM-HT115 plates . Gravid animals were removed and the eggs/plates were incubated for 36 hours at 25°C . Synchronized L4 animals were then transferred to RNAi or vector control plates and incubated for an additional 36 hours at 25°C . Young adult RNAi or vector control animals were then transferred to and grown on full lawn E . coli or S . enterica—GFP plates for 36 hours at 25°C . Adult worms were then transferred to plates with or without 50 µg/ml Tetracycline that were seeded with E . coli or S . enterica-GFP . unc-22 ( RNAi ) was used as positive control in all experiments to account for RNAi efficiency . The elt-2 ( mv_AAC36130 ) and pmk-1 ( sjj_B0218 . 3 ) RNAi vectors were verified by DNA sequencing . A total of 60 animals per condition per experiment were scored for survival . fer-1 ( b232ts ) . Animals were synchronized by treating gravid adults with sodium hydroxide and bleach . Synchronized L1 animals were grown on full lawn E . coli or S . enterica—GFP plates for 72 hours . At 72 hours , animals were rinsed off E . coli or S . enterica—GFP plates , washed with M9 ( 4 changes ×15 minutes ) , concentrated , and then transferred to E . coli , E . coli plus 50 µg/ml Tetracycline , E . coli plus 50 µg/ml Kanamycin , or S . enterica—GFP plates . At 24 hours post-transfer , animals were rinsed off plates , washed with M9 ( 4 changes ×15 minutes ) , and flash-frozen in Trizol ( Life Technologies , Carlsbad , CA ) . Total RNA was extracted using the RNeasy Plus Universal Kit ( Qiagen , Netherlands ) . elt-2 ( RNAi ) ; fer-1 ( b232ts ) . Animals were synchronized by treating gravid adults with sodium hydroxide and bleach . Synchronized L1 animals were grown on full lawn E . coli plates for 36 hours . At 36 hours , animals were rinsed off E . coli , washed with M9 ( 4 changes ×15 minutes ) , concentrated , and then placed on RNAi or vector control plates for 36 hours . At 72 hours , animals were rinsed off these plates washed with M9 ( 4 changes ×15 minutes ) , concentrated , and then placed on S . enterica—GFP plates for 36 hours . At 108 hours , animals were rinsed off S . enterica—GFP plates , washed with M9 ( 4 changes ×15 minutes ) , concentrated , and then transferred to E . coli , E . coli plus 50 µg/ml Tetracycline , or S . enterica—GFP plates for 24 hours . Animals were rinsed off plates , washed with M9 ( 4 changes ×15 minutes ) , and flash-frozen in Trizol ( Life Technologies , Carlsbad , CA ) . Total RNA was extracted using the RNeasy Plus Universal Kit ( Qiagen , Netherlands ) . All studies were performed at 25°C . Total RNA was obtained as described above . A total of 1 µg total RNA was oligo ( dT ) primed and reverse transcribed in a 50 µl volume using the SuperScript III First-Strand Synthesis System ( Life Technologies , Carlsbad , CA ) . Reactions without the addition of reverse transcriptase ( RT ) were also performed and served as controls for contaminating genomic DNA in quantitative PCR experiments . Two µl of the resulting plus or minus RT reactions served as templates in quantitative PCR experiments using Power SYBR Green PCR Master Mix ( Life Technologies , Carlsbad , CA ) and the StepOnePlus Real-Time PCR System ( Life Technologies , Carlsbad , CA ) . For each sample , 3 technical replicates were performed . Pan-actin-normalized Ct values were determined using the StepOnePlus Software ( Life Technologies , Carlsbad , CA ) . Primer sequences are available upon request . When applicable a one or two variable t-test was performed . fer-1 ( b232ts ) animals were synchronized by treating gravid adults with sodium hydroxide and bleach . Synchronized L1 animals were grown on full lawn E . coli OP50 ( uninfected ) or full lawn S . enterica plates at 25°C for 36 , 72 , 96 , or 120 hours . At designated transfer time points , animals were rinsed off S . enterica plates , washed with M9 ( 4 changes ×15 minutes ) , concentrated , and then transferred to S . enterica or E . coli plus 50 µg/ml Tetracycline plates . At designated harvesting time points , animals were rinsed off plates , washed with M9 ( 4 changes ×15 minutes ) , and flash-frozen in Trizol ( Life Technologies , Carlsbad , CA ) . Total RNA was extracted using the RNeasy Plus Universal Kit ( Qiagen , Netherlands ) . Total RNA was assessed for quality with an Agilent 2100 Bioanalyzer G2939A ( Agilent Technologies , Santa Clara , CA ) and a Nanodrop 8000 spectrophotomer ( Thermo , Wilmington , DE ) . 100 ng of total RNA was converted to 1 . 65 µg Cy-3-labeled , linearly amplified cRNA using the Low Input Quick Amp ( LIQA ) Labeling One-Color Microarray-Based Gene Expression Analysis Kit ( Agilent Technologies , Santa Clara , CA ) . cRNA was fragmented and added to 44 K feature Agilent C . elegans Gene Expression Microarray V2 slides ( Agilent Technologies , Santa Clara , CA ) . Hybridization was performed in the Agilent rotisserie Hybridization Oven for 17 hours . Arrays were subsequently washed and scanned with the Agilent B scanner according to standard Agilent protocols ( Agilent Technologies , Santa Clara , CA ) . Scanned data was log2 transformed and quantile normalized using Partek Genomics Suite ( St . Louis , MO ) . Analysis of variance ( ANOVA ) t tests and fold-change calculations were also performed using Partek Genomics Suite ( St . Louis , MO ) . For each of the 5 time points , 2 biological replicates were assessed . The microarray data was deposited in the Gene Expression Omnibus database: GSE54212 . Gene lists were culled from the literature and passed through WormBase Converter [52] using the WS220 genome release as the output ( references are noted in Table S5 ) . A total of 20 , 834 WS220 genes are represented by 1 or more probes in the Agilent C . elegans V2 array ( Agilent Technologies , Santa Clara , CA ) . Gene ontology analysis was performed using the DAVID Bioinformatics Database ( david . abcc . ncifcrf . gov/ ) . The most significant gene ontology term in each DAVID functional annotation cluster was set as the significance of the overall cluster . Statistical significance of the overlap between two gene sets was calculated using the following on-line program: nemates . org/MA/progs/overlap_stats . html . Representation Factor represents the number of overlapping genes divided by the expected number of overlapping genes drawn from 2 independent groups . A background gene list of 20 , 834 was used for the calculation . P values were calculated using the hypergeometric probability . 1 . 5 kb cis-regulatory sequences were identified using WormMart ( wormbase . org ) . Expression patterns were determined using WormMine ( wormbase . org ) . Detailed bioinformatics protocols are available upon request .
|
Infections by bacterial pathogens often produce substantial tissue damage and alter metabolism in the host that , if left unchecked , could be detrimental to overall fitness . The cellular and systemic responses that resolve these alterations in the host are not well defined . Here , we examine transcriptional networks in an animal host that are modulated during the resolution phase of an intestinal infection treated with an antibiotic . Up-regulation of genes involved in detoxification and cellular homeostasis during the resolution phase is controlled by the conserved endodermal GATA transcription factor ELT-2 . GATA transcription factors are known to be involved in the development , differentiation , and function of a diverse array of metazoan tissue types . Therefore , our results ascribe a new role to GATA transcription factors in recovery from an acute infection . Fully characterizing the host response during resolution of an infection will lead to a better understanding of human health concerns related to recurrent infections , wound healing , autoimmune diseases , and chronic inflammatory disorders .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"animal",
"models",
"immune",
"system",
"invertebrates",
"caenorhabditis",
"elegans",
"infectious",
"disease",
"immunology",
"model",
"organisms",
"innate",
"immune",
"system",
"clinical",
"immunology",
"immunity",
"caenorhabditis",
"nematoda",
"biology",
"and",
"life",
"sciences",
"immunology",
"animals",
"organisms",
"research",
"and",
"analysis",
"methods"
] |
2014
|
Recovery from an Acute Infection in C. elegans Requires the GATA Transcription Factor ELT-2
|
Ephrins and Eph receptors are involved in the establishment of vertebrate tissue boundaries . The complexity of the system is puzzling , however in many instances , tissues express multiple ephrins and Ephs on both sides of the boundary , a situation that should in principle cause repulsion between cells within each tissue . Although co-expression of ephrins and Eph receptors is widespread in embryonic tissues , neurons , and cancer cells , it is still unresolved how the respective signals are integrated into a coherent output . We present a simple explanation for the confinement of repulsion to the tissue interface: Using the dorsal ectoderm–mesoderm boundary of the Xenopus embryo as a model , we identify selective functional interactions between ephrin–Eph pairs that are expressed in partial complementary patterns . The combined repulsive signals add up to be strongest across the boundary , where they reach sufficient intensity to trigger cell detachments . The process can be largely explained using a simple model based exclusively on relative ephrin and Eph concentrations and binding affinities . We generalize these findings for the ventral ectoderm–mesoderm boundary and the notochord boundary , both of which appear to function on the same principles . These results provide a paradigm for how developmental systems may integrate multiple cues to generate discrete local outcomes .
In vertebrates , ephrins and Eph receptors have emerged as major players in the formation of cleft-like tissue boundaries . They control segmentation of rhombomeres [1] and somites [2] , [3] and the separation of embryonic germ layers [4]–[6] . Ephrins as well as Eph receptors are divided into A and B subclasses , based on their structural and binding characteristics . They are considered to bind promiscuously within each subclass , ephrinAs with EphAs and ephrinBs with EphBs [7] , with the exceptions of EphA4 , which can interact with both ephrinAs and Bs , and EphB2 , which can bind ephrinA5 [8]–[10] . Classically , a single ephrin–Eph pair is expressed in a complementary pattern in adjacent tissues . However , in many physiological situations , each cell type may express multiple ephrins and Eph receptors [11] , [12] . To explain the restriction of signaling to the tissue boundary , one must assume that these molecules interact in more selective ways . Consistently , in vitro studies have yielded a wide range of binding affinities between various ephrins and Eph receptors , suggesting a substantial degree of specificity , but the biological significance of these differences has not been clearly established [11] , [13] , [14] . Moreover , the presence of ephrins and Ephs in the same cell introduces a whole additional layer of complexity involving effects such as ephrin–Eph cis-interactions [15] , [16] as well as potential cross-talks between the downstream signaling events [10] , [17] . Understanding how the global output is determined under in vivo conditions has thus remained a daunting challenge . An example of where the integration of multiple co-expressed Eph receptors and ephrins can be tested is the ectoderm/mesoderm boundary in the early Xenopus embryo . We have demonstrated that ephrins and Ephs act directly at the tissue interface , where they generate cycles of attachments and detachments through transient activation of Rho GTPases [4] . This mechanism based on cell contact-mediated repulsion is highly reminiscent of neuronal contact guidance and utilizes the same molecular cues [18] . We showed that full separation required antiparallel forward signaling across the boundary such that ephrins in the mesoderm stimulate Ephs in the ectoderm and vice versa [4] . This observation was quite puzzling , as ephrin and Eph should in principle interact equally between cells within each tissue , which should cause repulsion and eventually lead to tissue dissociation . We ask here how cell repulsion is restricted to sites of contacts between the two tissues .
To address the issue of repulsion restriction , we conducted a comprehensive characterization of the ephrin–Eph system in the early gastrula . We first compared quantitatively the transcripts of all ephrins and Ephs in the dorsal ectoderm and in the mesoderm . Both tissues expressed multiple ephrins and Ephs . Although both A and B types are involved at the ectoderm–mesoderm boundary ( [5] and unpublished data ) , the contribution of the A type appears less important , as they are not sufficient on their own to induce separation ( [4] and unpublished data ) . We thus focused on the ephrin B subfamily and their receptors , which showed predominantly asymmetric patterns ( Figure S1B ) . Thus , ephrinB2 and EphA4 were strongly enriched in the mesoderm , whereas the ectoderm selectively accumulated ephrinB3 and EphB2–4 . We next wanted to test whether asymmetric expression is functionally relevant for tissue separation . Interestingly , ephrinB2 , ephrinB3 , and EphA4 start to be expressed just at the onset of gastrulation ( Figure S1A ) , coinciding with the appearance of the ectoderm–mesoderm boundary and the onset of separation behavior [19] . We thus focused on ephrinB2 , ephrinB3 , EphA4 , EphB2 , and EphB4 as strongly asymmetrically distributed molecules and included in our analysis ephrinB1 as an example of an evenly expressed ligand ( Figure 1C ) . Although ephrin/Eph depletion severely disrupts the endogenous ectoderm–mesoderm boundary ( [4] and Figure 1A ) , the embryonic phenotype is difficult to interpret , due to multiple functions of ephrins and Ephs in various aspects of gastrulation [5] , [20]–[22] ( Winklbauer unpublished ) . We thus performed most of our study on a reconstituted boundary produced by apposition of ectoderm and mesoderm explants ( Figure 1B , B′ ) , an assay that allows an in-depth dissection of tissue separation [4] , [19] , [23] . By systematic depletions using antisense morpholino oligonucleotides ( MOs ) ( Figure S2A , D ) , we established that each ephrin and each Eph is required , either in the ectoderm , in the mesoderm , or in both . Their depletion inhibited tissue separation to a degree that generally correlated with their relative tissue enrichment ( Figures S1B and S2A ) . Interference with ephrins or with Ephs on one side of the boundary , by single or multiple depletions ( Figure S2A ) or dominant negative constructs [4] , led to a maximal reduction of separation to 30–40% , whereas simultaneous interference on both sides of the boundary led to a significantly stronger inhibition ( Figure S2A ) , consistent with a requirement for two antiparallel forward signals [4] . After having established that each ephrin and Eph subtype is required , we asked next whether a given subtype could be replaced by another member of the family . An ephrin or Eph was depleted , and rescue was attempted by mRNA injection ( Figure S2B ) , or by direct activation at the boundary through incubation with soluble preclustered ephrin or Eph extracellular domains ( Figure 1D ) . Results from both kinds of rescues were in perfect agreement . Subtypes typically failed to substitute for each other , as observed for ephrinB1 and B3 , or for EphA4 and B4 . The only exception was ephrinB2 , which could substitute for ephrinB3 ( Figure 1D ) , a result that will be explained in later experiments . The apparent lack of rescue was not due to a lower activity of a particular construct or Fc-fragment , as rescue of the same subtype was in all cases efficient . This was confirmed by comparing two different amounts of ephrinB1 and B3 mRNAs ( Figure S2B ) and verifying their expression levels ( Figure S2C ) . The lower amount was sufficient to rescue depletion of the same subtype , but depletion of the other subtype could only be marginally rescued with the highest amount . These results demonstrated an unexpectedly tight , although not absolute , specificity of the requirements for each ephrin and Eph . Note that the ability of the EphA4-Fc fragment to rescue loss of EphA4 also uncovered a contribution from reverse signaling to tissue separation . The specific requirement for all ephrinB subtypes and their receptors could be due to specific differences in downstream signaling and hence to different roles during tissue separation . Alternatively , signaling could be uniform and additive , but depend on specific receptor–ligand interactions . To determine whether Eph receptor specificity resided in their cytoplasmic tail or in their extracellular domains , we constructed EphA4/B4 chimeras , where the respective cytoplasmic domains were swapped , which we tested for the ability to substitute for endogenous EphA4 or EphB4 proteins . We verified that these chimeric constructs were properly expressed at the cell surface ( Figure S3A ) and activated by ephrin-Fc fragments ( Figure S3B ) . The functional assays gave clear-cut results ( Figure 1E ) : The AB chimeric construct , which contained the extracellular domain of EphA4 and the intracellular domain of EphB4 , could perfectly rescue the loss of EphA4 , but was unable to substitute for EphB4 . Reciprocally , the BA construct could rescue EphB4 but not EphA4 depletion . These results showed that the extracellular domains were responsible for the Eph specificity . The cytoplasmic domains appeared interchangeable , suggesting that the requirement for each of these Eph receptors was probably not due to differences in signaling but rather reflected the ability to bind selected ligands . We postulated that specific combinations of receptor–ligand pairs could underlie the restriction of repulsion to the boundary . If indeed specific ephrin–Eph pairs formed preferentially at the boundary , we predicted that ectopic addition of ephrins normally enriched in the mesoderm should induce artificial separation of two ectoderm explants . This prediction was fully verified . Indeed , ectoderm explants could be induced to repel each other by treatment with soluble Fc fragments for mesoderm-enriched ephrinB2 but not ephrinB1 ( Figure 2A ) . Similarly , the separation of two mesoderm explants was efficiently induced by incubation with ephrinB3 , which is normally expressed only in the ectoderm , but not by ephrinB2 ( Figure 2A′ ) . Ectoderm–ectoderm separation could also be induced by “mesodermal” EphA4-Fc , but not by EphB4-Fc ( Figure 2A″ and A″′ ) . The sensitivity of ectoderm and mesoderm to respond to a subset of ephrin/Eph fragments implied that each of these two tissues must specifically express respective partners . We used the induction of ectopic separation as a functional assay to identify these endogenous receptors . Explants depleted of single ephrins or Ephs were tested for their ability to separate upon incubation with an Fc fragment . Among potential candidate receptors for ephrinB2 in the ectoderm , EphB4 depletion strongly inhibited ephrinB2-Fc–induced separation , whereas EphB2 depletion had a weaker effect ( Figure 2A ) . The ability of ephrinB3 to induce mesoderm separation was entirely dependent on the presence of EphA4 ( Figure 2A′ ) . Finally , of all the three ephrin ligands expressed in the ectoderm , ephrinB3 was clearly the one responsible for EphA4-Fc–induced separation ( Figure 2A″ ) . Although some of these results were consistent with the relative mRNA enrichments of the various ligands/receptors ( Figure S1B ) , others clearly implied functional selectivity . For instance , in EphA4-induced ectoderm–ectoderm separation , an explanation based only on relative expression levels and under promiscuous binding could explain the minimal role of ephrinB2 , which is scarce in the ectoderm , compared to abundantly expressed ephrinB3 . However , this explanation would predict a much stronger effect of ephrinB1 depletion , as the latter is present at significant levels . Thus , the best explanation was that ephrinB3 and EphA4 tended to interact preferentially . We directly assessed functional selectivity at the level of Eph activation ( Figure 2B ) . After treatment of ectoderm explants with equal concentrations of ephrinB1 , B2 , or B3 Fc fragments , each Eph receptor was immunoprecipitated , and the levels of phosphorylation were monitored by Western blot using an anti–phospho-tyrosine antibody [11] . Because of poor immunoprecipitation of the endogenous EphA4 protein with available antibodies , EphA4-YFP was ectopically expressed and immunoprecipitated with an anti-GFP antibody . In these experiments , cells were stimulated for 30 min , a time that should be sufficient to reach a bona fide steady state in terms of repulsive behavior , as separation can be induced already after a few minutes [4] . The results were clear cut: EphA4 was highly phosphorylated in response to ephrinB2 or ephrinB3 but not ephrinB1 . EphB2 responded strongly to ephrinB2 and weakly to ephrinB1 . EphB4 was activated by ephrinB2 but neither ephrinB1 nor ephrinB3 . These results correlate well with ephrin–Eph affinities measured in vitro [13] , with the notable exception of ephrinB1–EphB2 , for which the affinity was reported to be similar to ephrinB2–EphB2 [24] . Combined with the asymmetric distribution of the various molecules , the differences in binding largely explained our functional data . For instance , the failure of EphB4 to induce ectoderm separation was due to the low levels of ephrinB2 in this tissue , which is its only strong interactor . On the other hand , the ectoderm could respond to EphA4 via ephrinB3 , but not ephrinB1 . Taken together , our results show that each Eph receptor is selective for one or at most two ephrins . These results support the hypothesis that tissue separation is driven by asymmetric expression of specific receptor–ligand pairs . Most of the ephrin/Eph pairs identified functionally show indeed partially complementary expression ( Figure 2C and Figure S1B ) . This is clearly the case for ectodermal ephrinB3 and mesodermaly enriched EphA4 . Likewise , ectodermaly enriched EphB2 and EphB4 interact best with mesodermaly enriched ephrinB2 . However , not all factors are expressed in simple complementary patterns . In particular , EphA4 interacts not only with ectodermal ephrinB3 but equally well with ephrinB2 , which is abundant in the mesoderm . In contrast , ephrinB1 , evenly expressed in both tissues , can only weakly activate EphB2 , which is enriched in the ectoderm . Note that ephrinB1 depletion in either tissue had a rather strong phenotype ( Figure 1D and Figure 2A ) considering that it seemed to only interact weakly with EphB2 . This may be indicative of the occurrence of an additional receptor for ephrinB1 yet to be identified ( see also supplementary discussion in Text S1 ) . Our results suggested that tissue separation may be simply explained by the complementary expression of selective ephrin/Eph pairs , which would generate an excess of repulsive signal at the boundary ( Figure 3A ) . Nevertheless , it is conceivable that particular ephrins and Ephs would also be specifically needed on one or the other side of the boundary . We examined this possibility for the ephrinB3–EphA4 pair , which is closest to a fully complementary expression pattern . We depleted ephrinB3 in the ectoderm and EphA4 in the mesoderm and asked whether separation could be rescued by ectopic re-expression of these two molecules in the opposite tissues . The results were unambiguous: Swapping ephrinB3 and EphA4 efficiently restored separation ( Figure 3A′ ) . The same result was obtained when ephrinB3 was provided as a soluble ligand to the surface of the ectoderm ( Figure 3B′ ) . Thus , the presence of ephrinB3 and EphA4 , respectively , in the ectoderm and the mesoderm is not required for separation; the complementary expression appears sufficient . We tested the specificity of the process by attempting the rescue with other ephrins or Ephs . EphrinB2 , which is another good partner for EphA4 , could substitute for ephrinB3 ( Figure 3A′ , B′ ) . No rescue was observed with ephrinB1 , which only activates EphB2 , nor with EphB4 , which cannot function as an ephrinB3 receptor ( Figure 3A″ , B′ ) . Although EphA4 and B4 cytoplasmic tails appear interchangeable ( Figure 1E ) , we also know that this tail is required for Eph function in separation [4] . We thus asked whether the kinase activity was involved by using kinase dead ( KD ) variants , where the ATP binding site was mutated [25] , [26] . We found that both EphA4KD and EphB4KD failed to rescue depletion of the corresponding endogenous receptors ( Figure S4A ) . On the contrary , they acted as dominant negatives , decreasing levels of receptor phosphorylation ( Figure S4D ) and inhibiting separation ( Figure S4A ) to similar levels as MO depletion or expression of cytoplasmic truncated forms ( Figure S2 and [4] ) . This inhibition was fully rescued by co-expression of wild-type EphA4/B4 ( Figure S4A ) . We further tested the effect of these KD mutants in two other situations—that is , ( a ) in ectopic separation of two ectoderm , or of two mesoderm explants by soluble ephrinB2 , respectively , B3 Fc fragments ( as in Figure 2A ) , or ( b ) in rescues of ephrinB2/B3 depletions by the corresponding Fc fragments ( as in Figure 1D ) . In both types of experiments , expression of the EphA4KD blocked the action of ephrinB3 Fc , and EphB4KD that of ephrinB2 ( Figure S4C ) . Thus , these KD forms behaved in all cases as strong dominant-negative mutants , demonstrating that their kinase activity is essential for ectoderm–mesoderm separation . Immunostaining of sections from wild-type gastrulae with an antibody recognizing a conserved phosphorylated site present in all EphBs demonstrated that Eph signaling was indeed activated in both ectoderm and mesoderm , but was significantly stronger at the boundary ( Figure 4A , A′ ) . A similarly increased signal was observed with an anti–phospho-EphA antibody ( Figure 4A′ ) . We confirmed biochemically the existence of basal signaling in the tissues and enhanced activity at ectoderm–mesoderm contacts . Such contacts were maximized by mixing dissociated ectoderm and mesoderm cells to produce heterogeneous aggregates ( Figure 4B ) . The levels of phosphorylated EphAs and EphBs in extracts of mixed aggregates were compared with those of pure ectoderm and mesoderm aggregates . Mixed aggregates showed higher p-EphA and p-EphB signals than combined homogenous ectoderm and mesoderm aggregates . We further showed that EphA phosphorylation in these ectoderm–mesoderm aggregates required ephrinB3 , but not ephrinB1 , further confirming the specificity of EphA4 ( Figure 2D′ ) . Thus , high local Eph activation is consistent with cell repulsion being restricted to the boundary , due to the preferential interactions between complementary pairs of ephrins and Ephs enriched on opposite sides of the boundary . The typical mechanical output of Ephrin/Eph signaling in repulsive behavior involves myosin-based contraction [27] , [28] . We determined the distribution of phosphorylated myosin light chain ( p-MLC ) in the ectoderm and mesoderm tissues and at their interface ( Figure 4C , C″ ) . We observed p-MLC at cell–cell contacts within each tissue ( arrowheads ) . The signal was significantly stronger in the ectoderm than in the mesoderm , but by far the most intense signal was consistently found in patches along the boundary ( arrows ) , as expected from strong bursts of ephrin/Eph signaling at this interface . EphA4/EphB4 depletion strongly decreased p-MLC staining at the boundary and in the mesoderm , but not in the ectoderm ( Figure 4C′ , C″ ) . These results confirm that the boundary is a site of significantly high contractile activity that largely depends on Eph signaling . Significant Ephrin–Eph signaling also takes place within each of the two tissues ( Figure 3A , B ) . To account for the fact that the tissues remained coherent and overt cell detachments occurred only at the interface , we considered the role of cell–cell adhesion . We proposed that cohesion or separation is determined by a balance between cadherin adhesion and Eph signaling-dependent contractility . Although cadherin levels are lower in the most anterior mesendoderm ( [29]–[31] and unpublished data ) , they are quite similar between the ectoderm and the mesoderm analyzed in our experiments ( Figure S5B ) , implying that the balance would mostly depend on the strength of Eph signaling . This signaling would take place at all cell contacts , but only at the boundary would the signal be sufficiently intense to overcome adhesion , thus causing cell detachment . This model predicted that increasing cadherin levels or decreasing contractility should inhibit detachments between ectoderm and mesoderm cells , while on the contrary decreasing cell adhesion or increasing Eph signaling should lead to visible detachments between mesoderm cells . This hypothesis was consistent with the observation that ectoderm–mesoderm separation was impaired upon cadherin overexpression , which could be rescued by boosting Eph signaling across the tissue interface by incubation with soluble ephrin Fc fragments ( Figure S5A ) . Note that Eph depletion did not significantly affect cadherin levels ( Figure S5B ) , arguing against a direct regulation of cadherins by ephrin–Eph signaling . To explore the behavior at individual homotypic and heterotypic cell–cell contacts , we juxtaposed single cells obtained by dissociation of early gastrula tissues ( Figure 5A–I and Movies S1–S7 ) . Our dissociation conditions fully preserved both the capacity of cells of the same tissue to rapidly re-establish stable adhesions ( Figure 5D–G ) and the ability for contacts between ectoderm and mesoderm cells to reproduce the alternating cycles of attachment/detachment characteristic of the separation behavior ( Figure 5A–B and Movie S1 ) . We first tested the effect of increasing cadherin levels on the normal repulsion between ectoderm and mesoderm cells . We also tested the effect of incubating the cells with the myosin inhibitor blebbistatin . Both conditions severely reduced the frequency of detachments ( Figure 5B , C , H and Movies S2 and S3 ) , consistent with the inhibition of tissue separation observed under the same manipulations ( Figure S5A ) . Because we expected mild repulsion between mesoderm cells , we predicted that detachments should become more prominent if adhesion would be experimentally decreased . We subjected mesoderm cells to a mild cadherin depletion ( ∼30% , not shown ) and observed that contacts were now much less stable and cells displayed the typical repulsive behavior normally observed at contacts with ectoderm cells ( Figure 5E , H and Movies S4 and S5 ) . These retractions were entirely dependent on intact ephrin/Eph signaling ( Figure 5F , H and Movie S6 ) . We also predicted that mesoderm cells could become repellent even with normal cadherin levels if repulsive signals would be increased . We chose to express ephrinB3 and EphB4 , because they are normally enriched in the ectoderm , and are the respective specific partners for mesoderm-enriched EphA4 and ephrinB2 ( Figure 2C ) . EphrinB3/EphB4-expressing mesoderm cells readily showed strong repulsion ( Figure 5G , H and Movie S7 ) . This balance between repulsion forces and cell–cell adhesion was also observed at the tissue level in reaggregation assays ( Figure S5C–E ) , in which diminished cohesion induced by cadherin depletion was rescued by co-depletion of EphB4 , whereas ectopic expression of ephrinB3 and EphB4 decreased cohesion of cells with wild-type cadherin levels . Our results suggested that separation could be explained by the asymmetric expression of a subset of specific ephrin–Eph pairs , which resulted in widespread ephrin–Eph signaling , but with an altogether higher output across the boundary . Although the concept is intuitively coherent , the actual system is complex , with many ephrins and Ephs , including pairs that could interact extensively within one tissue ( e . g . , ephrinB2–EphA4 in the mesoderm ) . To better estimate the validity of this model , we simulated it by computing the contribution of all the various ephrin–Eph interactions established at homotypic contacts within the tissues and heterotypic contacts between tissues ( Figure S7 ) . We considered a minimal model taking into account two parameters: ( a ) the affinities between the extracellular domains of ephrins and Ephs and ( b ) their relative expression in the different tissues ( Text S1 and Figure S7 ) . Affinities for the various ephrin–Eph pairs have been measured in vitro [13] . These values remain imprecise , but were globally consistent with our data , with few exceptions ( e . g . , ephrinB1–EphB2; see Text S1 ) . Endogenous levels for the different ephrin and Eph proteins could not be measured directly , due to lack of adequate antibodies , but were approximated based on relative mRNA levels determined by real-time PCR ( Figure S1B ) and on a global estimate obtained indirectly ( see Text S1 and Figure S6 ) . Note that these “apparent” affinities and concentrations are here purely operational terms . The actual system is hugely complex , constituted of multiple ligands and receptors , all of them membrane proteins , capable of spontaneous or ligand-induced clustering , and potentially also of cis-interactions [15] , [32]–[37] . Most of these additional parameters and their impact on ephrin–Eph function are still ill-defined , and a formal description of such a system is a largely unresolved problem . However , this simple model where receptor activation depends on its own concentration , the concentrations of its various potential ligands , and the corresponding affinities turned out to describe with surprising accuracy the situation at this boundary . The signal output turned out indeed to be clearly highest at the tissue boundary compared to ectoderm–ectoderm and mesoderm–mesoderm contacts ( Figure S7D ) . The system appeared very robust , predicting a highest signal at the boundary over a broad range of ephrin–Eph apparent concentrations and of apparent KDs ( Figure S7D ) . Quite strikingly , the result of the simulation closely resembled the relative levels of phosphorylated receptors in our immunofluorescence images ( Figure S7D and E , reproduced from Figure 4A ) . The model was further tested for the ability to predict the outcome of loss- and gain-of-function experiments ( Figure S7F–J ) . The simulation recapitulated very well the major characteristics of the system . In particular , the specific induction of “separation” between ectoderm cells by activation with mesoderm-enriched ephrinB2 or EphA4 , the inhibition of normal separation upon ephrin or Eph depletions ( Figure S7G , H ) , and most importantly the fact that rescue could not be obtained indifferently by any ephrin ( or Eph ) ( Figure S7H ) argued against widespread ligand–receptor promiscuity . It also accounted for our original observation that strong inhibition of separation required interfering with receptors on both sides of the boundary ( Figure S7J ) ( see [4] and Figure S2A ) . We conclude that a system of partially complementary and semiselective ephrin–Eph pairs is indeed sufficient to explain ectoderm–mesoderm separation . The robustness of the simulation provides strong support for the general principles of this model . Although multiple additional factors are likely to impact on the apparent concentrations and affinities , on the response curves of Eph activation , and more downstream on the contractile activity of boundary cells , such factors may not affect significantly the final global pattern . The reason for this robustness appears to be the very limited set of pairs that can effectively interact and thus influence the system , and the fact that three out of five of these pairs are asymmetrically expressed ( ephrinB3–EphA4 , ephrinB2–EphB2 , and ephrinB2–B4 ) . The simulation indicated that , for these five pairs , differences up to 10-fold in apparent affinities would change the relative strength of the outputs , but the signal remained strongest at the boundary under most conditions . The few aspects of our experimental data that were not well simulated are discussed in Text S1 . Discrepancies were certainly expected considering the many additional mechanisms that can modulate ephrin–Eph signaling . Nevertheless , the predictions of the simulation were surprisingly good given the simplicity of the assumptions . We conclude that a combination of multiple semiselective pairs is sufficient to explain a large part of the system's behavior . We wondered whether the principles found for the early ectoderm–mesoderm boundary would similarly apply to other boundaries . Ephrin and Eph expression is indeed widespread in the gastrula embryo , and two other important boundaries form during this phase . A ventral boundary appears at midgastrula to separate the ectoderm for the ventral mesoderm ( Figure 6B ) , which is known to have properties different from those of dorsal axial mesoderm . At the end of gastrulation , the dorsal mesoderm is partitioned into the axial notochord and lateral paraxial mesoderm ( prospective somitic mesoderm ) ( Figure 6C ) . In the latter case , we recently showed that ephrins and Ephs were indeed involved in separation [38] . We decided to systematically analyze the patterns of ephrin/Eph expression in these two regions ( Figure S1B ) . We found that the same set of ephrins and Ephs was expressed in those regions . The expression patterns revealed common themes as well as some significant differences ( summarized in the diagrams of Figure 6 ) . In the case of the ventral boundary , the ephrinB3–EphA4 complementarity and the mesoderm enrichment of ephrinB2 were preserved . However , EphB4 , which was enriched in the ectoderm on the dorsal side , was now homogenously expressed , whereas on the contrary ephrinB1 , equal on both sides of the dorsal boundary , was here strongly asymmetric , now fully complementary to its receptor EphB2 . In the late gastrula , the two new structures emerging from the dorsal mesoderm showed very interesting expression patterns: The notochord had preserved strong EphA4 and ephrinB2 expression , which were the major characteristics of the early mesoderm . The paraxial mesoderm , however , had dramatically modified its ephrin/Eph expression , acquiring typical features of ectoderm—that is , low EphA4 and high EphB4 . Note that the complementarity was strong for Eph receptors , but rather mild for the ephrins . However , a similar trend was observed , where the notochord remained more “mesoderm-like” ( higher ephrinB2 , lower ephinB3 ) , and the paraxial mesoderm was now more “ectoderm-like” ( lower ephrinB2 , higher ephinB3 ) . These observations suggested that tissues that became separated by boundaries expressed some sort of “modules” characterized in particular in all cases by complementary ephrinB3–EphA4 expression , but had some flexibility for other components . We then tested the role of key players , at both boundaries . In the case of the ventral boundary , we used the classical separation assay , attempting both loss- and gain-of-function experiments ( Figure S8 ) . All the results were in perfect agreement with the expression patterns and with our model . Similar to the dorsal boundary , depletion of either the ectoderm-enriched ephrinB3 or of its mesoderm partner EphA4 inhibited separation . In addition , ephrinB1 depletion in the mesoderm , which had only a weak effect on the dorsal side , affected here separation much more drastically ( Figure S8B ) . Depletion in the ectoderm of its receptor EphB2 similarly inhibited separation . In gain-of-function experiments , both mesoderm-enriched ephrinB1 and B2 could induce robust separation between two ectoderm explants when added as soluble Fc fragments ( Figure S8C ) , an effect that could not be obtained with ephrinB1 on the dorsal side ( Figure 2A ) . We used this phenotype to identify the functional partner of ephrinB1 on the ectoderm surface . We found that EphB2 , but not EphB4 , depletion significantly inhibited separation , functionally validating our biochemical results ( Figure 2B ) . The role of ephrins and Ephs on the notochord boundary was examined by targeted depletion in a restricted region of the embryo [38] . The effect on the integrity of the boundary was examined on sections of whole embryos ( Figure 7 ) . We tested depletion of the two strongly asymmetric Eph receptors , A4 and B4 , and of their ligands ephrinB2 and B3 . We observed in each case strong disruption of the boundary . However , each ephrin/Eph MO disrupted the boundary only when targeted to the expressing tissue: ephrinB3 MO and EphB4 MO in the paraxial mesoderm , EphA4 MO in the notochord . EphrinB2 MO was the only one that had an effect on both sides . These phenotypes were perfectly consistent with the distribution and partial specificity of these molecules . For the receptors , which were strongly asymmetrically expressed , MO injection obviously had an effect only when targeted to the tissue where they were enriched . For the ligands , the results were explained by their specificity . Indeed , the only receptor for ephrinB3 is EphA4 , enriched in the notochord; thus , despite the poor complementarity of ephrinB3 distribution , its depletion only impaired separation when targeted to the opposite tissue ( paraxial mesoderm ) ( Figure 7D , E , L ) . EphrinB2 , however , can stimulate both EphA4 and EphB4 and was thus predicted to significantly contribute to generate repulsion at both sides of the boundary , explaining the widespread effect of its depletion ( Figure 7L ) . We further verified that this functional selectivity was purely due to extracellular interactions: We used the EphA4 and B4 chimeras to rescue disruption of the notochord boundary upon EphA4 or EphB4 depletion . In both cases , rescue was only achieved by expression of the chimeric construct that harbored the proper extracellular domain ( Figure 7F–K , M ) .
This study reconciles several previous puzzling observations and provides a coherent description of boundary formation based on selective repulsion between two cell populations . In particular , it explains how in tissues with widespread expression of multiple ephrin ligands and Eph receptors , cell–cell repulsion due to receptor–ligand interaction can be restricted to tissue boundaries . Situations where ephrins and Ephs are co-expressed were expected to be dauntingly complex , considering the many possible cross-regulations proposed to occur and the potentially distinct pathways that each member of these families may stimulate . However , we found that the essence of the early boundaries can be represented by a surprisingly simple model where the final output can be largely predicted based on the relative selectivity of the extracellular interactions and the abundance of the various components . It is important to emphasize that the system cannot be simply reduced to the sum of binary inputs of individual specific pairs expressed in opposite tissues , but must rather be viewed as an integrated network made of semiselective pairs . Most of the molecules have more than one partner ( ephrinB2 interacts will all receptors ) , and expression patterns range from equal distribution on both sides of the boundary to strong enrichment in one tissue , with intermediate partial asymmetries being most frequent . These characteristics of the network explain well its reaction to experimental manipulations: Although each component was required to a degree that generally corresponded to its expression levels and distribution , the specificity of the requirement was not absolute , as it could in some cases be substituted by another subtype that shared the same partner ( ephrinB2 and ephrinB3 with EphA4; Figure 1D ) . In other words , the role of each of the ephrins and of the Eph receptors is dictated by the possibility to establish an interaction with a partner across the boundary . Note that some weaker degree of rescue was also achieved by components that did not interact with the same partner , as in the cases of ephrinB1 and ephrinB3 ( Figure S2B ) . In those cases , overexpression could apparently boost signaling through a different ephrin–Eph pair , although this did not efficiently compensate for the loss of the original signal ( Figure S2B ) . These partial compensations are fully expected in this type of network . Arguably the most definitive validation of the model came from the ephrinB3–EphA4 swapping experiment ( Figure 3 ) , which demonstrated that separation did not depend on the presence of a particular ephrin or Eph in the ectoderm or the mesoderm , but on the ability of selective pairs to establish interactions across the boundary . Having said that the system is an integrated network , it is equally important to highlight the fact that only a subset of potential ephrin–Eph combinations ( five out of nine ) can establish interactions of functional significance ( Figure 7 ) . This subset of functional pairs is identical to the one determined as capable of in vitro binding [13] . Thus , our data represent a solid in vivo validation of the notion of partial ephrin–Eph specificity of ligand-receptor interactions . As discussed in Text S1 , the fact that ephrins and Ephs are all expressed at similar levels , that the affinities of the five functional pairs are all in the same range , and that the impact of varying these affinities is predicted to be limited allow us to summarize the system in a simple model , where all functional pairs may be considered as producing equivalent contributions . The output appears then largely imposed by the partially complementary patterns of several of these pairs ( Figure 7 ) . Altogether , there data represent to our knowledge the most extensive dissection of network made of multiple ligands and receptors . Its success relied on the extensive use of combinations of loss- and gain-of function experiments comparing in parallel different ephrins and Ephs . Concerns often arise when attempting to evaluate the ability of one member of a gene family to substitute for another , due to the difficulty to control for the activity of the reagents . In this context , the strength of our experimental system resides in the high coherence of the systematic comparison of multiple conditions , including cross-rescues , where each reagent was validated in at least one of the complementary conditions ( e . g . , Figure 1D , E , and Figure S2B ) . The comparison of three morphologically similar , cleft-like boundaries that form during Xenopus gastrulation and depend on Eph–ephrin signaling allowed us to extract recurrent regulatory motives in the form of complementary receptor–ligand pairs: ephrinB3—EphA4 , ephrinB1–EphB2 , and ephrinB2–EphB4 ( Figure 7 ) . For each boundary , at least two of these three pairs show complementary expression patterns . Moreover , we had shown previously that complete ectoderm–mesoderm boundary formation requires antiparallel ligand–receptor signaling [4] . We observed now that at all three boundaries studied here , the respective complementary pairs were arranged in an antiparallel pattern ( e . g . , ephrinB3–EphA4 from ectoderm to mesoderm , ephrinB2–EphB2 from mesoderm to ectoderm ) . Of the complementary pairs , ephrinB3–EphA4 is most systematically exploited in the different contexts , suggesting that it may be specialized in tissue separation . Altogether , the logic of ephrin/Eph-dependent repulsion regulation at tissue interfaces can be reconstructed as follows . For the complete cleft-like separation , a minimum of two antiparallel receptor–ligand pairs is required . If both receptors and both ligands were completely specific , signaling would be completely restricted to the boundary , as the ligands and receptors co-expressed in each tissue would interact only with partners in the adjacent tissue . If interactions were completely promiscuous , signaling intensity within each tissue were similar to that across the boundary , leading to the disintegration of the whole array . If interactions were relatively specific , favoring signaling between complementarily expressed pairs , repulsion could be restricted to the boundary by a threshold mechanism where full cell–cell detachment occurs only above a certain level of signaling intensity . In this situation , additional expression of receptors and ligands , with additional strong or weak interactions , would be compatible with proper boundary formation as long as signaling remained below the threshold within each tissue , and exceeded it at the tissue interface . This third scenario is the one that we encountered in the embryo . Such a dynamic yet robust system appears perfectly suited for the complex morphogenesis of vertebrate embryos . For example , they allow combining multiple functions of ephrin/Eph signaling , in the same tissues ( [39] , [40] and Winklbauer unpublished ) . The rules identified in this study provide a coherent logic to tissue separation . For instance , one now understands how separation between dorsal ectoderm and mesoderm is established at the onset of gastrulation [19] . The system builds on the EphB receptors , which are already expressed before gastrulation ( Figure S1A ) . Their enrichment in the prospective ectoderm is the typical default distribution for maternal components . This intrinsic bias in expression becomes functionally relevant once the mesoderm starts expressing ephrinB2 , the strongest ligand for EphB receptors , initiating a forward signal into the ectoderm . The simultaneous expression of ephrin B3 in the ectoderm and its receptor EphA4 in the mesoderm provides the antiparallel forward signal into the mesoderm , completing the requirement for full tissue separation at this boundary . In subsequent stages , this pattern is progressively modified . Complementary expression of ephrinB3 and EphA4 is still involved in all cases , but other components , including those which were maternally expressed , are now tightly regulated and change their expression pattern . This is the case for ephrinB1 , which , while ubiquitously expressed and hence neutral during dorsal ectoderm–mesoderm boundary formation , becomes mesoderm-enriched to play a prominent role on the ventral side . Other pairs , such as ephrinB2–EphA4 , should interact extensively within a tissue , presumably providing tissue-specific functions . Are other boundaries controlled by a similar integrated network ? Information of ephrin/Eph expression patterns in other systems is incomplete and not quantitative , but somites and most rhombomeres express more than one ephrin and/or Eph [1] , [3] , in patterns that are consistent with the basic principles described in our study: For instance , ephrinB3 and EphA4 are expressed in complementary patterns in several rhombomeres and are never enriched on the same side of the boundary . EphrinB2 , on the contrary , is found both opposite to as well as on the same side as its receptors [3] , [14] , [41] . These findings reveal that the surface of embryonic cells is endowed with a rich array of receptors that upon direct contact with neighboring cells can establish very specific interactions . We show how such systems , by integrating the signals generated by all the combinations of high- and low-affinity interactions , can produce clear-cut decisions at tissue interfaces and at the same time tolerate a good degree of within-tissue signaling . The key to this behavior of the ephrin/Eph system is the balance of adhesion and repulsion and its regulated breakdown at a preset threshold for repulsion . Although this model can largely explain tissue separation , our results do not exclude that particular ephrins or Ephs may play important additional roles . They may allow for the fine-tuning of the various signals , both at the boundary and within the tissues . EphrinB2–EphA4 constitutes an example of a pair that , at all stages , interacts extensively within a tissue and may provide tissue-specific functions . One such additional layer of regulation that remains to be investigated is suggested by our reaggregation assays , which showed that ephrinB2 or EphB4 depletion decreased cohesion of the ectoderm [4] , indicating that at least under some circumstances these molecules behave as “pro-adhesive” in the ectoderm , while they are repulsive in the mesoderm and across the boundary . We also provide here an important distinction between activities that are intrinsic to each tissue and reactions that occur specifically at the boundary . Our observation that global levels of myosin activation are much higher in the ectoderm is fully consistent with its well-known stiffness and much lower capacity for spreading on adhesive substrates and for migration [19] , [29] , [31] , [42] , [43] . However , we also detect a second p-MLC pool , which , unlike the former , is Eph-dependent , and highly concentrated at the boundary ( Figure 3C ) . Despite a large difference in “basal” myosin activity , the two tissues cannot remain separated in the absence of Eph signaling . Previous hypotheses based on differential adhesion/tension fail to accurately describe this situation [29] , [44] , [45] . Our observations are , however , in full agreement with our model of “selective repulsion” controlled by potent and highly localized ephrin–Eph reactions , dominating at specific cell–cell contacts over the adhesive and tensile tissue properties . Adhesion ( and cortical tension ) does participate in the global equation by setting the general properties of the tissues , and we show here that separation results from the balance between adhesion and ephrin-mediated repulsion . The network of Ephrin signaling is thus set at the appropriate level to overcome adhesion along the boundary , without jeopardizing cohesion within the tissues . Experimental manipulation of cadherin levels can disrupt this balance , at least in the situation of the early ectoderm and mesoderm ( Figure 5 and Figure S5 ) . The notochord boundary is , however , an example where separation is remarkably robust , resisting to rather strong changes in cadherin levels [38] , [46] , [47] . How adhesion , basal tension , and ephrin signaling are differently set at difference boundaries to confer their specific characteristics will be an important question to investigate . To explain his original discovery of cell sorting , Holtfreter had hypothesized that various cell types harbor different surface cues that he called “affinities . ” The combinatorial network of ephrins and Ephs here unraveled provides a molecular basis to this concept . Ephrins and Ephs and other similar cell–cell contact-dependent cues are expressed in a wide variety of tissues , both in the embryo and in the adult , and we predict that the principles uncovered in the Xenopus gastrula may apply to a vast spectrum of cellular processes and account for the ability of cell types to distinguish between “self” and “nonself” and thus organize into multicellular structures .
All animal studies were approved by the McGill University Animal Care Committee ( permit 4869 “Cellular mechanisms of embryonic boundary formation” ) and the University of Toronto Animal Care Committee ( permit 20010074 “Analysis of gastrulation movements in Xenopus” ) . Affinity-purified antibodies specific for EphB4 and EphB2 were generous gifts from Dr . Elena Pasquale ( Burnham Medical Institute , [48] ) . Anti–phospho-EphB4 , anti–phospho-EphA4 , and anti-EphA4 antibodies were gifts from Dr . Greenberg [49] . Mouse anti-EphA4 was purchased from BD , pan anti-mouse ephrinB antibody from Zymed , mouse anti-alpha tubulin from Cell Signalling , mouse anti-GAPDH from ABi , mouse and rabbit anti-GFP from Invitrogen , affinity-purified polyclonal goat EphB4 from R&D , and mouse anti–phospho-tyrosine PY-20 HRP-conjugated from Santa Cruz . Recombinant mouse ephrinB2-Fc , ephrinB1-Fc , human ephrinB3 , and mouse EphB4-Fc and EphA4-Fc ( R&D Systems ) comprising the extracellular domain of ephrin/Eph fused to C-terminal 6× histidin-tagged Fc region of human IgG were preclustered by 1 h incubation with anti-human Fc antibody ( Jackson ImmunoResearch Laboratories ) at a 1∶2 ratio in MBSH [4] before application . Anti-human Fc IgGs alone were used as control . Membrane-targeted GFP and Cherry and Xenopus ephrinB1 and ephrinB2 were described previously [4] . Xenopus EphB4 and ephrinB3 in pCS2+ were gifts from Dr . A . Brändli [50] . Xenopus EphA4 ( Pagliaccio ) in pBluescript KS by DR Bob Winning [51] was a gift from Dr . T . Sargent . Chicken EphA4-GFP in peGFPN2 was a gift from Dr . A . Kania [52] . EphA4 was subcloned into the pCS2+and C-terminally fused to eYFP . Mouse EphA4 Y928F KD mutant in pCS2+ was a gift from Dr . Ira Daar [25] . Xenopus EphB4 KD mutant was constructed by substituting arginine to lysine at position 645 ( ATP-binding ) using the QuickChange site-directed mutagenesis kit . Alk4* , a constitutively active Activin receptor , was a gift from Dr . J . Smith [53] . β-catenin in pSP36T was a gift from Drs . P . McCrea and B . M . Gumbiner [54] . To construct Eph chimeras with swapped cytoplasmic domains , a restriction site was introduced at the end of the transmembrane domain in each original receptor ( Nhe1 for EphB4 and Spe1 for EphA4 ) by site-directed mutagenesis ( Quick Change XII , Stratagene ) . The two resulting mutants , called EphA4* and EphB4* , had , respectively , a change from bp1926 tgtcat to actagt and from bp 1763 ggtggt to gctagc corresponding to a V to L and V to A substitution corresponding to the last hydrophobic amino acid of the transmembrane domain amino acid 558–559 of Xenopus EphB4 and EphA4 . EphA4* was subcloned into pCS2+ for consistency with EphB4 . EphA4* and EphB4* in pCS2+ rescued loss of the corresponding endogenous Ephs with the same efficiency as the original A4 and B4 constructs ( Figure 1E ) . The newly introduced Spe1 and Nhe1 sites were used to cut and exchange the fragments corresponding to the sequences of the cytoplasmic tails , yielding EphA4B4 ( extracellular and transmembrane domains of A4 and cytoplasmic tail of B4 ) and EphB4A4 ( extracellular and transmembrane domains of B4 and cytoplasmic tail of A4 ) . mRNAs were synthesized according to the manufacturer's instructions ( mMessage mMachine kit , Ambion ) . MOs and mRNAs were injected animally in the two blastomeres of two-cell stage embryos to target the ectoderm , and equatorially in the two dorsal blastomeres of four-cell stage embryos to target the mesoderm . MO sequences . The amounts of MO and of mRNA injected are listed in the Text S1 . mRNA was injected animally at the two-cell stage for ectoderm expression and dorsally at the four-cell stage for dorsal mesoderm expression . Dissections and assays were performed in Modified Barth Solution ( MBSH ) containing: 88 mM NaCl , 1 mM KCl , 2 . 4 mM NaHCO3 , 0 . 82 mM MgSO4 , 0 . 33 mM Ca ( NO3 ) 2 , 0 . 41 mM CaCl2 , 10 mM Hepes , adjusted to pH 7 . 4 with NaOH and supplemented with 10 µg/ml streptomycin sulfate and penicillin . For the standard assay , explants were dissected at stage 10+ ( early gastrula ) . Ectoderm or mesoderm aggregates were laid on ectoderm caps , and the degree of separation was scored as the percentage of aggregates that did not incorporate into the cap after 45–60 min incubation [31] . In some cases , animal caps were induced into mesoderm by injection of 120 pg β-catenin and 1 ng constitutively active Activin receptor mRNAs . Separation of ventral tissue was similarly performed using stage 11 ectoderm and mesoderm from the ventral lip . For in vitro activation using soluble ephrins/Ephs , explants were either preincubated with preclustered ephrinB/Eph-Fc fragments ( 40 nM unless specified otherwise ) in MBSH for 15 min at room temperature ( e . g . , Figure 1D ) or the entire assay was performed in the presence of Fc fragments ( e . g . , Figure 2A ) . For the statistical analysis , results were compared using the two-tailed paired-sample Student's t test . Cells from dissected mesoderm and inner layer ectoderm were dissociated in alkaline calcium-free buffer ( 88 mM NaCl , 1 mM KCl , 10 mM NaHCO3 , pH 9 . 3 ) . For reaggregation , cells were transferred to agarose-coated Petri dishes containing MBSH and incubated for 1 h under mild rotation ( 10 rpm ) on an orbital shaker . Images were taken under a dissecting microscope at a12× magnification using a Micropublished RTV3 . 3 camera ( Qimaging ) and were analyzed for object size using ImageJ software . Two parameters were measured: average object area and area/perimeter ratio . Results of six independent experiments were normalized using wild-type ectoderm or mesoderm as reference . Wild-type embryos were fixed at stage 10 . 5 , and sagittal cryosections were prepared as described [55] , [56] . Sections were stained with anti–phospho-EphB antibody and Alexa488-coupled anti-rabbit IgG ( Invitrogen ) . Images were collected with a DMIRE2 epifluorescence microscope ( Leica ) equipped with a 20×/0 . 70IMM Corr CS oil immersion objective and an ORCA-ER camera ( Hamamatsu Photonics ) , controlled with Metamorph software . Tissues expressing membrane-targeted Cherry or GFP as well as various mRNAs/MOs were dissociated in dissociation buffer , cells were transferred to glass-bottom petri dishes ( Fluoro dish , World Precision Instruments ) coated with 0 . 01 mg/ml Fibronectin , and cell behavior was filmed for 2 h . Cells were imaged with a WaveFX spinning disc confocal ( Quorum Technologies ) mounted on an automated DMI6000B Leica microscope , controlled with Volocity 3DM software ( Improvision ) , using a 40× HCX PL APO CS , NA = 1 . 25 oil objective . Images were acquired every 2 to 5 min using an EM CCD 512×512 BT camera . Image processing was performed with Metamorph ( Universal Imaging Corporation ) and Adobe Photoshop7 software . Processing consisted of merging two to three planes from z stacks , assigning pseudo-colors , and adjusting image contrast . Cells from 14 ectoderm- and mesoderm-dissected explants were dissociated and an equal amount of cells were reaggregated either as mixed ectoderm/mesoderm aggregates , or as separate ectoderm and mesoderm aggregates for 1 h on agarose-coated plates in 1× MBSH . Mixed aggregates formed both homotypic ectoderm–ectoderm and mesoderm–mesoderm contacts as well as heterotypic ectoderm–mesoderm contacts , which mimicked the contacts at the boundary . The reaggregation time was set to maximize heterotypic contacts . Separate ectoderm and mesoderm aggregates , which formed only homotypic contacts , were combined for extraction , thus yielding the same amount of material as the mixed aggregates , and served as control . Extraction was performed in 1% NP40 containing buffer as previously described [46] . Extracts were probed by Western blot for EphA , EphB , p-EphA , and p-EphB . Ectoderm tissues were dissected from wild-type 40 embryos ( for EphB2 and EphB4 immunoprecipiation ) or from 40 embryos injected with EphA4-YFP mRNA ( 250 pg per blastomere ) for EphA4 immunoprecipitation . Dissected tissues were treated for 40 min with an equal concentration ( 40 nM ) of EphrinB1/B2/B3-Fc fragments , or control goat anti-human Fc . Tissues were extracted in 10 mM Hepes , 150 mM Nacl , 2 mM EDTA , 1% NP40 , supplemented with protease and phosphatase inhibitors [57] . Cleared lysates were incubated for 4–5 h with rabbit anti-EphB2 , anti-EphB4 , or anti-GFP ( for EphA4-YFP ) , followed by 1 h incubation with protein A-Sepharose beads ( Thermo Pierce ) at 4°C . The beads were washed four times with 10 mM Hepes , 150 mM NaCl , 2 mM EDTA , 1% NP40 , phosphatase inhibitors+0 . 5% sodium deoxycholate , 0 . 1% SDS . Immunoprecipitates were analyzed by Western blot for phospho-Tyrosine as well as for total EphA4 , EphB2 , and EphB4 . RT-PCR was performed using mRNA extracted from whole-stage 9–12 embryos . Loading was equalized by comparing levels of FGFR . Two independent experiments showed identical temporal patterns of expression . RT-qPCR was performed using mRNA extracted from ectoderm and dorsal mesoderm at stage 10 . 5 . qPCR was carried out using CFX 96 Thermo cycler ( Biorad ) . The PCR reactions were set up using 5 µl of RT ( 20 to 50 times diluted ) with 5 µl of SYBR green ( Biorad ) ½ dilution , 5 µl of 3× PCR-MgCl2 buffer ( Invitrogen ) , and 5 µl of a 6µM solution of primers . Cycling conditions were as follows: 94°C for 15 s , and 58°C for 30 s , 72 . 0°C for 1 min . Quantification was based on a dilution series ( five fold steps ) of the whole embryo RT . Relative expression levels were normalized as ratio to ODC , a ubiquitous gene with homogenous distribution in Xenopus embryos . The sequences of the primers are listed in Text S1 .
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How embryonic tissues separate from each other to shape the developing organism is a fundamental question in developmental biology . In vertebrates , this process relies on local repulsive reactions specifically generated at contacts between cells of different types . These reactions are triggered by typical repulsive cell surface cues , the ephrin ligands , and Eph receptors . However , the expression of multiple ephrins and the Eph receptors by each cell type represents a puzzle: Why is repulsion observed only at the tissue interface and not within the tissue itself ? By studying three cases of separation in the early amphibian embryo , we uncover a surprisingly simple logic underlying this phenomenon , which can be explained by the selectivity of ligand–receptor interactions and by their asymmetric distribution . The system is set such that , despite generalized interactions throughout the tissues , it is only at contacts between different cell types that the overall repulsive output is sufficiently strong to overcome cell–cell adhesion . Our study may serve as paradigm for how systematic dissection of complex cellular systems can reduce them to simple laws and make them intelligible .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"cell",
"biology",
"biology",
"and",
"life",
"sciences",
"developmental",
"biology"
] |
2014
|
Variable Combinations of Specific Ephrin Ligand/Eph Receptor Pairs Control Embryonic Tissue Separation
|
Human brucellosis is considered to be an important but typically under-diagnosed cause of febrile illness in many low and middle-income countries . In Kenya , and throughout East Africa , laboratory diagnosis for the disease is based primarily on the febrile antigen Brucella agglutination test ( FBAT ) , yet few studies of the diagnostic accuracy of this test exist . Assessment of the performance of the FBAT is essential for its appropriate clinical use , as well as for evaluating surveillance data reported by public health systems . To assess FBAT performance , we collected sera from people with symptoms compatible with brucellosis attending two health facilities in Busia County , Kenya . Sera were tested using the FBAT and results compared with those from the Rose Bengal Test ( RBT ) , an assay with well-known performance characteristics . Positives on either test were confirmed using the classical serum agglutination test ( SAT ) -Coombs test combination and a rapid IgM/IgG lateral flow immunochromatography assay ( LFA ) . A questionnaire focussing on known risk factors for exposure to Brucella spp . was also conducted , and relationships with FBAT positivity examined using logistic regression . Out of 825 recruited individuals , 162 ( 19 . 6% ) were classified as positive using the FBAT . In contrast , only eight ( 1 . 0% ) were positive using the RBT . Of the 162 FBAT positives , one ( 0 . 62% ) had an atypical agglutination in SAT and three ( 1 . 9% ) showed low Coombs titres . Out of 148 FBAT positive individuals tested using the LFA , five ( 3 . 4% ) were IgM positive and none were IgG positive . Poor or no correlation was observed between FBAT results and most established risk factors for Brucella infection . We observed substantial disagreement between the FBAT and a number of well-known serological tests , with the majority of reactive FBAT results appearing to be false positives . Poor FBAT specificity , combined with a lack of confirmatory testing , strongly suggests overdiagnosis of brucellosis is common in this low prevalence setting . This is expected to have important economic impacts on affected patients subjected to the long and likely unnecessary courses of multiple antibiotics required for treatment of the disease .
Brucellosis is a zoonotic disease with a global distribution that can cause severe illness in people and important economic losses in livestock [1–4] . Brucella abortus and B . melitensis are the most important zoonotic pathogens , and both species are likely to be endemic in cattle and small ruminants in Kenya [5–7] . In humans , brucellosis is a debilitating disease that can cause a wide range of symptoms including fever , arthralgia , myalgia and fatigue [8–10] . Development of focal ( e . g . joint , pulmonary , gastrointestinal , hepatobiliary , genitourinary and neurological ) complications is common and influenced by the length of time before diagnosis and initiation of treatment [11 , 12] . Treatment of human disease requires long courses of combined antibiotics and whilst rates of relapse are low with the best regimes [13] , compliance in resource-limited countries is often difficult to achieve . Brucellosis can therefore be a considerable economic burden on affected individuals , both in terms of the cost of therapy and days of potential work lost [14 , 15] . Whilst animal contact is a major risk factor for human infection , Brucella spp . are excreted in milk and may be present in the offal and meat of infected animals [16] . Hence , individuals living in both livestock and non-livestock keeping households may be at risk in areas in which Brucella spp . are endemic in animals . There is no human vaccine , and prevention of infection relies on reducing people’s exposure to infected animals and animal products [16] . Little is known about the prevalence of human brucellosis in sub-Saharan Africa [1 , 17] . The disease can mimic a variety of acute febrile illnesses , and laboratory tests are essential for diagnosis . Identifying cases presents a particular challenge in settings with limited laboratory capacity and where better-known causes of fever , such as malaria or typhoid , co-occur [1 , 17–21] . It is generally considered that human brucellosis is under-diagnosed in many of the areas in which it is endemic in livestock [9 , 22 , 23] . Culture of clinical specimens has 100% specificity , but often poor sensitivity , particularly in chronic cases , and also requires the availability of appropriate facilities [24] . Serological tests are therefore the mainstay of laboratory diagnosis in the majority of endemic areas . Serum agglutination assays using whole smooth Brucella cell suspensions as antigens are the most commonly used test and are available in a variety of formats . These include the rapid slide Brucella agglutination test performed with plain serum at neutral pH and the buffered plate agglutination tests , a family of rapid tests that use B . abortus suspensions in lactate buffer at low pH . The Rose Bengal Test ( RBT ) is the most widely used of the buffered plate agglutination tests [25] . While these rapid tests may be used to provide a semi-quantitative assessment of antibody titre through serial dilutions , the standard tube agglutination test ( SAT ) , performed with serum dilutions in tubes or microplates at neutral pH using saline as the diluent , enables more accurate assessment of antibody levels [24] . Infection with Brucella can result in the production of antibodies that do not agglutinate at neutral pH , which steadily increase and replace agglutinating antibodies over the course of the infection [11 , 25–27] . Thus , the SAT is usually complemented with the Brucella Coombs test [28] , an IgG and IgA anti-immunoglobulin assay [11 , 24 , 25] . All of these tests detect antibodies to the Brucella smooth lipopolysaccharide [24] , and the purified antigen is also used in a variety of non-agglutination based tests , including the lateral flow immunochromatographic assay ( LFA ) [29 , 30] . The sensitivity and specificity of serological testing for brucellosis varies depending upon the characteristics of the test and the antigenic suspensions used , as well as the stage of infection [24–27] . Reduced specificity can result from the persistence of anti-lipopolysaccharide antibody titres in a proportion of recovered patients as well as infections by cross-reacting bacteria [26 , 27 , 31–33] . The serological test currently used for the laboratory diagnosis of human brucellosis in government health facilities in Kenya and , to our knowledge , throughout Tanzania and Uganda , is a variant of the rapid slide Brucella agglutination test . There are several commercial kits currently available in East Africa , comprising separate ‘melitensis’ and ‘abortus’ antigens , marketed as part of a "febrile antigen test” kit . Despite their widespread use in East Africa , very little has been reported regarding the diagnostic accuracy of these febrile antigen Brucella agglutination tests ( FBAT ) or their predictive value in brucellosis endemic areas . Clearly , filling this information gap has important implications for the clinical application of this test . Furthermore , a recent review highlighted the paucity of data available to estimate global human brucellosis burden , and identified the strengthening of public health systems as an important mechanism to improve the quality of data captured through routine reporting [34] . A major consideration in interpreting the data generated by public health systems is the performance of tests used to diagnose brucellosis in endemic areas . The aim of this study was to assess the performance of the FBAT as currently used in health facilities in Kenya . We conducted a serological and questionnaire-based survey of individuals presenting to health facilities in western Kenya with symptoms compatible with brucellosis . The performance of the FBAT was compared with that of a range of serological assays with well-established diagnostic performance . To provide epidemiological evidence to support test performance , logistic regression was used to explore predictors of FBAT seropositivity .
Ethical approval for the study was granted by the Kenya Medical Research Institute ( KEMRI ) ethical review board ( SCC1701 ) . All participants provided written informed consent; minors between 5 and 17 signed an assent document and their guardians provided consent . The study was conducted in Busia county which has a total area of 1637 km2 and a population of 786 , 365 people ( Open Kenya ) . Two health facilities were selected for this study . These were Busia County Referral Hospital ( BCH ) and a private clinic at the Kenya Medical Research Institute ( KEMRI ) field station at Alupe . Livestock production in the County is characterised by a predominantly small-scale , mixed crop and livestock system [35] . Sampling took place on weekdays from June to December 2012 . A separate focus of the study was to use epidemiological tools to identify the source of infection in suspect brucellosis cases , with sampling effort intended to maximise the number of identified cases within the study period . Patients attending the outpatient clinic of BCH and the KEMRI clinic were recruited into the study by non-study health centre clinicians . The criterion for inclusion was clinical suspicion of brucellosis , including exposure history and compatible symptoms ( see Table 1 ) . We therefore aimed to include those patients who would normally present for laboratory-based brucellosis testing within either health facility . Exclusion criteria were patients less than 5 years of age , minors ( 5–17 years ) not accompanied by a guardian , patients presenting with severe illness requiring hospitalisation , and those patients with an already established diagnosis explaining illness . Patients typically pay for brucellosis diagnostics in government facilities in Kenya ( 1 . 50–3 . 00 USD ) , but testing was provided free of charge to all participants enrolled in this study . A study technician collected 6 ml of blood from study participants into a plain tube . Clotted blood was spun at 3000 rpm for 5 to 10 minutes and serum extracted . Each consenting or assenting participant underwent a questionnaire interview with a study clinician who collected information on clinical history , presenting clinical symptoms , and health seeking behaviour . On the basis of a maximum expected brucellosis incubation period of 6 months [36] , participants were given a structured questionnaire in which they were asked to recall high-risk exposures ( i . e . to food products or livestock ) in the 180 days prior to the onset of symptoms . Serum was used to perform the FBAT and RBT at BCH and the KEMRI clinic . Antigens for the FBAT used in these settings were manufactured by Fortress diagnostics , UK ( http://www . fortressdiagnostics . com , "Febrile Antigen Kit" ) and purchased locally . Standard hospital operating procedures were used for the FBAT . These were the same in both health centres and involved mixing a 50μl drop of serum with a drop ( approximately 50μl ) of ‘abortus’ antigen and drop of ‘melitensis’ antigen on a dedicated agglutination slide . Serum and antigen solution were rotated on a mechanical rotator for 2 minutes . As per standard procedures in both health facilities , tests were reported as ‘reactive’ if any evidence of agglutination was observed on naked eye examination . While the study health centres did not routinely measure antibody titres in reactive samples , these were semi-quantitatively assessed as part of this study using a serial dilution approach as described by the manufacturers . For this , 80 , 40 , 20 , 10 and 5 μl drops of serum were placed on agglutination tiles and a 50 μl drop of reagent was added to each , followed by mixing and rotation for 2 minutes . Agglutination in the first , second , third , fourth or fifth well was considered suggestive of a 1:20 , 1:40 , 1:80 , 1:160 or 1:320 titre , respectively . The RBT performed in each health centre followed standard procedures [25] and involved mixing 25μl of serum with 25μl of antigen on a white glossy ceramic tile with a wooden toothpick and rotation on a mechanical rotator for 4 minutes . The antigen , produced at the University of Navarra , was a suspension of fully smooth B . abortus 1119 standardized according to internationally established guidelines [37] and controlled for quality using a panel of brucellosis positive and negative serum samples [38] . Samples were confirmed as reactive if any perceptible agglutination , including the formation of a ring around the sample [25] , was observed on naked eye examination . Sera that were reactive using either the RBT or FBAT was subjected to the rapid LFA for the presence of IgG and IgM antibodies [30] . This test was marketed by Life Assay ( South Africa ) and was performed according to the manufacturer’s instructions . Remaining serum was stored at -40°C on the day of collection before being shipped on ice to the University of Navarra , Spain , for confirmatory serological testing . All samples were shipped at the end of the sampling period . At the University of Navarra , samples from the 825 individuals were subjected to a repeat of the RBT . The second RBT was performed by mixing 30μl of serum with 30μl of the aforementioned antigen on a white glossy tile , followed by gentle rotation by hand for 4 minutes , and sera yielding doubtful results rotated for an additional period of 4 minutes . Positive samples were subjected to semi-quantitative titration using the modified version of the test [25] . For this , eight 30μl drops of saline were dispensed on the tile and the first mixed with 30 μl serum . From this , 30μl was transferred to the second drop using a micropipette . This was repeated for each drop to derive dilutions ranging from 1:2 to 1:64 . Each drop was tested with an equal volume ( 30 μl ) of the RBT reagent , resulting in a range of final dilutions from 1:2 to 1:128 . Positive samples on the FBAT or RBT were also subjected to the SAT and Coombs test . The SAT was performed in a microplate format using a standardised antigenic suspension of B . abortus 1119–3 as previously described [39] and serum dilutions from 1:20 to 1:2 , 560 . The Coombs test was performed with anti-total human immunoglobulin serum in microplates [40] . A titre ≥1:160 was considered suspicious for the SAT . The Coombs test was considered positive for a titre ≥ 2 times the SAT titre from the same sample [24] . A random selection of FBAT positive samples ( n = 24 ) were retested by technicians at the University of Navarra using the same FBAT test procedure performed in participating health centres , but with a different kit ( Febrile Serodiagnostics , Biosystems ) . Multivariable logistic regression was used to assess the relationship between known risk factors for brucellosis and the log odds of seropositivity to the FBAT based on a 1:2 dilution ( i . e . equal volumes of serum and each antigen ) . Risk factors were extracted from the patient interview and were age in years , sex , consumption of high risk milk and yoghurt products , consumption of high risk meat or blood products , direct contact with the birth products of livestock ( cattle , sheep , goats or pigs ) , abortion of livestock in the participant’s compound , involvement in livestock slaughter , and knowing someone with a brucellosis diagnosis in the 6 months prior to the onset of symptoms . Food exposures were considered ‘high risk’ when products were consumed without prior heat treatment , or without adequate heat treatment in the case of blood and meat . The number of variables included in the final model was determined based on the number of FBAT positive results . One variable was selected for every 10 positive cases in order to ensure model complexity did not exceed the available number of degrees of freedom [41] . To meet this criterion , variables for inclusion were either a summarised ‘high risk’ exposure to any food or livestock , or the component parts ( i . e . different food types or different livestock exposure types ) where the sample size of FBAT cases allowed . No further model selection was performed . Model fit was assessed using the le Cessie-van Houwelingen normal test . Analysis was conducted using the rms package [41] in R , version 3 . 1 . 1 . ( http://cran . r-project . org/ ) .
A total of 825 individuals attending our study sites for care were recruited into the study , with 691 ( 84% ) coming from BCH and 134 ( 16% ) from the KEMRI clinic . It was possible to conduct the patient survey and collect clinical data from 703 ( 85% ) of these . The presenting symptoms and potential Brucella spp . exposure characteristics are summarised in Table 1 . The average age of participants was 37 . 6 years , with a median of 35 and a range between 5 and 98 years . The mean duration of symptoms was 118 days , but this was skewed by a small number of individuals ( n = 8 ) who reported the duration of current illness as more than 3 years . The median value was 14 days . The majority of individuals ( 426 , 60 . 6% ) reported that this was their first visit to a health care provider for their current illness . Of the remaining 277 , 177 ( 64% ) had already visited one health facility without resolution , 61 ( 22 . 2% ) had visited two , 20 ( 7 . 2% ) had visited three , 11 ( 3 . 9% ) had visited four and eight ( 2 . 9% ) had visited five or more . The median duration of symptoms in those presenting for the first time was seven days compared to 61 days in those who had previously sought health care for their current condition ( p<0 . 001 ) . High-risk food consumption was reported in 150 ( 21 . 4% ) participants , and high-risk animal contact ( defined as direct contact with animal birth products , animal abortion in the participant’s home compound or involvement in livestock slaughter ) in 37 ( 5 . 5% ) individuals ( Table 1 ) . On the basis of tests performed within study health facilities , a total of 162 out of 825 ( 19 . 6% , 95% CI 17 . 0–22 . 5 ) individuals had reactive FBAT tests ( Table 2 ) . At BCH , 147 ( 21 . 3% , 95% CI 18 . 3–24 . 6 ) FBAT tests were reactive , while at the KEMRI clinic , 15 ( 11 . 2% , 95% CI 6 . 6–18 . 0 ) ) were reactive . When the test was performed on serum dilutions , fifteen out of 162 ( 1 . 8% , 95% CI 1 . 1–3 . 1 ) ) were seropositive at titres ≥1:160 while 2 ( 0 . 2% , 95% CI 0 . 04–0 . 9 ) were seropositive at titres ≥1:320 , representing 9 . 3% and 1 . 2% of all FBAT positive tests , respectively . Of all 162 FBAT positives , 104 ( 62 . 4% ) were reactive to both the ‘abortus’ and ‘melitensis’ antigen , while 44 ( 27 . 2% ) were reactive to the ‘abortus’ antigen only and 14 ( 8 . 6% ) reactive to the ‘melitensis’ antigen only . Of the 15 FBAT tests seropositive at titres ≥1:160 , all but 1 were reactive to both abortus and melitensis antigen , while both of the FBAT tests seropositive at titres ≥1:320 were reactive to both antigens . Individuals with FBAT titres ≥1:160 were more likely to be reactive to both antigens compared to individuals with FBAT titres <1:160 ( OR = 8 . 9 , 95% CI 1 . 3–387 , p = 0 . 01 ) . Eight out of all 825 individuals ( 1 . 0% , 95% CI 0 . 4–2 . 0 ) were found to be positive using the RBT ( Table 2 ) . Of these , 6 were positive in the RBT performed in the study health facilities and four had titres ≥1:160 and 1 had a titre ≥1:320 when FBAT was performed on serum dilution . Similarly , 6 individuals were weakly positive on the basis of the repeat RBT at the University of Navarra . There was some discrepancy between the results of the repeat RBT at the University of Navarra and those performed in study health facilities , with 4 out of 6 results matching on both occasions ( Table 3 ) . None of the 8 RBT positive individuals was positive at dilutions equal or higher than 1:4 ( Table 2 ) . Positive sera with FBAT titres ≥ 1:160 were much more likely to be also RBT positive than sera with FBAT titres ≤ 1:160 ( OR = 22 . 8 , 95% CI = 3 . 8–168 . 9 , p = <0 . 001 ) . No FBAT negative sera were RBT positive ( Table 2 ) . Out of the 162 FBAT positive sera tested using the SAT at the University of Navarra , 161 were clearly negative with titres below 1:40 . The remaining serum ( 0 . 62% ) showed a 1:80–1:160 titre ( Table 2 ) which could be considered as suspicious . However , agglutination was atypical and consisted of mucoid filamentous aggregates rather than the typical clumps . This same serum was RBT negative and Coombs negative when tested at the University of Navarra , but was RBT positive when tested in study health facilities . Three ( 1 . 4% ) sera , all with clearly negative SAT titres ( ≤ 1:40 ) , were weakly positive using the Coombs test ( Table 3 ) , and of these three , two were weakly RBT positive ( Tables 2 and 3 ) . A total of 148 of the 162 FBAT reactive sera were tested using the LFA , of which five ( 3 . 4% ) were also positive using the IgM LFA but negative in the IgG LFA ( Tables 2 and 3 ) . Three of these five were positive at FBAT titres ≥1:160 ( Table 3 ) . Of the 24 randomly selected FBAT positive results that were retested at the University of Navarra using a different febrile antigen kit , 23 ( 96% ) were positive . The large number of FBAT seropositives ( 138 with linked questionnaire data ) provided sufficient sample size to allow us to explore the full range of potential predictors of seropositivity . Observed relationships are presented in Table 4 . Knowing someone with a brucellosis diagnosis increased the odds of FBAT seropositivity , as did involvement in animal slaughter . Males were at lower risk of FBAT positivity . The goodness-of-fit test indicated no evidence of any lack of model fit ( p = 0 . 2 ) .
The findings from this study strongly suggest that human brucellosis is being over-diagnosed in a mixed farming area of western Kenya . We expect that this is contributing to the over use of antibiotics , and has important economic impacts on affected patients . The FBAT used in government facilities throughout Kenya appears to have very poor diagnostic specificity and should be phased out . Further studies are needed to assess alternative diagnostic tests and testing strategies that can replace the FBAT in health facilities in the region . However , in the short term , and while awaiting the development of these alternatives , published recommendations and the results from this study suggest that the standard RBT , with antigen sourced from established sources with high standards of quality control , would provide a better alternative than the FBAT for the laboratory diagnosis of human brucellosis .
|
Brucellosis is a debilitating disease of people caused by infection with one of a number of different Brucella species . In almost all cases , people acquire the infection through exposure to infected animals or contaminated animal products . Human brucellosis is well known for its wide range of symptoms , and is often clinically indistinguishable from other infectious diseases , such as malaria or typhoid . Diagnosing the disease therefore typically relies on laboratory tests . A wide range of tests are available , but little is known about the accuracy of the principal test used in Government health facilities in Kenya , the febrile Brucella agglutination test ( FBAT ) . In this study , we identified people with symptoms compatible with brucellosis attending health centres in Kenya . By comparing results from the FBAT performed on samples collected from these individuals with the results from a range of well-established diagnostic tests , we were able to show that the FBAT produces large numbers of false positive results . We expect that this leads to a high levels of overdiagnosis of brucellosis in some parts of Kenya . Treatment of the disease involves multiple weeks of multiple antibiotics , and these incorrect diagnoses may have important and unnecessary negative impacts on affected patients .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"livestock",
"medicine",
"and",
"health",
"sciences",
"immune",
"physiology",
"pathology",
"and",
"laboratory",
"medicine",
"pathogens",
"immunology",
"tropical",
"diseases",
"microbiology",
"geographical",
"locations",
"brucellosis",
"bacterial",
"diseases",
"veterinary",
"diagnostics",
"neglected",
"tropical",
"diseases",
"antibodies",
"bacteria",
"bacterial",
"pathogens",
"africa",
"veterinary",
"science",
"veterinary",
"medicine",
"immune",
"system",
"proteins",
"infectious",
"diseases",
"zoonoses",
"serology",
"proteins",
"medical",
"microbiology",
"brucella",
"microbial",
"pathogens",
"agriculture",
"people",
"and",
"places",
"biochemistry",
"kenya",
"diagnostic",
"medicine",
"physiology",
"biology",
"and",
"life",
"sciences",
"organisms"
] |
2017
|
Poor performance of the rapid test for human brucellosis in health facilities in Kenya
|
Leishmania major cutaneous leishmaniasis is an infectious zoonotic disease . It is produced by a digenetic parasite , which resides in the phagolysosomal compartment of different mammalian macrophage populations . There is an urgent need to develop new therapies ( drugs ) against this neglected disease that hits developing countries . The main goal of this work is to establish an easier and cheaper tool of choice for real-time monitoring of the establishment and progression of this pathology either in BALB/c mice or in vitro assays . To validate this new technique we vaccinated mice with an attenuated Δhsp70-II strain of Leishmania to assess protection against this disease . We engineered a transgenic L . major strain expressing the mCherry red-fluorescent protein for real-time monitoring of the parasitic load . This is achieved via measurement of fluorescence emission , allowing a weekly record of the footpads over eight weeks after the inoculation of BALB/c mice . In vitro results show a linear correlation between the number of parasites and fluorescence emission over a range of four logs . The minimum number of parasites ( amastigote isolated from lesion ) detected by their fluorescent phenotype was 10 , 000 . The effect of antileishmanial drugs against mCherry+L . major infecting peritoneal macrophages were evaluated by direct assay of fluorescence emission , with IC50 values of 0 . 12 , 0 . 56 and 9 . 20 µM for amphotericin B , miltefosine and paromomycin , respectively . An experimental vaccination trial based on the protection conferred by an attenuated Δhsp70-II mutant of Leishmania was used to validate the suitability of this technique in vivo . A Leishmania major strain expressing mCherry red-fluorescent protein enables the monitoring of parasitic load via measurement of fluorescence emission . This approach allows a simpler , faster , non-invasive and cost-effective technique to assess the clinical progression of the infection after drug or vaccine therapy .
Leishmania major is the main cause of cutaneous leishmaniasis ( CL ) in the Old World . Parasites are transmitted by Phlebotominae sandflies whilst blood feeding on infected mammalian hosts . CL is widely spread in the developing world , affecting people in 88 countries with 1 . 5 million new cases reported each year . CL usually produces ulcers on the exposed parts of the body that often leave disfiguring scars , which in turn , can cause serious social prejudice [1] . Conventional in vivo animal models for the study of parasite-host relationships involve large number of animals . These animals are required to be slaughtered at different time points in order to identify both anatomical distribution and parasite numbers in organs and tissues . Furthermore , this approach has some important limitations that must be overcome: i ) post-mortem analysis of animals makes it impossible to track the space/time progression of the pathogen within the hosts; ii ) spread of the pathogen to unexpected anatomic sites can remain undetected; iii ) in order to achieve precise and relevant data , it is necessary to kill large numbers of animals . Recent real-time in-vivo imaging techniques with genetically modified pathogens represent a valuable complementary tool . They can be used for conventional studies of pathogenesis and therapy as long as the modified pathogen retains the virulence of the parental strain . Moreover , this has led to an increased number of reports concerning genetically modified parasites that express bioluminescent and/or fluorescent reporters . This was principally developed for in-vitro infection studies and to monitor diseases in living animals [2] , [3] . Bioluminescent pathogens expressing the sea pansy Renilla reniformis luciferase have been used in experimental murine infections of Toxoplasma gondii [4] as well as in the rodent malaria parasite Plasmodium berghei [5] . A recent study has allowed scientists to identify the liver stages of firefly luciferase-expressing parasites in living animals [6] . This approach has also been successfully implemented in trypanosomatids . Lang and co-workers showed that a luciferase expressing L . amazonensis strain was useful for rapid screening of drugs in infected macrophages [7] . Further studies used the same techniques with L . major [8] , L . infantum [9] and in in-vivo murine experimental infections [7] , [10] . Besides , the use of Trypanosoma brucei expressing R . reniformis luc gene has permitted scientists to find unusual colonizing niches during the progression of African trypanosomiasis [11] . Fluorescent imaging offers several benefits: i ) unlike light-emitting proteins , fluorescent reporters do not require specific substrates: ii ) the fluorescence emitted is very stable over time and iii ) this approach is useful when studying tissue harvested from infected animals since parasites can be individually identified [12] . The first transgenic Leishmania species expressing the green fluorescent protein ( GFP ) was reported by Beverley's group [13] . Episomally transfected Leishmania spp . with GFP or enhanced GFP ( EGFP ) have enhanced High Throughput Screening ( HTS ) methods in free-living promastigotes [14]–[17] and amastigotes [18]–[22] . However , only recently , the stable transfection of the EGFP reporter has been found suitable for both in vitro and in vivo infection studies [21]–[24] . Although native GFP produces significant fluorescence and is extremely stable , the excitation maximum is close to the ultraviolet range , which can result in damaging living cells . Red-fluorescence labelled parasites have been used to determine the early stages of CL pathogeny at the infection site ( revised by Millington and co-workers [25] ) . By combining a L . major Red Fluorescent Protein 1 ( RFP ) -expressing strain and dynamic intravital microscopy , the site of sandfly bites has been identified in vivo in a mouse model . The study reveals an essential role for both neutrophils and dendritic cells that converge at localized sites of acute inflammation in the skin following pathogen deposition [26] , [27] . Using mCherry-L . infantum chagasi – responsible of visceral leishmaniasis in the New World – researchers have been able to report the recruitment of neutrophils and their role in non-ulcerative forms of leishmaniasis [28] . In addition , to study the mechanism regulating dentritic cell recruitment and activation in susceptible BALB/c [29] and resistant C57BL/6 mice [30] DsRed labelled parasites were used Fluorescent parasites have been used to explain some aspects of Leishmania biology . L . donovani lines stably expressing either EGFP or RFP have been used to identify hybrid parasites produced during the early development of the sandfly [31] . In addition , a L . major strain , which episomally expressed the DsRed protein , was used for quantifying the infectious dosage transmitted by a sandfly bite [32] . Based on the improved photostability as well as suitability for intravital imaging , mCherry was considered the best choice for our studies in comparison to other red fluorescent proteins [33] . mCherry is a protein derived from the coral Discosoma striata RFP . It has a maximum emission peak at 610 nm with a 587 nm excitation wavelength . Despite the fact that it is 50% less bright than EGFP , it is more photostable and it has higher tissue penetration [12] . Because of this , it is the best-suited choice in applications of single-molecule fluorescence or multicolour fluorescent imaging [34] . In this report we describe the use of a stably mCherry-transfected L . major strain as a valuable tool to both in vitro assays for drug screening and in vivo pre-clinical vaccine studies in real-time .
The animal research described in this manuscript complied with Spanish ( Ley 32/2007 ) and European Union Legislation ( 2010/63/UE ) . The used protocols were approved by the Animal Care Committee of the Centro de Biología Molecular and Universidad Autónoma de Madrid ( Spain ) . Female BALB/c mice ( 6–8 week old ) were purchased from Harlan Interfauna Iberica S . A . ( Barcelona , Spain ) and maintained in specific-pathogen-free facilities for this study . L . major LV39c5 ( RHO/SU/59/P ) strain was used for generating mCherry transgenic promastigotes . Parasites were cultured at 26°C in M199 supplemented with 25 mM HEPES pH 7 . 2 , 0 . 1 mM adenine , 0 . 0005% ( w/v ) hemin , 2 µg/ml biopterin , 0 . 0001% ( w/v ) biotin , 10% ( v/v ) heat-inactivated foetal calf serum ( FCS ) and antibiotic cocktail ( 50 U/ml penicillin , 50 µg/ml streptomycin ) . Attenuated Δhsp70-II ( Δhsp70-II::NEO/Δhsp70-II::HYG ) , used as candidate vaccine [35] , is a null mutant for the hsp70-type-II gene , generated by targeted deletion in L . infantum ( MCAN/ES/96/BCN150 ) strain [36] . Δhsp70-II promastigotes were grown in RPMI 1640 ( Sigma-Aldrich ) culture medium supplemented with 10% ( v/v ) FCS , 50 U/ml penicillin and 50 µg/ml streptomycin . The 711-bp mCherry coding region was amplified by PCR from pRSETb-mCherry vector , a kindly gift from Dr Roger Y . Tsien – Departments of Pharmacology and Chemistry & Biochemistry , UCSD ( USA ) – [37] with the primers RBF634 and RBF600 ( Table 1 ) . PCR product was cut with appropriate restriction enzymes and ligated into the BglII and NotI sites of the pLEXSY-hyg2 expression vector ( Jena Bioscience GmbH , Germany ) . Parasites expressing mCherry Open Reading Frame ( ORF ) were obtained by transfection of L . major with the large SwaI targeting fragment derived from pLEXSY-mCherry by electroporation and subsequent plating on semisolid media containing 200 µg/ml hygromycin B ( Sigma-Aldrich ) as previously described [38] . Correct integration of mCherry ORF into the 18S rRNA locus of the resulting transgenic clones ( mCherry+L . major ) was confirmed by Southern blot and PCR amplification analyses , using the primers of Table 1 . The fluorescent of stable-transfected mCherry clones was confirmed by both flow cytometry ( BD FACSCantoII ) and confocal microscopy ( Nikon Eclipse TE2000E ) . Starch-elicited peritoneal macrophages were recovered from BALB/c mice and then 5×104 cells were plated on black 96 wells plates with clear bottom . Macrophages were infected at a ratio of five metacyclic promastigotes per macrophage . Metacyclic mCherry+L . major promastigotes were isolated from stationary cultures ( 4–5 days old ) by Ficoll gradient centrifugation [39] . Briefly , 2 ml of parasite suspension in M199 containing approximately 7×107 stationary-phase promastigotes were layered onto a discontinuous density gradient in a 15 ml conical tube consisting of 2 ml of 20% ( w/v ) Ficoll stock solution made in distilled water and 2 ml of 10% ( w/v ) Ficoll diluted in M199 medium . Metacyclic parasites were opsonized with 4% ( v/v ) C5− mouse deficient serum ( The Jackson Laboratory , USA ) at 37°C for 30 min and resuspended in RPMI containing 10% ( v/v ) FCS [40] . The infection was synchronized by centrifugation ( 330×g , 3 min at 4°C ) and infected macrophages were incubated at 37°C in a humidified 5% CO2 atmosphere [41] . Cells were washed extensively with phosphate buffer saline ( PBS ) to remove the free parasites and overlaid with fresh medium , which was replaced daily thereafter . After one day incubation , to allow differentiation into amastigotes , drugs ( miltefosine , amphotericin B and paromomycin ) were added to the appropriate wells in a threefold dilution series in RPMI ( Sigma-Aldrich ) with 10% ( v/v ) FCS and cells were further incubated at 37°C for a further incubation of 72 h . Plates were read in a fluorescence microplate reader ( Synergy HT; BioTek ) ( λex = 587 nm; λem = 610 nm ) . Metacyclic promastigotes were isolated from stationary cultures ( 4–5 days old ) by negative selection with peanut agglutinin for mouse infections . Briefly , promastigotes were resuspended in PBS at 108 cells/ml , and peanut agglutinin ( Vector laboratories ) was added at 50 µg/ml; the sample was incubated for 25 min at room temperature . After centrifugation at 200×g for 10 min , the supernatant contained the non-agglutinated metacyclic promastigotes [42] . The virulence of L . major parasites was maintained by passage in BALB/c mice by injecting hind footpads with 106 stationary-phase parasites . After 6–8 weeks , animals were euthanized and popliteal lymph nodes were dissected , mechanically dissociated , homogenized and filtered . L . major amastigotes were isolated from murine lymph nodes by passing the tissue through a wire mesh followed by disrupting the cells sequentially through 25G1/2 and 27G1/2 needles , and polycarbonate membrane filters with pore size of 8 , 5 and 3 µm ( Isopore , Millipore ) [43] . Isolated amastigotes were transformed to promastigote forms by culturing at 26°C in Schneider's medium ( Gibco , BRL , Grand Island , NY , USA ) supplemented with 20% ( v/v ) FCS , 100 U/ml penicillin and 100 µg/ml streptomycin . For infections , amastigote-derived promastigotes with less than five passages in vitro were used . BALB/c mice were injected with several inocula ( 104 , 105 and 106 promastigotes/mouse ) during the setting up of the model . For protection studies mice were vaccinated intravenously ( tail-vein injection ) with the Δhsp70-II mutant strain ( 2×107 promastigotes/mouse ) [35] , or injection of PBS ( control group ) , and four weeks post-vaccination , were infected with 2×105 mCherry+L . major metacyclic promastigotes . The infections were performed by injection of parasites in 50 µl PBS in the right hind footpads . The growth of the lesion was monitored by fluorescence emission detection ( see below ) . The contralateral footpad of each animal represented the negative control value . Footpad swelling was measured using a Vernier calliper and data were represented as the increment of the lesion size respect to the not infected footpad . Fluorescence emission was measured using an intensified charged coupled device camera of the In Vivo Imaging System ( IVIS 100 , Xenogen ) . Wild type- and mCherry+L . major-infected animals were lightly anesthetized with 2 . 5–3 . 5% isoflurane and then reduced to 1 . 5–2 . 0% . Anesthetized animals were placed in the camera chamber , and the fluorescence signal was acquired for 3 s . Fluorescence determinations , recorded by the IVIS 100 system , were expressed as a pseudocolour on a gray background , with red colour denoting the highest intensity and blue the lowest . To quantify fluorescence , a region of interest was outlined and analyzed by using the Living Image Software Package ( version 2 . 11 , Xenogen ) . The total number of living parasites invading the target organs ( popliteal lymph node draining the injected site ) was calculated from single-cell suspensions that were obtained by homogenization of the tissue through a wire mesh . The cells were washed and cultured in Schneider's medium containing 20% ( v/v ) heat-inactivated FCS , 100 U/ml penicillin and 100 µg/ml streptomycin . The cell suspensions were serially diluted and dispensed into 96-well plates . The plates were incubated for 10 days and then each well was examined and classified as positive or negative according to whether or not viable promastigotes were present . The number of parasites was calculated as follows: Limit Dilution Assay Units ( LDAU ) = ( geometric mean of titer from quadruplicate cultures ) × ( reciprocal fraction of the homogenized organ added to the first well ) . The titer was the reciprocal of the last dilution in which parasites were observed [44] .
Aimed to create a L . major fluorescent strain we electroporated wild-type promastigotes with the lineal 5874 bp SwaI-SwaI fragment containing the ORF encoding mCherry as well as the hyg selection marker of the pLEXSY-mCherry plasmid . After selection on semisolid plates containing 100 µg/ml hygromycin B , individual colonies were seeded in M199 liquid medium supplemented with 10% FCS and hygromycin B . Genomic DNA isolated from these cultures was used to confirm the correct integration of the target sequence into the 18S rRNA locus of L . major genome . Figure 1A shows a schematic representation of the planned integration . Genomic DNA from wild-type strain and two hygromycin B resistant clones were digested with NdeI and hybridized with a labelled external probe ( EP ) . As shown in the Southern analysis of Fig . 1B , wild-type DNA digested with NdeI yielded an 8 . 4-kb hybridization band , whereas in the two-hygromycin B resistant clones an additional 3 . 8-kb hybridization band was observed; this band is generated by the integration event ( Fig . 1A ) corresponding to the expected size . Further confirmation of the correct planned replacements was confirmed by PCR ( Fig . 1C ) using the set of primers depicted in Fig . 1A . The mCherry expression in stable-transfected L . major promastigotes ( mCherry+L . major ) was monitored by flow cytometry . Cell populations of mCherry+L . major strain and a parasite line containing the pLEXSY-mCherry episome emitted strong red fluorescence when they were excited at wavelength of 587 nm ( Fig . 2A ) . Clones with integrated mCherry gene had an average 10-fold higher fluorescence than the ones expressing the gene episomally . This is an expected result given that the mCherry gene was integrated under the control of rRNA promoter , which is known to present high-level transcription rates . As shown in Fig . 2B , both strains ( episomal and integrative ) were strongly more fluorescent than untransfected parasites . In order to establish the correlation between parasite number and fluorescence intensity , different number of procyclic and metacyclic promastigotes as well as freshly isolated amastigotes from infected animals were placed in 96-well plates and their fluorescence intensity was measured spectrofluorometrically . A clear correlation between fluorescence intensity and the number of the three parasitic forms was observed ( Fig . 2C ) . The stability of mCherry expression was monitored over a period of 6 months after transfection and no change was observed in fluorescence intensity during this period , even in the absence of hygromycin B . Once the infectivity of the mCherry+L . major parasites was recovered through mouse infections , the amastigotes obtained from cutaneous lesions were differentiated back into promastigotes . These cells were grown up to stationary phase and used to infect freshly isolated BALB/c peritoneal macrophages at a 5∶1 multiplicity in 24-well plates . Figure 3 shows fluorescence images of either promastigotes or amastigotes internalized in macrophages . A strong red fluorescence emission from free-living mCherry+L . major promastigotes was observed by confocal microscopy ( Fig . 3B ) . Similarly , round-shaped red fluorescent emitting amastigotes were observed inside parasitophorous vacuoles in the cytoplasm of the infected macrophages ( Fig . 3F ) . The course of the in vitro infection was followed over a period of 48 h by measuring the absolute fluorescence of the infection and the percentage of infected macrophages . These experiments were carried out in parallel with others using the classical Giemsa staining to determine parasite load in vitro ( data not shown ) . No differences between both methods were accounted thus pointing to the suitability of fluorescence analyses to assess the infectivity of mCherry+L . major strain on mouse macrophages . A major application of a fluorescent Leishmania model would be its usefulness to perform HTS of potential leishmanicidal compounds in vitro . To assess the suitability of our mCherry+L . major for this goal , current drugs used in the treatment of human leishmaniasis ( miltefosine , paromomycin and amphotericin B ) were assayed in Leishmania-macrophage infections at different concentrations over a 72 h-span . Absolute fluorescence emitted by mCherry+L . major infected macrophages was plotted vs . drug concentrations , obtaining the dose-response curves of Fig . 4 . Nonlinear regression analysis of the curves , fitted by the SigmaPlot statistic package , reached IC50 values of 0 . 12±0 . 03 µM for amphotericin B , 0 . 57±0 . 12 µM for miltefosine and 9 . 20±3 . 59 µM for paromomycin . In all the cases the difference in fluorescence emission corresponded to a difference in the percentage of infected cells , also observed microscopically . These findings clearly showed that the mCherry+L . major strain is a useful tool for in vitro drug screening . In order to determine whether mCherry+L . major parasites could be detected in vivo using whole-body imaging , 104 , 105 and 106 metacyclic forms of the fluorescent-transgenic strain were injected subcutaneously into the hind footpads of six BALB/c mice per group . Lesion progression monitored both by direct measuring of fluorescence emission by mCherry+L . major amastigotes ( recorded in an IVIS 100 ) and by the development of hind-limb lesions assessed by measuring the thickness of the footpads with a Vernier calliper . Animals were examined every seven days for a total of eight weeks ( except the group infected with 106 parasites that were sacrificed at 6th week post-infection because the appearance of ulcerations in the footpads ) . Figure 5 shows the fluorescence intensity recorded weekly from the footpads of representative mice of each inoculum group ( 104 , 105 and 106 metacyclic promastigotes per mouse ) . The fluorescent signal ( estimated as average radiance: p/s/cm2/sr ) was plotted against the infection time of each inoculum ( Fig . 6A ) . Fluorescent signal was detected after the first week post-infection in mice infected with 106 metacyclic parasites ( radiance = 0 . 26×108 p/s/cm2/sr ) , reaching a radiance of 5 . 0×108 p/s/cm2/sr five weeks later . In the group of mice injected with 105 metacyclic parasites , the fluorescence was detected the third week after inoculation ( radiance = 0 . 8×108 p/s/cm2/sr ) , reaching similar intensity than the mice group injected with 106 parasites at the 8th week of inoculation . Finally , the fluorescence signal of mice injected with 104 metacyclic mCherry+L . major parasites was not detectable until the 5th week ( radiance = 0 . 35×108 p/s/cm2/sr ) , reaching the maximum intensity ( radiance = 1 . 14×108 p/s/cm2/sr ) at the end of the experiment . The success of infection defined as the lesion onset and its development in the inoculated footpads over the time , was observed in every mice . Although there was a good correlation to lesion size , fluorescence was more sensitive to evaluate the progression of infection . Figure 6B shows that lesion emergence was dependent on the size of pathogen inoculum and it was hardly measurable during the first weeks after infection . Lesion size in mm was 0 . 34 , 0 . 94 and 0 . 57 measured at the third , fifth and seventh week , respectively corresponding to 106 , 105 and 104 metacyclic promastigotes per inoculum . It is remarkable that a weak but measurable fluorescence signal from infected hind limbs was detectable two weeks prior to visible and measurable injury took place in all dose groups . As expected and since BALB/c mice have a predisposition to develop an anti-inflammatory Th2 response , the lesions appearing after infection setup were non-healing without treatment . A correlation analysis of the fluorescence emitted by the lesions toward the end of the 8-week period from mice infected with 104 and 105 metacyclic mCherry+L . major parasites ( Fig . 6C ) shows significant differences between both groups ( P<0 . 001 ) using unpaired t-Student test . These differences were also found when the lesion thickness of the footpads was compared using the traditional calliper-based method ( Fig . 6D ) . There was a clear correlation between both parameters in both dosing groups with an estimated Pearson coefficient of 0 . 94 . At the end of the experiments , animals were sacrificed and the popliteal lymph nodes draining the lesions were dissected under sterile conditions . The parasite load of these organs was determined by the limit dilution method . Figure 6E shows the number of promastigote-transforming amastigotes estimated in the animals of both dosing groups , showing significant differences ( P<0 . 001 ) and correlating highly with both size lesion and fluorescence ( Pearson coefficient = 0 . 79 ) . The suitability of this in vivo approach was assessed for the evaluation of an experimental vaccination protocol against CL that had been previously shown to be effective on a L . major–BALB/c infection model [35] . In previous studies , it was established that intravenous inoculation with Leishmania promastigotes , lacking both alleles of the hsp70-II gene ( Δhsp70-II line ) , confers a partial protection against L . major infection in mice . For this study , we inoculated a group of six mice with 2×107 promastigotes of Δhsp70-II mutant and four weeks later , mice were challenged with 2×105 metacyclic forms of the mCherry+L . major strain into mouse footpads . In parallel to the vaccinated group , a control group was injected with the same inoculum of mCherry-expressing transgenic parasites . Red fluorescence emission in the footpad of mice infected with mCherry+L . major parasites was followed over the time in both groups ( Fig . 7A ) . Fluorescence signal was detected in both groups four weeks after challenge; however , fluorescence signal was higher in control group mice than in vaccinated animals . By the end of the 8th week animals were euthanized , the poplyteal lymph nodes dissected , homogenized and the parasite load determined as above . Figure 7B shows an 80% reduction ( P<0 . 001 ) in the parasite load of popliteal lymph nodes of vaccinated group related to the control group . Therefore , since reproducible results were obtained with both parasite quantification and fluorescence emission methods , we conclude that the murine model of CL established with the mCherry+L . major fluorescent strain might be a suitable system for testing antileishmanial therapies both in vitro and in vivo .
Transgenic parasites expressing reporter proteins are valuable tools to perform robust HTS platforms [45] and to understand the underlying mechanisms of pathogenesis [3] . GFP is one of the most commonly used reporters among fluorescent proteins . Several mutants derived from native GFP have been developed to cover longer wavelengths of the spectrum . Reporter molecules , whose emission peak is in the red spectral range , the same as mCherry , are excellent candidates for these kinds of studies . Furthermore , light absorption by tissues in the red and far-red spectra is reduced and consequently , the penetration is higher [46] . Moreover , mCherry is the best general-purpose red monomer due to its superior photostability compared to mStrawberry and DsRed , which is inadequately folded at 37°C [13] . The integration of the reporter gene into the 18S rRNA locus of L . major represents an efficient and effective strategy to guarantee a stable expression when the parasites need to be grown in the absence of selection drugs for both in vitro screenings and in mice infections [7] , [22]–[25] , [47] , [48] . mCherry fluorescence was detected in the different stages of the L . major life cycle . Lesion-derived amastigotes showed two times less activity than metacyclic promastigotes . In turn , these were three times less fluorescent than logarithmic promastigotes . Similar results were reported in promastigotes of different Leishmania species , in which luciferase expression was much higher than that of amastigotes from animal lesions and experimentally infected macrophages , respectively [7] , [47] . However , Mißlitz and co-workers [23] showed that EGFP expression levels were 2–10 times higher in amastigotes than in promastigotes of both L . mexicana and L . major . Although these species were stably transfected by the integration into the 18S rRNA locus; they differed in the downstream region of the reporter gene . Whilst no specific 3′ untranslated region implicated in the stage-specific regulation was included downstream on the luc gene [47]; the intergenic calmodulin A region was configured into the pLEXSY plasmid ( [7] and the present work ) . In a similar way , the cysteine proteinase intergenic region ( cpb2 . 8 ) was included in the studies conducted by Mißlitz [23] . Intergenic sequences responsible for a high transcription rate in amastigotes should be included in future vectors for regulating the reporter's expression . In this sense , technologies such as RNA sequencing can provide a complete transcriptome that could be used to improve the expression technology in both promastigote and amastigote forms [49] , [50] . Assays designed to simplify rapid and large-scale drug screenings are not performed on the clinically relevant parasite stage , but on promastigotes instead . Axenic amastigotes have also been screened by means of HTS platforms [9] , [51] , [52] . However , expression arrays comparing both axenic amastigotes and those isolated from infected macrophages have shown metabolic differences , impaired intracellular transport and altered response to oxidative stress [53] . The suitability of mCherry+L . major transgenic strain is an important tool for bulk testing of drugs in the intracellular amastigote stage . This was demonstrated further by using three drugs in clinical use against leishmaniasis: amphotericin B , paromomycin and miltefosine . Most of the drug screening assays attempted to analyze intracellular parasites using GFP-tranfected Leishmania spp . Theses methodologies clearly showed that there was not enough sensitivity to enable a precise and reliable microplate screening . Consequently an in-depth flow cytometric analysis is required [54] . Recently , a novel method for assessing the activity of potential leishmanicidal compounds on intracellular amastigotes through the use of resazurin ( a fluorescent dye with emission wavelength in the red spectrum ) has advanced to microplate analysis [55] . Unlike the GFP-expressing parasites , mCherry emission is also found in the same spectral range as resazurin . This level of sensitivity was sufficient to detect 104 amastigotes isolated from lesions . This means that mCherry reporter provides several benefits over fluorescent proteins for performing HTS into microplate format . Other advantages of fluorescent proteins are that they allow a dynamic follow up ( kinetic monitoring ) of the drug efficiency using a single plate . Drugs must be maintained in the culture medium for a time long enough for them to take effect . On the contrary , multiple plates are required if a specific substrate is added , requiring one for each recorded time interval . Through our research we want to raise the importance of the source of host cells used for experimental infections when drug-screening assays are carried out . Several differences in the host-parasite interactions have been pinpointed when comparing primary macrophages with immortalized human macrophage-like cell lines [56] . Most of the current multiwell-screening methods involve established-macrophage cell lines since it is quite difficult to scale-up a procedure based on primary macrophages [57] , [18] , [58] , [20] , [22] , [59] . Accordingly , a well-planned combination of different approaches ( promastigote/intracellular; cell line/primary cultures ) would help us to identify lead compounds through large-scale drug screening [60] , [61] . The manipulation of large numbers of potential drugs not only requires easy-to-use , repeatable and readily quantifiable tests , but also it needs to mimic natural conditions within the host cell . Because of the profound influence of the host's immune response on the treatment of leishmaniasis , new approaches should include the whole immunopathological environment found at the host-parasite interaction site . However , only one alternative approach has been used in order to transfer this immunological concept to HTS systems [43] , [61] . The main advantages of mCherry-transfected parasites are automation and miniaturization . As experiments are performed in 96-well plates , reducing costs of reagents , and time of analysis is of great importance . Besides , we can also eliminate tedious steps such as staining or cell lysis . In addition this allows a dynamic follow up as cells remain viable after each reading time interval . As the stable integration of the gene encoding reporter proteins represents a valuable tool for assessing whole-body imaging in laboratory mice [47] , [7] , [10] , [62]–[64] , we decided to use the same mCherry-transfected strain for in vivo applications . Experimental infections with L . major in BALB/c mice footpads resulted in a non-healing and destructive chronic lesion at the site of injection . The mCherry in vivo model developed in this study clearly allowed the fluorescence signal in the first week post-inoculation with 1×106 stationary parasites , a dose used in leishmanial research to induce the rapid development of CL . Similar models in BALB/c mice with EGFP , used 10- and 200-fold parasite doses and the fluorescence signal was visualized afterwards [21] , [24] . The lymph node draining the lesion was not detected in this study , probably because the lower inoculum used or because of the shorter time of testing when the animals were killed . Previously , reports detected the fluorescence or luminescence signal emitted by the lymph node a long time after post-infection ( 2 . 5–10 months ) [7] , [21] , [23] . In order to evaluate the eligibility of our fluorescent tool for the monitoring of in vivo treatments , we applied this approach by evaluating an experimental vaccine against leishmaniasis that had been previously shown to be effective . Our previous studies showed that a L . infantum strain lacking the hsp70-II gene ( Δhsp70-II line ) conferred resistance to a subsequent infection with L . major [35] , [36] . We found that the progression of the infection was efficiently and effectively observed by recording the mCherry signal through real-time imaging . Vaccination of infected mice for a period of 8 weeks with the vaccine reduced the infection when compared with the control group . Further to this , Mehta and co-workers successfully used a similar vaccination approach to assess the efficiency of a real-time imaging platform using an engineered strain of L . amazonensis expressing the egfp gene [24] . In conclusion , we have developed a valuable fluorescence-emitting L . major transfected strain . This strain allows us: i ) actual imaging , which is important when studying tissue harvested from an infected animal because parasites can be individually identified; ii ) to easily develop new , fast and efficient platforms for the screening of potential leishmanicide drugs testing thousands of compounds in Leishmania amastigote-infected macrophages; iii ) to reproduce the infection in real-time due to the virulence of L . major-transfected strain , which in turn increases the sensitivity of detection especially at the earlier phases of the process . Furthermore , this avoids the unnecessary slaughter of large amounts of animals at different time-points owing to direct imaging and fluorescence testing , which can be performed without traumatic handling to the animals .
|
Leishmaniasis is a parasitic disease that is far from eradication . The lack of an efficacious vaccine and treatment failures are major factors in its intractable worldwide prevalence . A non-invasive imaging technique using genetically engineered parasites that expressed fluorescent proteins could give to researchers a quantitative and visual tool to characterize the parasite burden in experimental infections . In addition , it can be useful for determining the efficacy of candidate vaccines or drugs using High Throughput Screening methods that allow the testing of libraries of compounds in an automated 96-well plate format . Herein , we demonstrate that there is a good correlation between fluorescence emission and the parasite load , thus permitting the use of this output to monitor the progression of the disease . In order to validate this tool we have immunized mice prior the parasite challenge with a red-emitting parasite strain , confirming the scientific suitability of this approach as a valuable alternative model .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"parastic",
"protozoans",
"medicine",
"infectious",
"diseases",
"leishmania",
"leishmaniasis",
"neglected",
"tropical",
"diseases",
"protozoology",
"biology",
"microbiology",
"parasitic",
"diseases"
] |
2012
|
Appraisal of a Leishmania major Strain Stably Expressing mCherry Fluorescent Protein for Both In Vitro and In Vivo Studies of Potential Drugs and Vaccine against Cutaneous Leishmaniasis
|
Variability , stochastic or otherwise , is a central feature of neural activity . Yet the means by which estimates of variation and uncertainty are derived from noisy observations of neural activity is often heuristic , with more weight given to numerical convenience than statistical rigour . For two-photon imaging data , composed of fundamentally probabilistic streams of photon detections , the problem is particularly acute . Here , we present a statistical pipeline for the inference and analysis of neural activity using Gaussian Process regression , applied to two-photon recordings of light-driven activity in ex vivo mouse retina . We demonstrate the flexibility and extensibility of these models , considering cases with non-stationary statistics , driven by complex parametric stimuli , in signal discrimination , hierarchical clustering and other inference tasks . Sparse approximation methods allow these models to be fitted rapidly , permitting them to actively guide the design of light stimulation in the midst of ongoing two-photon experiments .
Over the last two decades , two-photon ( 2P ) imaging has become one of the premier tools for studying coding in neural systems from the population level down to individual neural compartments [1] . The resulting data is highly variable due to the inherent variability of neurons and technical sources of noise in the imaging process [2 , 3] . Yet we typically assume that beneath the noisy signals which are observed there is a smooth latent function describing the activity of a neuron or a neural compartment . In a typical analysis pipeline for 2P data , we attempt to recover this function by grouping noisy observations from pixels into regions of interest ( ROIs ) , which cover the soma or different compartments of a neuron , temporally interpolating them to a common frame rate and averaging across repetitions of the same stimulus ( see also Box 1 ) . Each stage is intended to smooth the observations and get closer to the “true” underlying activity function of the neuron . To measure the uncertainty about this latent activity function , often the variance between repetitions of the same experimental condition is used , with little assessment of whether this reflects the actual uncertainty given measurement and neural variability . Here , we propose a different approach based on Gaussian Process ( GP ) regression [4] to infer signals from 2P recordings in a statistically principled manner , propagating the uncertainty all the way from the measurements to the desired inference . This regression procedure recovers an estimate of the true activity of the neuron , whether changes in calcium or glutamate concentration , from observations with experimental noise . This is facilitated by modelling explicitly the change in the signal over time and as a function of stimulus parameters . Gaussian processes are probabilistic models , which describe the functional relationship between a set of predictors and a set of observations ( see Box 2 for a mathematical primer ) . In contrast to typical pre-processing pipelines , the statistical properties of the observed signal are considered explicitly as part of the model optimisation . Recently developed sparse GP approximations allow us to apply these models to comparatively large datasets with several thousand observations , as are common in 2P experiments [5] . Using 2P recordings of calcium and glutamate dynamics in isolated mouse retina , we demonstrate how these models can be used to construct probabilistic representations of neural activity . We treat several use cases: First , we show that GP-based analysis of 2P recordings can be used to perform comparisons between the responses of a given cell under different conditions , allowing one to identify parts of the response with significant differences . Second , we exploit the properties of the GPs to perform a hierarchical clustering of cell responses and provides quantitative criteria for deciding how many clusters to keep . In addition , we use the framework to test which stimulus parameters influence neural activity in an ANOVA-like framework . Finally , we explore how the representation of uncertainty can be exploited for experimental design , informing the choice of parameters to optimally reduce the uncertainty about the neural response .
Our first objective was to infer the neural activity function and its associated uncertainty from our observations of the activity of single ROIs , located on individual synaptic axon terminals of a bipolar cell . In bipolar cells injected with the calcium indicator OGB-1 , it is possible to image the anatomy of the cell by recording 3D stacks of x-y images at regular intervals along the vertical ( z ) axis ( Fig 1a ) . High resolution scans allowed us to identify individual axon terminals ( Fig 1b ) . Faster scans with lower spatial resolution are required to resolve neural activity , although the required reduction in resolution is substantially less for spiral configurations relative to classical linear configurations ( Fig 1c and 1d ) . Although the scan patterns are highly regular , the spatial organisation of the neural structures results in irregular sampling over time ( Fig 1e–1h ) . We recorded bipolar cell calcium and glutamate signals measured during the presentation of a spatially homogeneous light stimulus including a light step and variations in temporal frequency and contrast ( Fig 2a , chirp stimulus ) , as used in previous studies [7 , 11] . We used the observed activity of a ROI ( Fig 2b ) , and inferred a signal for each repeat using frame-averaging and cubic-spline interpolation ( Fig 2c ) , corresponding to the classical way of inferring these functions ( i . e . [7 , 11] ) . We then fitted a GP with a radial basis function ( RBF ) kernel for the time dimension to the observed activity ( Fig 2d ) . We monitored the computation time and calculated the likelihood of an out-of-sample test set to determine a suitable number of inducing inputs . Surprisingly , this indicated that there was already little improvement in the performance of the model when more than 250 data points were used ( S1a and S1b Fig ) , and that relatively few iterations of the fitting algorithm were required ( S1c Fig ) . To account for temporal non-stationarities in the neural response , we then compared the GP model to an extended model with input warping ( see Methods ) . One assumption of classical GP models is that the function space has a stationary autocorrelation function , i . e . that its correlational structure does not change with respect to a predictor , such as time . However , light induced neural activity like responses to the chirp , which have highly non-stationary correlational structure , are likely to show a commensurate non-stationarity in the response . We computed a warping function which transforms the time dimension such that the stimulus input has a stationary autocorrelation structure ( Fig 3a ) . We then used this warping function to transform the input to the GP model of the response , under the assumption that the correlational structure of the response matched that of the stimulus input [12 , 13] ( Fig 3b–3d ) . By performing this extra processing step , we were able to fit a model which could vary in its autocorrelation . Our results show a clear difference between the predictions of the warped GP model and the simpler stationary one . In the simpler model , the selected parameters reflect a trade-off between models which fit closely to each of the different stimulus components ( i . e . steps vs . chirps ) , resulting in an inferred mean signal which appears noisy during the light step and poorly tracks the faster chirp oscillations . As a consequence , the inferred uncertainty was relatively stationary over time ( Fig 3e ) . By contrast , in the warped GP model , the inferred mean signal during the light step was smoother and tracked the faster oscillations much more closely ( Fig 3f ) . More importantly , in contrast to the interpolated signal derived by a classical pipeline , the warped GP infers a high level of uncertainty during periods of rapid oscillation which are at , or close to , the sampling limit of the recording . In practice , we found that the approach described above was more stable and faster than inferring the autocorrelation function directly from the observed activity . This appeared to be due to two factors: the irregular sampling distribution of the observed activity and the observation noise . Estimating the autocorrelation function separately for each ROI added a considerable computational burden to the pre-processing pipeline . In principle , the approach demonstrated can be applied to any stimulus with a known time-course if it is spatially homogeneous . Where this is not the case , the temporal statistics of the observed response may also be influenced by spatial integration and an alternative model , which explicitly accounted for this , would be appropriate . The key benefit of the GP framework is that it provides an explicit estimate of the uncertainty about the neural activity which can be used to perform well calibrated statistical inference , e . g . for inferring which periods of neural activity differed between two conditions . This is in contrast to classical approaches , where typical analysis follows multiple smoothing steps and often only the inter-trial variability is considered , providing a poorly calibrated estimate of uncertainty . In our framework , we use a GP equality test to identify whether two signals are statistically different [14] . As an example , we consider the response of bipolar cells to the chirp stimulus as a function of the spatial extent of the light spot . This is known to modulate bipolar cell responses , with the difference being induced by lateral inhibition [7 , 15–17] . We compared the calcium and glutamate signals of bipolar cells presented with chirp stimuli whose light spots differed in size ( 100μm and full field ) . We fitted a GP model with time warping to each of the sets of observations ( Fig 4a and 4b ) , performed maximum likelihood estimation to optimise the GP parameters with respect to the data , and then computed the difference between the estimated latent functions . We identified the periods of activity where the stimulus drives greater differences in the response than would be expected by chance ( defined as the three standard deviations around the estimated difference function not including zero ) . We found the number of disconnected regions where the difference is greater than could be expected by chance , which is called the Euler characteristic ( EC ) . It provides a measure of the strength of the difference between two signals ( Fig 4c ) and depends on the number of standard deviations chosen as a threshold . To estimate whether the EC was higher than expected by chance for a given threshold , we developed a bootstrap procedure for the GP models . We approximated a null distribution of the EC by shuffling the observed activity between the two conditions , and calculated an empirical p-value with respect to this null distribution of the EC . If we assume a fixed threshold for calling two regions in the signal different ( e . g . three s . d . ) , we did not find a significant difference between the two stimulus conditions ( bootstrap: p ∼ 0 . 103 , α = 0 . 01 ) for the calcium recordings , but for the glutamate recording ( bootstrap: p ∼ 0 , α = 0 . 01 ) . Significant differences occurred during the light step and in both oscillatory sequences . The shuffle test can also be evaluated for the whole range of thresholds . For comparison , we computed a similar test using the classical analysis pipeline , using inter-trial standard deviation as an estimate of the uncertainty associated with the mean signal . For the bootstrap procedure shuffled the interpolated data between the two stimulus conditions to approximate the null distribution . At the same threshold as above , for neither the calcium ( bootstrap: p ∼ 0 . 062 , α = 0 . 01 ) nor the glutamate ( bootstrap: p ∼ 0 . 062 , α = 0 . 01 ) recording was the EC found to be significantly elevated . It should be noted that the p-values estimated for the GP and classical pipeline are not directly comparable: the classical approach does not distinguish between observational and stimulus driven variability , rather identifying whether observed differences are greater than inter-trial variability . The choice of a fixed threshold for inferring whether two signals are statistically distinct may result in overly conservative statistical estimates . While there were no thresholds for which the classical approach inferred an EC greater than expected from the null distribution , for the GP models of the calcium and glutamate signals there were a range of thresholds for which the EC was greater . These ranges differed between the two signals , which may relate to the effect of the physiological properties of these two signals or to the kinetics of their respective indicators on the dynamics of the observed signals . We recommend that the selection of a threshold be guided by the particular needs of the analysis task , not to keep to statistical conventions developed for other methods . We next show how GP equality tests can be used to provide a principled criterion for choosing the number of clusters in a hierarchical clustering of light responses . For example , in a single imaging plane , one may wish to know whether the observed responses originate form distinct functional groups , perhaps due to the presence of multiple cells or cell types within the recording plane , or multiple neurites of the same cell acting independently [18] . This pipeline was composed of two stages , firstly identifying putative clusters , then evaluating the evidence for different cluster configurations . In the first stage , a GP was estimated for each ROI in GCaMP6f labelled bipolar cell axon terminals in a PCP2 mouse line ( data previously published in [7] ) . Then , we hierarchically clustered the mean signals from each GP to identify putative clusters among the set of responses , using the Ward algorithm and Euclidean distance ( Fig 5a ) . Each node in the hierarchy then corresponded to a hypothesis about whether a particular cluster should be partitioned into two sub-clusters ( Fig 5b ) . In the second stage , we start at the top of the clustering . At each node , we fit two GPs to the data from all ROIs assigned to each of the two clusters independently . We then tested the hypothesis that the two clusters were different using a GP equality test with the EC as the measure of dissimilarity ( Fig 5c–5f ) . A null distribution for the Euler Characteristic was approximated by a further bootstrap test , where the pair of signals for which the null distribution of the EC was calculated were drawn at random from the pooled observations at each node . This process continued iteratively through the hierarchy , terminating when the Euler Characteristic for a split in two new clusters was not greater than 99 . 5% of the null distribution at that node . Interestingly , the first node ( N0 ) separated ROIs belonging to two bipolar cells in the imaging field , with strong quantitative backup for the split ( bootstrap: p ∼ 0 at three s . d . , α = 0 . 01 , Fig 5g ) . The split at the second node ( N1 ) was also accepted ( bootstrap: p ∼ 0 , α = 0 . 01 at three s . d . , Fig 5h ) , which separates ROIs of the left bipolar cell into two groups , indicating potential sub-clusters within the terminals of a single bipolar cell . Subsequent separations were rejected ( N2 , bootstrap: p ∼ 0 . 68 , α = 0 . 01 at three s . d . , Fig 5i ) . The difference observed within the terminal systems of these cells may reflect functional variation within the output of a single bipolar cell [19] . Were this difference to exist , it would likely be a consequence of differential inhibition from amacrine cells , and represent an additional layer of complexity in the functional parallelisation of retinal signalling . While our analysis is suggestive of this conclusion , verification is beyond the scope of this study . We next extended our GP framework to study the effect of multiple stimulus parameters and their interactions on the latent neural activity in an ANOVA-like framework [20] . GP-ANOVA models posses multiple kernels , each of which models the effect of a predictor or an interaction between predictors . In contrast to classical ANOVA , the interaction effects can have non-linear structure [20] , and it is possible to compute not merely the strength of particular effects but also an inference of the response of a ROI over time as a stimulus feature varies . To demonstrate the usefulness of this extension , we fitted a GP model to predict the response of a single ROI to a light stimulus where light intensity was modulated as a sine wave of varying frequency and contrast ( Fig 6a ) . The input for this model was a predictor matrix where each column corresponded to one of the stimulus parameters , including two columns jointly encoding phase as a circular feature , and one each for frequency and contrast ( see Methods ) . For the experiments , we selected 150 stimulus parameters using blue noise sampling , such that parameters were uniformly selected from the parameter space and excluded if they were below certain thresholds for frequency ( < 1Hz ) or contrast ( < 10% ) or too close to an already existing stimulus parameter . Although the frequency and contrast parameters are fixed during each one second trial of the sine stimulus , the model can accommodate parameters which vary continuously over time , by encoding this change in the columns of the predictor matrix . As in a classical ANOVA , there are many possible ways in which the effects of these stimulus parameters can be incorporated into the model . In this case , stimulus features were encoded in the kernel ( see Methods ) , either as additive independent effects of phase , contrast or frequency , or through multiplicative interactions between the features . The cost of adding more kernels with a fixed amount of data is that the uncertainty associated with each parameter increases as the number of parameters to be learned grows . To compensate for this , we performed kernel selection through a two stage iterative process ( Fig 6b ) . The first stage identified the kernel which , when included , most strongly improved model performance , as measured by the log marginal likelihood . Once there were two or more parameters , each new kernel had to contribute a greater improvement to the model performance than could be expected by chance , as established by a likelihood ratio test ( see Methods ) . If a kernel was accepted it was retained in the model in the consecutive iterations ( for an overview of the models evaluated in this pipeline , see S1 and S2 Tables ) . We fitted a GP model to the glutamate signal of a single ROI in the IPL in response to the sine stimulus using this procedure . After three iterations the improvement in model performance was less than the required ratio . We tested one further iteration which also returned a negative result , and the process ceased . The kernels which were accepted included an interaction kernel between all three parameters and a frequency-contrast kernel ( Λ = 11 . 25 , p < 0 . 001 ) . A frequency kernel ( Λ = 4 . 01946568 , p = 0 . 045 ) was rejected in the third stage , and a phase kernel was rejected ( Λ = 2 . 29 , p = 0 . 130 ) . We then used the model to predict neural activity for unseen parameter combinations and quantified how uncertain our predictions about the activity in response to these were [21–23] . Intuitively , the model should have the least uncertainty about stimulus parameters which had been observed . Uncertainty then should increase as a function of the distance from the observed parameters . We quantified uncertainty by computing the expected response of the ROI and taking the sum of the latent variance ( Fig 6c ) . For the studied cell , calcium recordings to stimulation with the chirp stimulus were also available ( Fig 6d ) , and we compared the model fitted directly to the chirp response data to predictions from the model fitted to the sine data . There were some qualitative similarities between the two models , such as the overall amplitude of the signal , and the decrease in signal amplitude as the frequency of the stimulus increased . The prediction that the signal amplitude would slightly increase with contrast was not reflected in the chirp data , where the relationship was more ambiguous . The quality of prediction of the activity at high frequencies was difficult to evaluate , as there is a high-level of uncertainty about the mean signal at those frequencies . One factor to consider with regards to this direct comparison is that the differences in the chirp responses may be due to temporal dependencies over time . A critical advantage of our framework is that we can use it for Bayesian experimental design . This is useful , as in 2P imaging experiments time is usually severely limited . For example , isolated mouse retinal tissue becomes unresponsive to light stimulation in a matter of hours , and single recording fields often bleach within half an hour of recording . To efficiently explore the space of possible stimulus features under severe time constraints is thus a critical problem , which GP models can be used to address [21 , 22] . To show how this works , we performed an experiment using GP models to guide parameter selection . In retinal tissue expressing iGluSnFR we selected a single ROI , likely representing a single bipolar cell axon terminal . We used two control stimuli to evaluate the parameter selection: a local chirp stimulus playing over three trials , to which we fitted a warped GP , and a sinusoidal stimulus with 90 parameters uniformly sampled from the parameter space ( Fig 7a ) , to which we fitted a GP with the kernels derived in the previous likelihood ratio procedure . We performed three rounds of active parameter selection , starting with 30 uniformly sampled parameters in the first iteration , fitting the GP and using parameters selected by identifying 30 peaks in the uncertainty map in the subsequent two iterations . We then used the models from each iteration to predict how the ROI would respond during the oscillatory components of the chirp stimulus ( Fig 7b ) . Parameters selected using the active approach were more broadly distributed across the parameter space , although we noted that the peak finding algorithm was biased away from the edges . In the purely random design procedure , parameters often clustered and there were large empty regions , resulting in high uncertainty in these regions . Neither the random nor the active parameter procedure inferred a good prediction of the contrast-varying chirp component , which in the case of the active parameter inference was likely due to the bias away from the periphery of the parameter space , resulting in very few samples in the proximity of the 8Hz parametric edge . At lower frequencies the experimental design algorithm seemed better able to capture qualitative aspects of the chirp response , such as the decrease in response amplitude as the frequency increased , though again the lack of samples at the very highest frequencies resulted in a high level of uncertainty . We finally constructed a model which combined stimulus effect modelling and hierarchical clustering into a single framework . We fitted the model to calcium recordings of RGC activity in response to a bright bar moving in different directions on a dark background . RGCs show different response polarities and a large range of response kinetics to this stimulus [11] and some modulate the response amplitude as a function of stimulus direction . The model incorporated the stimulus features of time and direction as additive effects , alongside with a time-direction interaction effect ( Fig 8a and 8b ) . The data were then sorted using hierarchical clustering ( Fig 8c and 8d; S2 Fig ) and for the purpose of demonstration the first three nodes of the hierarchy were tested using GP equality tests ( Fig 8e–8h ) . The algorithm first separated ON and OFF responses into separate clusters ( N0 , bootstrap: p ∼ 0 at three s . d . , Fig 8i ) . The ON cluster was then further divided into sustained and transient responses ( N1 , bootstrap: p ∼ 0 at three s . d . , Fig 8j ) . The sustained ON responses were finally separated into direction selective and non-direction selective clusters ( N2 , bootstrap: p ∼ 0 . 01 at three s . d . , Fig 8k ) . We did not test further splits for significance .
Accurately characterising the variability of neural responses is essential for understanding neural coding . Noise manifests itself throughout sensory systems and presents a fundamental problem for information processing [2] . While imaging ex-vivo retinal tissue does not present some of the challenges as in vivo cortical recordings ( where movement is a significant source of variability ) , two-photon imaging in ex-vivo tissue is still subject to many sources of variance , due to fluctuations in biosensor excitation and photon detection , among other factors . This issue may be particularly acute for two-photon imaging of retinal tissue , where it is necessary to keep the excitation energy low to avoid laser-evoked responses , which may result in lower overall fluorescent signals relative to in vivo recordings of non-light-sensitive tissue . Computational processing can introduce further variability , e . g . due to the discretisation of the measured signal . This is rarely acknowledged , perhaps due to the convenience of standard approaches . In principle , splines in combination with generalized additive models ( GAMs , e . g . [24] ) provide an alternative framework to perform uncertainty aware analysis of calcium imaging data . Exploring and contrasting this to the GP framework introduced here is beyond the scope of this paper . Classical GP models can be computationally costly due to the need to compute the inverse of the kernel matrix involving all training data [4] . To make practical use of GPs for modelling large 2P recordings , we capitalized on recent advances in sparse approximations for GPs that work with a limited number of inducing points [5] and demonstrated their applicability for a real world task . In addition , we only performed point-estimates for hyperparameters instead of fully Bayesian inference and pre-determined kernels before statistical evaluation . This was important for two reasons: firstly , a processing pipeline should not be excessively computationally costly , so as to make them impractical for general use with larger datasets; and secondly , the application of these models in closed-loop imaging experiments was only possible if one complete iteration of the process ( data acquisition; pre-processing; prediction; parameter selection ) could be completed in a few minutes . In principle , our approach could be extended to a fully Bayesian framework with hyperpriors on the model parameters , although this introduces additional difficulties for sparse approximation and still entails a greater computational burden [25] . While our work solely addressed Gaussian distributed data , the models can be readily extended to point processes as well . There , sparse approximation techniques overcome the computational intractability of the model , and allow inference on relatively large datasets [26] . Although we demonstrated the potential for using GP models during 2P experiments , there were several limitations to our approach . We were able to reduce the time per iteration of our active experiments to less than five minutes , addressing a key practical concern . However , it emerged during the experiment that the peak-finding algorithm was biased away from the periphery of the stimulus space , which made the chirp stimulus unsuitable as our “ground truth” for model evaluation . The parameter batch size may also have been too small for each iteration . Batch size is a critical consideration in active Bayesian experimentation . Where the cost per iteration is low , single parameters can be selected for each iteration , for which the objective function can be relatively easily defined and evaluated . In one recent publication , Charles et al . [23] used GPs to model the effect of inter-trial variability in monkey V1 neurons , using sequential parameter selection to optimise a coloured light stimulus . For experiments where iterations are prohibitively expensive , new parameters can be selected in batches , although this requires interactions between parameters to be taken into account , which can be computationally expensive to evaluate . In such cases , approximate methods provide an attractive method for reducing computational overheads ( e . g . [22] ) . Batch parameter selection algorithms which account for , or approximate , parameter interactions would likely overcome simple peak finding methods . Historical obstacles to the use of Bayesian methods such as the difficulty of their implementation and their computational cost have been reduced . Much research over the past decade has focused on the problem of minimising the computational complexity of the algorithms through sparse approximation methods and efficient parameter estimation ( such as [5] ) . New libraries for popular coding languages—such as GPy [27] , PyMC3 [28] , and pySTAN [29] for Python 3—have lowered the barrier to entry . Likewise , we provide a collection of notebooks with this paper to allow straightforward application of our framework . Taken together , our approach exploits the flexibility and extensibility of Gaussian process models to improve on classical approaches for two photon data analysis and addresses important analytical tasks in a way that preserves a representation of uncertainty propagated up from the underlying data . We feel that it will be particularly useful for disentangling the dynamics of neural circuits in the early visual system under complex , multivariate experimental conditions .
All animal procedures were performed according to the laws governing animal experimentation issued by the German Government . The documentation for the animal and tissue preparation was submitted in accordance with Mitteilung nach §4 Abs . 3 Tierschutzgesetz , and approved by the Regierungspräsidium Tübingen on 09 . 11 . 2016 . Viral injection documentation Tierversuch Nr . AK6/13 was appraised by the ethics committee and approved by Regierungspräsidium Tübingen , on 05 . 11 . 2013 . For single-cell-injection experiments , we used one adult mouse cross-bred between transgenic line B6 . Cg-Tg ( Pcp2-cre ) 3555Jdhu/J ( Tg3555 , JAX 010536 ) and the Cre-dependent red fluorescence reporter line B6;129S6-Gt ( ROSA ) 26Sortm9 ( CAG-tdTomato ) Hze/J ( Ai9tdTomato , JAX 007905 ) . For glutamate-imaging , we used one adult C57BL/6J mouse . Owing to the exploratory nature of our study , we did not use blinding and did not perform a power analysis to predetermine sample size . Animals were housed under a standard 12h day-night cycle . For recordings , animals were dark-adapted for ≤ 1h , then anaesthetised with isoflurane ( Baxter ) and killed by cervical dislocation . The eyes were removed and hemisected in carboxygenated ( 95% O2 , 5% CO2 ) artificial cerebral spinal fluid ( ACSF ) solution containing ( in mM ) : 125 NaCl , 2 . 5 KCl , 2 CaCl2 , 1 MgCl2 , 1 . 25 NaH2PO4 , 26 NaHCO3 , 20 glucose , and 0 . 5 L-glutamine ( pH 7 . 4 ) . Then , the tissue was moved to the recording chamber of the microscope , where it was continuously perfused with carboxygenated ACSF at ∼ 37°C . The ACSF contained ∼ 0 . 1μM sulforhodamine-101 ( SR101 , Invitrogen ) to reveal blood vessels and any damaged cells in the red fluorescence channel . All procedures were carried out under very dim red ( >650nm ) light . Sharp electrodes were pulled on a P-1000 micropipette puller ( Sutter Instruments ) with resistances between 70–100MΩ . Afterwards , the tip ( ∼ 500μm ) of each electrode was bent on a custom-made microforge . Single bipolar cell somata in the inner nuclear layer were filled with the fluorescent calcium indicator Oregon-Green BAPTA-1 ( OGB-1 ) by using the pulse function ( 500ms ) of the MultiClamp 700B software ( Molecular Devices ) . OGB-1 ( hexapotassium salt; Life Technologies ) was prepared as 15mM in distilled water . Immediately after filling , the electrode was carefully retracted . Imaging started after about 30 minutes after the injection to allow cells to recover and the dye to diffuse within the cell . At the end of the recording , a stack of images was captured for the cellular morphology , which was then traced semi-automatically using the Simple Neurite Tracer plugin implemented in Fiji [30] . For virus injections , we used adult wild-type mice ( C57BL/6J ) . Animals were anesthetized with 10% ketamine ( Bela-Pharm GmbH & Co . KG ) and 2% xylazine ( Rompun , Bayer Vital GmbH ) in 0 . 9% NaCl ( Fresenius ) . A volume of 1μl of the viral construct ( AAV2 . hSyn . iGluSnFR . WPRE . SV40 , Penn Vector Core ) was injected into the vitreous humour of both eyes via a Hamilton injection system ( syringe: 7634-01 , needles: 207434 , point style 3 , length 51mm , Hamilton Messtechnik GmbH ) mounted on a micromanipulator ( World Precision Instruments ) . Imaging experiments were performed 3 weeks after virus injection . We used a MOM-type 2P microscope ( designed by W . Denk , now MPI Martinsried; purchased from Sutter Instruments/Science Products ) . The design and procedures have been described previously [7 , 11 , 31] ) . In brief , the system was equipped with a mode-locked Ti:Sapphire laser ( MaiTai-HP DeepSee , Newport Spectra-Physics ) , two fluorescence detection channels for OGB-1 or iGluSnFR ( HQ 510/84 , AHF/Chroma ) and SR101/tdTomato ( HQ 630/60 , AHF ) , and a water immersion objective ( W Plan-Apochromat 20x /1 . 0 DIC M27 , Zeiss ) . The laser was tuned to 927nm for imaging OGB-1 , iGluSnFR or SR101 . For image acquisition , we used custom-made software ( ScanM by M . Müller , MPI Martinsried , and T . Euler ) running under IGOR Pro 6 . 3 for Windows ( Wavemetrics ) , taking time lapsed 32 x 32 pixel image scans ( at 15 . 625Hz ) or 16-line “spiral” scans ( at 31 . 25Hz ) . For documenting morphology , 512 x 512 pixel images were acquired with step size of 0 . 5μm along the Z axis . To resolve transient changes in calcium concentration or glutamate release ( i . e . with decay times of ∼100ms ) , scan rates of around 20Hz or more are wanted . Many scanning 2P microscopes use conventional ( non-resonant ) galvanometric scanners and are limited by the inertia of the scan mirrors , which introduce positional errors at high scan rates . This is especially critical for typical linear ( image ) scans , with their abrupt changes in direction when jumping between scan lines . For constant spatial resolution , faster scan rates are often realised by decreasing the scan area . However , it is possible to increase the spatio-temporal resolution by using non-linear “spiral scan” configurations . These overcome the key mechanical limitation of linear scans , that they incorporate sharp turns , rather than following smoother trajectories . Unlike linear scans , which are composed of single linear trajectories repeated along an axis at regular intervals , spiral scan configurations consist of radial trajectories moving away from a central point at a constant speed and rotation and permit rapid movement of the scan mirrors . A regular radial grid can be constructed by generating a single spiral trajectory and successively rotating it around a central point . We used an Archimedean spiral is used to generate each trajectory ( r = Θ1/a ) , where the radial distance r from the central point is a function of the angle Θ and a tightness parameter a which determines the rate of rotation around the centre . With a grid composed of 16 such curves we can resolve , for instance , axon terminals of retinal bipolar cells at twice the spatial and twice the temporal resolution of linear recordings . One can see the advantages of such scan configurations by showing how frequently the scan trajectory intersects with ROIs in a single frame . The times at which labelled structures are observed by these trajectories are both more frequent and more irregularly distributed in time than a typical linear scan , providing a superior temporal resolution . For light stimulation , a modified LightCrafter ( DLPLCR4500 , Texas instruments; modification by EKB Technology ) was focused through the objective lens of the microscope . Instead of standard RGB light-emitting diodes ( LEDs ) , it was fitted with a green ( 576nm ) and a UV ( 390nm ) LED for matching the spectral sensitivity of mouse M- and S-opsins [32] . To prevent the LEDs from interfering with the fluorescence detection , the light from the projector was band-pass-filtered ( ET Dualband Exciter , 380-407/562-589 , AHF ) and the LEDs were synchronised with the microscope’s scan retrace . Stimulator intensity was calibrated to range from 0 . 5 * 103 ( “black” background image ) to 20 * 103 ( “white” full field ) photoisomerisations P*/s/cone [7] . The light stimulus was centred before every experiment , such that its centre corresponded to the centre of the recording field . In linear scans , the stimulus is displayed while the trajectory moves between consecutive lines; while for the spiral scans this occurs while the trajectory returns from the periphery to the centre . Light stimuli were generated using the QDSpy light stimulation software , which is written in Python 3 [33] . The chirp stimulus ran for 4 repeats of 32s each , with the stimulus extent alternating between a 800μm and a 100μm light spot . The moving bar stimulus consisted of a 300μm rectangular bar moving at 1000μm/s for 4 seconds along 8 evenly space directions , repeated three times for each direction . The sine stimulus consisted of a 100μm light spot , and ran for 45 1s-trials , with contrast and frequency varying in each trial . The contrast and frequency parameters were chosen by blue-noise sampling 150 parameters from the parameter space , between 10% and 100% contrast and 1Hz to 8Hz frequency . Later closed-loop experiments used a sine stimulus with 90 parameter sets sampled uniformly from the parameter space , in addition to 3x30 parameters sets , of which the first were chosen from random uniform sampling and the latter two sets by active Bayesian inference . Initial data analysis was performed in IGOR Pro 6 . Regions of Interest ( ROIs ) were defined manually using the SARFIA toolbox for IGOR Pro [34] . In the iGluSnFR recordings , a custom-script generated a correlation map [7] , which defined structures for the ROI drawing . The observations were synchronised to the light stimuli using time markers which were generated by the stimulation software and acquired during imaging . Once the initial pre-processing was completed , the data was exported to HDF5 files , and all subsequent analysis was performed in Python 3 . 5 . Gaussian process ( GP ) models were used to infer the relationship between time , stimulus parameters and the observed activity of each ROI . Thus , the predictor matrix X was a function of the stimulus parameters and time , short hand referred to as θ . An introduction to the mathematics of GP regression is provided in Box 2 . All GPs used the Radial Basis Function ( RBF ) kernel , with additive Gaussian noise . k R B F , ϕ ( X , X ′ ) = σ s i g n a l 2 exp ( - ∥ X - X ′ ∥ 2 2 l 2 ) + I σ n o i s e 2 ( 10 ) ϕ = { l , σ s i g n a l , σ n o i s e } ( 11 ) The lengthscale l , signal variance σsignal and noise variance σnoise were learned as part of the model optimisation . Since the fluorescence measurements Fi , θ for ROI i were irregularly spaced in time , the mean μ and covariance Σ of the signal Fi were inferred for a new set of predictors X* where time is regularly spaced: μ ϕ ( X * | X ) = k ϕ ( X * , X ) ( k ϕ ( X , X ) + I σ n o i s e 2 ) - 1 F i , θ ( 12 ) Σ ϕ ( X * | X ) = k ϕ ( X * , X * ) - k ϕ ( X * , X ) ( k ϕ ( X , X ) + I σ n o i s e 2 ) - 1 k ϕ ( X , X * ) ( 13 ) The additive noise component I σ n o i s e 2 was removed for statistical inference , and we refer to the resultant noise-free GP as the “latent function” , in line with the terminology in the GPy documentation [27] . Confidence intervals were calculated as μ ϕ ( X * ) ± 3 * d i a g ( Σ ϕ ( X * ) ) ( 14 ) The Gaussian process models were developed in the GPy framework [27] . Feature encoding , input warping , equality tests , parameter selection and closed-loop parameter selection were computed using custom scripts , which we provide as supplementary content to this document and online at https://github . com/berenslab/bayesian_2p_pipeline . Hierarchical clustering was performed using scripts from the Scipy library , using Euclidean distance , the Ward algorithm and maxclust as the criteria [35] . The Ward algorithm was chosen as it tends to infer balanced clusters across the hierarchy . Adaptive parameter selection used a local peak finding algorithm from the Scikit-Image library . Since our datasets included several thousand observations , it was necessary to use sparse approximation methods to fit the GP models . The sparse approximation algorithm provided in GPy follows [5] , whereby the kernel is approximated using a subset of the data , termed the inducing inputs . The selection of the inducing inputs is learned as part of the model optimisation , selecting the inputs which minimise the KL-Divergence between the approximation and the target distribution . Details are provided in [5] . The Gaussian process equality test establishes whether two functions modelled by GPs are equal [14] . It operates by computing the difference between the two distributions and identifying whether the credible region encompasses the zero vector across the complete domain of the predictors . If the zero vector is outside of these intervals , we say the two functions are distinct with probability 1 − a . The probability is calculated using the mean μ* and covariance k* of the posterior of our two functions , excluding their respective noise components from the estimate of the covariance . μϕΔ ( X*|X ) μϕTΣϕΔ ( X*|X ) ΣϕμϕΔ ( X*|X ) ≤χ2 ( 1−a ) ( 15 ) μ ϕ Δ ( X * | X ) = μ ϕ 1 ( X * | X ) - μ ϕ 2 ( X * | X ) ( 16 ) Σ ϕ Δ ( X * | X ) = Σ ϕ 1 ( X * | X ) + Σ ϕ 2 ( X * | X ) ( 17 ) The total number of discrete , non-intersecting regions where two Gaussian processes differ more than could be expected by chance is termed the Euler characteristic ( EC ) [36] . The EC is a measure of the geometry of random fields which accounts for the smoothness of the underlying functions , and is well established in fMRI research , where it forms part of the broader literature on statistical mapping [37] . While the expected value of the EC can be analytically tractable under certain conditions , we wished to incorporate it into our pipeline in a manner which was not sensitive to the number of input dimensions and could handle non-stationary autocorrelation functions , and so inferred its null distribution through bootstrap resampling . To evaluate whether the values of the EC , which were obtained from the GP equality tests , were statistically significant , we constructed an approximate null distribution by bootstrapping samples from pooled data and performing equality tests on these samples . The procedure was as follows: the observations from each of the signals being compared were pooled to form a larger set of observations; from this set , pairs of samples each 300 observations in size were drawn at random , without replacement; Gaussian processes were fitted to each of the samples in the pair; the difference between the two Gaussian process models was calculated; the Euler characteristic was calculated from this difference for varying thresholds . This was repeated 500 times to build an approximate null distribution . We applied this bootstrap test in Figs 4 , 5 and 8 . For Fig 4 , the observations were pooled from the responses to the stimulus; for Figs 5 and 8 , for each node the observations were pooled from the two putative clusters . For the comparisons to the classical pipeline , the null distribution was computed by shuffling the observations between stimulus conditions , with a total of 500 shuffled pairs used for the estimation . Approximate p-values were computed by calculating the proportion of the N shuffled sample pairs which had a greater EC value than that calculated from the GP equality test . To address non-stationarity of the chirp response data , we computed the autocorrelation function for the chirp stimulus in 500 ms windows spaced at 1/16 s intervals ( 512 windows total ) . As we used RBF kernels for our regression , we fitted a Gaussian curve to each autocorrelation function and retained the inferred lengthscale lt for each window . A further parameter A modulates the height of the function . c o v ( f s t i m u l u s ) ∼ A e ( x - μ ) 2 2 l t 2 ( 18 ) If the signal were stationary , we would observe that the lengthscale parameter was constant with respect to time . By using the cumulative sum of the inverse of the lengthscale as the predictor , we could derive a warping function which transformed the predictors such that the stimulus autocorrelation was stationary . We assumed that the autocorrelation of the observed signal was approximately equal to that of the light stimulus input , and used the warping function to transform the observations . This transformation could be inverted to visualise the fitted GP with respect to the original time base . f w a r p e d ( x t ) ∼ 1 2 l t 2 ( 19 ) GP models can also be used for functional Analysis of Variance [20] . These GP-ANOVA models disentangle the contribution and interaction of different predictors to the observed function . The GP models for the chirp stimulus data modelled the observed activity with time relative to the start of each stimulus trial as the predictor X . For the moving bar stimulus , a direction parameter was encoded as a 2D circular feature by converting the angle α in polar coordinates to an xy position in Cartesian coordinates ( cos ( 2πα/360 ) , sin ( 2πα/360 ) ) . Likewise , for the sine wave stimuli the phase of the oscillation was encoded as ( cos ( 2πtf ) , sin ( 2πtf ) ) , while frequency and contrast were encoded linearly . In contrast to classical ANOVA , the effects and interactions can be non-linear . Flexible kernel composition makes such models comparatively simple to implement . Kernels can be combined in a number of ways [4] , each expressing some belief about the effect of a parameter , most commonly by taking the sum or product of two kernels . Additive components represent effects of predictors which are independent of one another , while multiplicative kernels represent interactions between predictors . For example , for a kernel encoding two stimulus parameters xa and xb , with both additive and interactive effects and RBF kernels , the correlation function of the GP model would be: k ϕ ( X , X ′ ) = k ϕ ( x a , x a ′ ) + k ϕ ( x b , x b ′ ) + k ϕ ( X a , b , X a , b ′ ) ( 20 ) Here , Xa , b = ( xa , xb ) . We estimated interaction effects of different stimulus parameters in our GP ANOVA models by including kernels which learned a single lengthscale parameter over multiple input dimensions . This inferred the joint effect of the parameters as a single function; where , since the parameters are z-scored , a change in the magnitude of one stimulus parameter would have the same effect as varying the other by an equal magnitude . This approach to GP ANOVA provides an efficient and principled way of choosing optimal hyperparameters to infer stimulus effects . For the chirp stimulus , where there is one predictor , a single kernel encoding the autocorrelation of the signal over time was used . For the warped GPs , the warped time was used instead . For the moving bar and sine wave stimuli , additional kernels were included to model the effects of their respective parameters . The GP model for the moving bar responses included both additive effects for time and direction , and a time-direction interaction effect . Likelihood ratio tests were used to select kernels from the full set of additive and multiplicative stimulus effects: χ 2 = - 2 l n ( L 0 L 1 ) ( 21 ) Where LN is the likelihood of the fitted model , and the addition of the proposed parameter is rejected if the improvement in the likelihood is greater than chance with probability 1 − a . These tests were applied iteratively until a kernel was rejected . For the closed loop experimentation , we retained the model from the previous selection procedure with the randomly parameterised sine stimulus . The data used throughout this paper and corresponding code used to compute the models will be provided as supplementary material alongside this paper .
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There are many sources of noise in recordings of neural activity , and the first challenge in neural data analysis is to separate this noise from experimentally relevant variation . This is particularly problematic for two-photon imaging data . Two-photon imaging uses fluorescent indicators to measure changes in the concentration of molecules involved in cell signalling , and adds a variety of numerical , biological and optical noise sources . We present a method for disentangling this signal and noise using Gaussian processes , a family of probabilistic models which provide a principled way of inferring mean activity and variability . In addition to signal recovery , we show that these models can test the evidence for whether and where two signals are different and that these tests can be used to look for groups in sets of signals . We explore how these models can be extended to predict how signals will change under different experimental conditions , and that these predictions can be used to select new conditions for further exploration .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
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"fluorescence",
"imaging",
"cluster",
"analysis",
"neurochemistry",
"statistical",
"noise",
"statistics",
"hierarchical",
"clustering",
"neuroscience",
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"cellular",
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] |
2019
|
Bayesian hypothesis testing and experimental design for two-photon imaging data
|
The relationship between the underlying contact network over which a pathogen spreads and the pathogen phylogenetic trees that are obtained presents an opportunity to use sequence data to learn about contact networks that are difficult to study empirically . However , this relationship is not explicitly known and is usually studied in simulations , often with the simplifying assumption that the contact network is static in time , though human contact networks are dynamic . We simulate pathogen phylogenetic trees on dynamic Erdős-Renyi random networks and on two dynamic networks with skewed degree distribution , of which one is additionally clustered . We use tree shape features to explore how adding dynamics changes the relationships between the overall network structure and phylogenies . Our tree features include the number of small substructures ( cherries , pitchforks ) in the trees , measures of tree imbalance ( Sackin index , Colless index ) , features derived from network science ( diameter , closeness ) , as well as features using the internal branch lengths from the tip to the root . Using principal component analysis we find that the network dynamics influence the shapes of phylogenies , as does the network type . We also compare dynamic and time-integrated static networks . We find , in particular , that static network models like the widely used Barabasi-Albert model can be poor approximations for dynamic networks . We explore the effects of mis-specifying the network on the performance of classifiers trained identify the transmission rate ( using supervised learning methods ) . We find that both mis-specification of the underlying network and its parameters ( mean degree , turnover rate ) have a strong adverse effect on the ability to estimate the transmission parameter . We illustrate these results by classifying HIV trees with a classifier that we trained on simulated trees from different networks , infection rates and turnover rates . Our results point to the importance of correctly estimating and modelling contact networks with dynamics when using phylodynamic tools to estimate epidemiological parameters .
Understanding whether and how the transmission patterns of a pathogen are revealed by branching patterns in pathogen phylogenetic trees remains a challenging research question . Alongside the stochastic diversification of the pathogen on the short time scales of an infectious disease outbreak , branching patterns in the pathogen’s phylogenetic tree also depend strongly on the underlying transmission pattern [1] and the host contact structure , as these shape the pathogen’s reproductive opportunities . The role of networks in epidemic spreading has been studied extensively in past decades [2–12] . The topology of the host contact network plays a crucial role in setting the epidemic threshold , the epidemic size and the most effective interventions . Network properties also play a role in determining which individuals are at high risk of infection . Naturally , modellers seek to inform simulated networks with individual-level data from real populations . Respondent-driven sampling [13 , 14] , snowball sampling or questionnaires [15] are several approaches to gathering these data , but all are challenging: people do not always remember how many people they have been in contact with , and in some contexts ( such as injection drug use or sexual behaviour ) , contact is stigmatized or even illegal . As a result , individuals may not wish to report contacts to public health practitioners . Recently there has been interest in using genetic data from pathogens , together with phylogenetic and phylodynamic tools , to estimate the parameters of human contact networks [16–19] . This is appealing , in that data now accessible with high-throughput sequencing technologies ( pathogen sequences , at a level of resolution that makes detecting even small amounts of genetic variation feasible ) can reveal information about a fundamental population-level structure ( the network ) . Sequences can show patterns of descent , and pathogens transmitted directly from human to human need human contact networks to have descendants . Since networks are difficult to observe directly and phylogenetic trees in principle contain some information about them , researchers have used a variety of tools to relate pathogen phylogenetic trees to the underlying contact network’s degree distribution , connectivity and clustering [17 , 20] . This method has been of particular interest for HIV phylogenies [21–24] . Studies have reported varying strengths of the effect of the contact network on the phylogeny . For example , [25] found a very weak influence of the network’s clustering coefficient when the degree distribution is held constant , [26] studied the shapes of phylogenies from simulated genetic data and found a moderate influence of the underlying network degree distribution , though “clustering” in phylogenetic trees did not parallel the heterogeneity in the degree distribution , and network dynamics shape phylogenies as well . [21] found a relatively stong effect of the variance in degree distribution and of the average pathlength of the network on the shapes of phylogenies . Also , within-host viral diversity affects the link between network structures and phylogenies [23] , as do the basic reproduction number and other details of the process [27 , 28] . It is therefore reasonable to assume that details of timing of infection , in-host selection , selection at the population level and other factors may also affect the relationship between contact networks and phylogies . Human contact networks are self-organizing systems with certain general characteristics; one approach to modelling human host networks is to perform simulations that are able to reproduce those characteristics . Key characteristics include a short average pathlength ( small-world property ) [29] , clustering [30] and a scale-free ( or at least highly skewed ) degree distribution [31 , 32] . In particular , networks with a skewed degree distribution have received much attention for epidemic spreading , as they yield significantly different transmission patterns from a homogeneously mixed population . Depending on the transmission pathway , there is evidence that networks can have an exponential degree distribution [13 , 33] or a scale-free degree distribution , found in various social networks [34–36] , and in human contact networks [37–39] . The Barabasi-Albert model [40] in particular is a much-studied process by which scale-free degree distributions may emerge . It is based on the idea of preferential attachment: nodes attach preferentially to existing nodes that already have many links . Preferential attachment is a plausible rationale for many applications ( fame , publicity ) . It describes a constantly growing network , or a static network if the growth is halted . In contrast , human host contact networks are often dynamic , but may not be growing in size over time . Instead , they have population turnover [5 , 41] , with individuals entering and leaving a network as time goes on . Especially for chronic infections like TB , HCV or HIV [42] , people may enter and exit the network over shorter timescales than the length of the infectious period . The number of contacts that individuals accumulate over time is significantly larger than the number of contacts at one point in time . Furthermore , many of the observations underlying reports of scale-free degree distributions in human contact networks are derived from reports of the cumulative numbers of contacts that individuals have over a long period ( for example over one year [32 , 43] , or accumulated to date ) . Accordingly , it may not be appropriate to compare simulated transmission dynamics in models where individuals’ degrees are modelled from observed accumulated numbers of contacts to transmission where degrees are taken as the instantaneous ( or even shorter-term ) numbers of contacts . The static network ( with degrees modelled on data for the number of contacts accumulated over long time periods ) can be a very poor approximation of the true dynamic network; outbreaks can spread faster in such a static network due to the potentially very high numbers of simultaneous contacts . In using phylodynamic tools to estimate network parameters from pathogen phylogenies , it is typically assumed that the contact network is static in time; one seeks network parameters that produce pathogen phylogenetic trees that are similar to observed trees , conditional on the static network assumption ( and perhaps also on assumptions about the degree distribution , clustering patterns and other network attributes ) . Whatever the details , inferred quantities such as degree distribution , the average number of partners and the infection rate are influenced by assumptions about the network , including the static assumption . The duration of infectiousness and the time scale of the network dynamics must affect the relationship between pathogen phylogenies and network parameters . Clearly , no individual has thousands of contacts over a week; reports of degrees that are orders of magnitude higher than the average are from data aggregated over long time periods; where an infectious duration is of the order of weeks or a few months , the scale-free property is unlikely to hold . These issues are presented briefly in [26] and [44 , 45] . In this paper , we investigate the effect of human host network dynamics on pathogen phylogenies . Our study focuses on simulations , and on the relationship between network assumptions and estimates of transmission parameters . We compare simulated phylogenies from outbreaks on static and dynamic networks , and we explore the effect of the turnover rate at which individuals enter and leave the system . We also study the effect of the network characteristics on the phylogenies . For this , we use networks with binomial degree distribution and skewed degree distribution , as well as clustered and unclustered networks . We explore the effect of the infection rate and the mean number of contacts . We study how the features of the underlying networks affect phylogenetic trees with various tree statistics . Finally , we turn to phylodynamic inference of HIV transmission parameters and illustrate our main results using HIV sequence data from the Dutch ATHENA cohort and Los Alamos . In particular , we characterise the impact of alternative assumptions on human contact network dynamics on estimation of key transmission parameters including R0 .
We use an algorithm for a “skewed-clustered” network which generates a network with a skewed degree distribution and positive transitivity [38] . To understand what these features add , we also use skewed ( but not particularly clustered ) networks , and an Erdős-Renyi random network . These all have a stationary average number of contacts and stationary degree distribution , while people are entering and exiting the network . This entry and exit happens with a turnover rate δ , which is the ratio between the number of people entering per time step to the number of people in the network . Networks are simulated in discrete time . In each time step the following steps happen: In our simulations , we begin with one infected individual who then infects neighbours at a constant infection rate per contact , after which the neighbours can infect their respective neighbours in the next time step , and so forth . Infected individuals stay infected throughout the simulation , modelling a long-term infection . This simulates an outbreak on these dynamic networks . There is at least one time step between an individual becoming infected and infecting a neighbour , and we model a positive time between any two infection events by adding a small positive time to the infection events of one iteration , such that they occur with equal time lapses . We extract what would be the “true timed phylogeny” of the pathogen given the transmission tree in our network , under the assumption that hosts carry a single pathogen lineage . To do this we form a binary branching tree in which each host corresponds to a tip in the phylogeny and branch lengths correspond to time . Since we know the true transmission tree and its timing , this can be done by tracking the infectors , infectees and the time between infection events . This is available in the getLabGenealogy function in the R package PhyloTop [47] . The simulation of the outbreak is stopped after a time such that the phylogenetic trees all have the same number of tips . We compute features of the phylogenies with software sources listed in Table 1 . Number of substructures Cherries: Substructure consisting of two tip descendants Pitchforks: Substructure consisting of three tips Imbalance measures Sackin Index: Average number of internal nodes Ni between each tip i and the root of the phylogenetic tree S n = 1 n ∑ i = 1 n N i , [48 , 49] Colless Index: It compares the number of tips that descend on the left and right ( L and R ) from each internal node , and averages over these differences |L − R| [49 , 50] . Other tree measures Maximum Height: Maximum height of tips in the tree . Average Size of Ladder: Ladder structures [1] consisting of a connected set of internal nodes with a single tip descendant IL numbers: Number of internal nodes with a single tip child . Centrality measures and general network measures Maximum Betweenness: Maximum number of shortest paths that pass through a particular node . Wiener Index: Sum of the lengths of the shortest paths between all pairs of nodes . Maximum Closeness: Sum of lengths of the shortest paths between one node and all other nodes ( maximum thereof ) . Average Pathlength: Average distance between two nodes . Diameter: Longest possible path between two nodes in the tree . Tree measures that use the edge length Branching next index ( BNI ) : We compare the extent to which a node that branches at time t is chronologically next to branch; in other words , does branching now make it more or less likely that a node will branch next ? If a node’s child is chronologically next to branch following the node itself , we say the node has the ‘branching next’ property ( si = 1 ) . We add and rescale the sum of si over all internal nodes i in the tree ( except the root and the last node to branch ) . si is a Bernoulli random variable whose expected value is pi = 2/ki , where ki is the number of lineages in the tree that exist at time ti + ϵ , in the limit as ϵ → 0 , where ti is the time of node i and ϵ > 0 . We define the BNI as ∑ i s i - p i ∑ i p i ( 1 - p i ) Generalised branching next ( MNI ) : Extending the BNI concept , we ask whether one of the next m branching events ( chronologically ) in the tree descends from the current node , in which case we set di = 1 for node i . We sum and rescale di , as with si , over the tree to create this summary statistic . We let kij , j = 1 , … , m be the numbers of lineages immediately after the j′th branching event following node i ( in the entire tree ) . We define qi = ∏j ( 1 − 1/kij ) and normalise by setting MNI to ∑ i d i - q i ∑ i q i ( 1 - q i ) . Since now they are not independent we use every m′th node i rather than every node . Length statistics We use the mean of the path length from the internal nodes of the tree to its root , as well as the median , variance , skewness and kurtosis of this set of path lengths . We use two approaches to understand how the underlying contact network affects the tree features . The first is to visualise the results using principal components analysis ( PCA ) on the matrix of features described above . The matrix values are scaled such that the mean is zero , and normalized such that variance is 1 , as is standard in PCA . This visually illustrates the extent to which these features discriminate between phylogenetic trees derived from different contact networks . However , visual separation on a 2-dimensional PCA plot is a limited measure of how informative the features are of the contact network . Thus , we also explore this quantitatively using both K-nearest neighbours and random forest classification . We attempt to classify the network ( random , skewed or skewed-clustered ) based on the features . We assess accuracy in these binary and categorical classifications when the underlying network model correct , and when it is mis-specified . We also attempt to classify the transmission rate . For this goal we use trees from simulated outbreaks where we distributed the transmission rate β uniformly . We grouped these trees into bins depending on the underlying β and train classifiers on the tree features with the aim of predicting the bin of β for a test set . We study a scenario where turnover rate δ and mean degree d ^ are distributed uniformly , and a scenario where they are kept constant . Partial nucleotide HIV-1 polymerase sequences were obtained as described previously from patients in the ATHENA national observational HIV cohort in the Netherlands ( by June 2015 ) [52] . We used the first sequence per patient , with a minimum of 750 nucleotides length . No patient information was included in the analysis . Sequences were aligned with Clustal Omega 1 . 1 . 0 [53] and manually checked and adjusted . HIV-1 subtyping was performed with COMET v1 . 3 [54] and 6912 subtype B sequences were considered for further analysis . In addition we retrieved 19 , 459 HIV-1 subtype B sequences from the Los Alamos database ( by September 2017 ) [55] , with a minimum length of 1000 nucleotides overlap to the ATHENA alignment . Excluding sites with less than 75% coverage , and with IAS resistant mutations 2015 removed This resulted in a sequence alignment of 1 , 128 nucleotides length [56] ) . Viral phylogenies were reconstructed with FastTree version 2 [57] . From this tree we identify 90 non-intersecting clades in the specified size range 100-151 , using a depth first search approach . The mean number of tips in the clades was 127 . 86 out of 90 clades contained samples from the ATHENA cohort , with a fraction between 0 . 01 and 0 . 97 . Overall , the clades we extracted contained 8326 sequences from the Los Alamos data and 3186 from the Dutch HIV-1 ATHENA cohort . We compared the HIV clades with simulated trees from different networks and to trees simulated on the same network , but with varying infection rates . We trained random forest and K-nearest neighbour classifiers on tree features from the simulated networks , and used the features from the HIV clades as a test set . The simulated trees ( the training set ) had 100 tips . We then used the classifiers to predict the network type or infection rate for the HIV clades . We used principal component analysis to study different types of networks , different mean degree and infection rate for a given network , as well as different turnover rates and a time-integrated static network ( see all scenarios in Table 2 ) . We also trained classifiers on the networks in order to predict infection rate , turnover rate and network type ( see all scenarios in Table 3 ) .
Fig 2 shows a principal component analysis based on phylogenetic trees simulated on dynamic networks with three different topologies . Phylogenies from the Erdős-Renyi network differ strongly from the two others . This holds even for relatively small trees ( 100 tips ) , whereas for clustered and unclustered networks , the discrimination improves with the size of tree ( up to 250 tips ) . The same results hold for a wide range of infection rates ( β = 0 . 025 to β = 0 . 2 ) and higher turnover rates ( δ = 0 . 1 ) . Overall , the discrimination between networks improves with tree size . The distinction between trees from different underlying networks improves if additional features are used that take into account the lengths of edges . Skewed and skewed-clustered network have a lower number of small substructures ( cherries and pitchforks ) , and a higher value for all imbalance measures . Most network measures ( except betweenness ) are also positively correlated with imbalance measures . The network structures become more distinct with a higher rate of infection per contact and with a higher rate of turnover ( eg β = 0 . 2 , δ = 0 . 1 ) , and in particular the numbers of cherries and the path lengths become more distinct as these parameters increase . Differences in the path lengths and the imbalance between the networks are also more pronounced with higher β and δ . In contrast , however , there are a few features for which differences are more pronounced at low infection rates ( including the ‘ILnumbers’ and the Wiener index for clustered vs unclustered networks ) . In other words , given fixed values of the transmission and turnover rates , it is possible to separate , and estimate , the underlying network structure based on phylogenetic tree features , for example by discriminant analysis , classification methods , or by Approximate Bayesian Computation . However , the details—which phylogenetic features point to which kinds of networks—are specific to the transmission and turnover rates , and mis-estimation seems likely if these are mis-specified . Furthermore , for some choices of parameters , the networks are no longer well-separated in the PCA analysis; for example , if β = 0 . 05 and δ = 0 . 1 ( so β < δ ) , the clustered network overlaps with the random network , whereas if β > δ , they do not overlap , but the two skewed networks ( clustered and unclustered ) begin to overlap . When infection rate per contact β increases , so does the variance of tree features , and the following tree features increase on average: Colless index , Sackin index , IL numbers ( nodes with single tip child ) , average ladder size , maximum height , average pathlength , Wiener index and diameter . The number of cherries , pitchforks and maximal closeness decrease with increasing infection rate , as shown in Fig 3 for the skewed-clustered network . The same features increase as the mean degree increases ( red and green vs . turquoise and purple in Fig 3 ) , which is expected , as both increasing β ( infection rate per contact ) and increasing the number of contacts increase the basic reproduction number R 0 = β d ¯ τ ( τ being the duration of infection and d ¯ the median degree ) of an outbreak . The phylogenies from the four outbreak hypotheses in Fig 3 may therefore correspond to different pathogens or to a pathogen in rather different epidemiological settings , as in these scenarios R0 values may differ substantially . However , the tree features that discriminate these scenarios are also affected by the nature of the contact network ( Fig 1 ) and by the turnover rate ( Fig 4 ) . This comparison highlights that the network type and turnover are likely to affect estimation of the mean degree and the infection rate from phylogenetic trees . Simulated trees to figure 4 are found in S3 File . Fig 4 shows a PCA of phylogeny features derived from skewed-clustered networks with same mean degree but different turnover rates ( i . e . rates at which people enter and exit the system ) , and from a time-integrated static network of same mean degree d ^ . Higher population turnover of the network increases the following features of the simulated phylogenetic trees: Sackin index , Colless index , average ladder sizes , IL number , maximum height , average pathlengths , diameter , Wiener index , and betweenness , and decreases the number of cherries and pitchforks as well as maximum closeness . Higher turnover gives similar results to a higher mean degree or a higher infection rate ( see Fig 3 ) . The static time-integrated network has no turnover , but contacts have a longer duration , presenting the opportunity to transmit comparably to a dynamic network with much higher turnover than the one used for the time integration . In dynamic networks , links get rewired often and therefore many opportunities for transmission exist . The static network has higher mean degree as the temporally existing links are accumulated ( see Fig 4 ) . Instead of resembling those from very low turnover , the phylogenies from static networks have therefore features similar to those from networks with very high turnover . This effect holds for different infection rates β , but the higher the infection rate , the more the phylogenies from a time-integrated network differ from those from networks with low turnover . Results for varying infection rate , mean degree , turnover and time-integration are qualitatively the same for the skewed-clustered and skewed-unclustered network , but since the unclustered network has shorter average pathlength than the clustered network of same mean degree , the effects are more pronounced . Imbalance measures are always anticorrelated with the counts of small substructures ( pitchforks and cherries ) . The fact that network skewness increases tree imbalance ( and decreases substructures ) could be due to the fact that high heterogeneity in the network degree is passed on to high heterogeneity in the number of secondary infection , resulting in an imbalanced tree ( measured e . g . by Sackin and Colless index ) . On the other hand , increased network clustering may have the opposite effect , as it results in fewer nodes being connected to hubs in the network , which may cause the infection tree and resulting phylogenetic tree to be more balanced and to exhibit more pitchforks and cherries . However , an imbalanced phylogenetic tree could in principle also result from long chains of person-to-person transmission , in which each individual infects exactly one other: imbalanced trees do not necessarily require heterogeneous contact numbers or heterogeneous numbers of secondary infections . For simulations with distributed values for β , δ and mean degree of the network , we calculated all of our features of phylogenetic trees and used these to train classifiers , which we then tested . We used K nearest neighbours ( KNN ) [58] which classifies an object based the the class of the majority of its nearest neighbours , and random forests [59] which use decision trees to classify the test data . We simulated 1549 phylogenetic trees on the three types of networks , with random uniformly distributed values of the turnover and transmission rate parameters ( both in [0 . 05 , 0 . 15] ) and mean degrees ( in [4 , 9] ) . We trained classifiers on 1040 instances to classify from which type of network a phylogeny was derived . We compute the mean and standard deviation of the accuracy using 10-fold cross-validation . The classification is successful in the sense that it is possible to classify the dynamic network type based on the phylogenetic features , given a range of transmission parameters and turnover rates in the training data . Table 4 lists the results when we choose the key parameters β ( transmission rate ) , mean degree and turnover δ uniformly at random over the specified ranges . Both classifiers predict the network type with high accuracy , using the phylogenetic features . This means that even with the additional complications of dynamic networks and unknown underlying parameters , phylogenetic trees encode information about the nature of the network . We also asked how varying the underlying ( and in general unknown ) dynamic contact network would affect estimation of the transmission parameter β ( also in Tables 4 and 5 ) . Estimation of β is much worse than estimation of the network , and strongly depends on the assumed network . The performance is best for random forests with either all three networks present in the data ( accuracy 0 . 47 ) or with a single , correctly-specified , skewed or random network used to train the model ( accuracy 0 . 55 , 0 . 44 respectively ) . Mis-specification of the network worsens predictions . Discrimination between skewed and skewed-clustered networks remains difficult , as these networks are quite similar . The difference between skewed and random networks is more pronounced ( as also seen in the PCA analysis in Fig 2 ) . In that sense our results are similar to the results in [60–62] , who successfully predicted contact rates with Approximate Bayesian Computation ( ABC ) on static networks , where the phylogenetic trees separate well in a PCA plot of extracted tree measures . Given the poor ability to predict β when the mean degree and turnover are randomly sampled , we explored whether keeping these parameters fixed would improve the estimation: if we knew these parameters and had pathogen phylogenies , would we then be able to estimate the transmission rate in the context of dynamic networks ? Here , the accuracy is only good in the case of the random network ( 0 . 7 , 0 . 82 for KNN , random forests respectively ) . Random forests give consistently slightly higher accuracy , with an accuracy over 0 . 5 where ( 1 ) all three networks ( skewed , skewed-clustered an random ) were present in the training data , or ( 2 ) the model was trained on the skewed or random networks . If the network is mis-specified or skewed , neither approach is able to predict β . We suggest that this may have adverse consequences for analyses using static or other assumed network models in phylodynamics; these may draw erroneous conclusions about the rate of transmission or other parameters due to mis-specification of the underlying network . We trained classifiers on phylogenetic trees simulated with different network hypotheses , in order to predict the network type for HIV clades from sequences of patients in the Dutch ATHENA cohort and from sequences of the Los Alamos Sequence database [55] . The Dutch sequences predominantly capture the Dutch national HIV epidemic ( cite Bezemer PLoS Med ) , whereas the sequences in the Los Alamos database are from cases worldwide and capture many diverse HIV epidemics . Our network predictions are consistent with this: the higher the fraction of tips from the Netherlands , the more HIV trees are predicted to arise from skewed or skewed-clustered networks , rather than random ( see Table 6 ) ; this signal is consistent in the K-nearest neighbour and random forest classification . We also trained the classifiers on simulated trees from a skewed-clustered network with two different infection rates ( β = 0 . 05 and β = 0 . 2 ) , in order to predict the infection rate for the HIV trees ( see Table ( 7 ) . We did the latter both with trees from static networks and dynamic networks with turnover rate δ = 0 . 1 . For the static network , roughly two thirds of the HIV trees are predicted to have infection rate β = 0 . 05 and one third β = 0 . 2 . In contrast , all of the HIV trees are predicted to have the higher infection rate of β = 0 . 2 on the dynamic network . It is not surprising that more HIV trees were predicted to have the higher infection rate β = 0 . 2 when the classifiers were trained on the dynamic network . On dynamic networks , not all links are present at any moment , which slows down the outbreak . A higher infection rate could compensate to attain the same R0 . This result was very robust even when fewer tree features were used to train the classifier . However , if only imbalance measures were used , a low fraction of HIV trees were predicted to have β = 0 . 05 by dynamic-network-based classifiers . This suggests that using a variety of tree features is important for specification of network parameters from phylogenies . We have also listed separate predictions for clades in which more than 50% or 70% of the tips are from the ATHENA dataset; these are geographically linked , may include more recent transmission and are likely to have a higher sampling density than background clades from the Los Alamos database . Compared to the whole set of 90 HIV clades , these clades are more likely to be classified to have come from a skewed ( clustered ) network and to have a high transmission rate ( β = 0 . 2 ) . However , the certainty on this prediction depends on the underlying network assumption , with classifiers trained on dynamic models showing a completely consistent set of predictions while those trained on static models leave considerable variation ( Table 7 ) . In contrast , clades with fewer Dutch sequences were classified predominantly to have a lower transmission rate if classifiers were trained using static networks , but a higher transmission rate using dynamic networks . The fact that the results differ considerably depending on the underlying network assumption indicates that a mis-specified network , via an incorrect turnover rate or indeed the assumption of a static network , can have a strong effect on predicted transmission rates .
We used models of different human host contact networks to simulate outbreaks of pathogens , and convert the infection trees into phylogenetic trees . We showed that it is possible to discriminate with tree statistics between different contact network hypotheses , different turnover rates , different mean degrees and different infection rates . Table 8 sumarizes the network effect on tree statistics . The underlying contact network hypothesis ( random , skewed or skewed-clustered ) is clearly identifiable in statistics of the simulated phylogenetic trees , if β and δ are the same . This indicates that simple networks such as the Erdős-Renyi model are likely to be unsuitable models for human host contact networks where there is evidence for a skewed degree distribution and clustering . Nevertheless , in our simulations , phylogenies from skewed-clustered networks are slightly more similar to those from random networks than those from unclustered networks of the same degree distribution . Phylogenetic trees from outbreaks on the same static network , but with different infection rates or different mean degrees , can be distinguished clearly in PCA plots . This result holds also on dynamic networks , and suggests , in keeping with previous work , that phylogenetic tree features can be used to estimate epidemiological parameters . However , the relationships between the epidemiological parameters , networks and phylogenetic trees are complex . We tested the strength of some of these relationships using supervised learning methods , and found that both network mis-specification and variability in other parameters ( modelling uncertainty about the values of these parameters ) have a strong impact on the ability to estimate the transmission parameter . Our results indicate that consistent network mis-specification and parameter uncertainty may have an adverse impact on phylodynamic studies estimating parameters from data . Population turnover in dynamic networks has a measurable effect on pathogen phylogenies; phylogenetic tree features can discriminate between different turnover rates at which the underlying network is evolving . Overall , the higher the turnover , the higher the imbalance measures and the lower counts of small substructures . No single feature captures the differences between contact network hypotheses entirely , and a combination of many different features yields the best visual separation between the groups in a PCA plot . Features that take into account the branch length of the phylogenetic trees improve the separation slightly . Very different patterns are obtained from a static time-integrated network as compared to dynamic networks , on which transmission happens slower . This suggests that in the phylodynamic setting , static networks are a poor approximation for dynamic networks , highlighting the need for dynamic network models . This also highlights the need for investigating turnover and dynamics in empirical networks to obtain the data necessary to develop dynamic models . We illustrated this result by predicting the infection rate β of HIV trees , and showed that the predictions strongly underestimate β if a static network is used instead of a dynamic one . Comparison to HIV data also showed that clades with tips predominantly from the Dutch sequence dataset with high sampling fraction of infected individuals are more likely to be predicted to have come from a skewed or skewed-clustered network than those with tips mainly from the even sparser sampled Los Alamos database . Although the dynamic skewed-clustered network is likely to be a more realistic approximation to real networks than static or unclustered networks , it still might not be as clustered as a given real contact network . The details of the relevant network for a study of real data will depend on the pathogen and also on the nature of the community in which that pathogen is being studied . The dynamic models we have used here are still relatively simple and tractable , and real networks are likely to be even more heterogeneous .
|
Understanding whether and how transmission patterns are revealed by branching patterns in phylogenetic trees for pathogens remains a challenging research question . Besides the diversification of the pathogen , branching patterns depend strongly on the host contact structure as it shapes opportunities for the pathogen to reproduce . However , the host contact network is often difficult to study , in particular as it evolves in time . In this paper we perform a simulation study on three different dynamic networks , on which we simulate transmission trees . We use a simple Erdős-Renyi random network and two more realistic networks with skewed degree distribution , where one is also clustered . We convert transmission trees into phylogenetic trees and analyze them with different tree statistics like imbalance measures , counts of small substructures , and measures containing the branch lengths . We study the tree features with principal component analysis and with supervised learning methods , and find that network dynamics and network type can strongly influence the shape of phylogenetic trees . This implies that using phylogenetic trees from a mis-specified network type and dynamic can lead to poor phylodynamic estimation of transmission parameters . We illustrate this with HIV phylogenetic trees constructed from viral sequences of patients in the Dutch ATHENA cohort , and from sequences of the Los Alamos Sequence database .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"taxonomy",
"medicine",
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"statistics",
"pathogens",
"microbiology",
"retroviruses",
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] |
2019
|
Phylogenies from dynamic networks
|
The creation of restriction enzymes with programmable DNA-binding and -cleavage specificities has long been a goal of modern biology . The recently discovered Type IIL MmeI family of restriction-and-modification ( RM ) enzymes that possess a shared target recognition domain provides a framework for engineering such new specificities . However , a lack of structural information on Type IIL enzymes has limited the repertoire that can be rationally engineered . We report here a crystal structure of MmeI in complex with its DNA substrate and an S-adenosylmethionine analog ( Sinefungin ) . The structure uncovers for the first time the interactions that underlie MmeI-DNA recognition and methylation ( 5’-TCCRAC-3’; R = purine ) and provides a molecular basis for changing specificity at four of the six base pairs of the recognition sequence ( 5’-TCCRAC-3’ ) . Surprisingly , the enzyme is resilient to specificity changes at the first position of the recognition sequence ( 5’-TCCRAC-3’ ) . Collectively , the structure provides a basis for engineering further derivatives of MmeI and delineates which base pairs of the recognition sequence are more amenable to alterations than others .
Due to their exquisite selectivity , Type II restriction endonucleases ( REases ) are paradigms in the study of protein-DNA sequence recognition [1 , 2] . Approximately 4 , 000 have now been discovered [3] , specific for a remarkable 365 different DNA sequences . Impressive as this number is , it represents only a small fraction of the total number of DNA sequences that could in principle be recognized . Attempts to increase the number of REase specificities by protein engineering have met with very limited success due both to our incomplete understanding of the molecular mechanism of recognition and to the proteins themselves , which inherently resist such changes [4–9] , a property termed “immutability” [10] . Immutability stems from the circumstances under which these enzymes have evolved . REases occur mainly in prokaryotes—bacteria and archaea—in partnership with DNA-methyltransferases ( MTases ) of identical specificity that serve to protect the cell’s own DNA from REase cleavage [2 , 11 , 12] . Together , the two enzymes form a restriction-modification ( R-M ) system that confers innate immunity against viruses and other infectious genetic elements . Unless compensated for by a corresponding change in the partner enzyme , a change in the specificity of either one is liable to be detrimental due to cleavage of the host’s DNA at unprotected sites [10] . Simultaneous , matching changes are exceedingly unlikely among systems in which the REase and MTase ( s ) are separate proteins that act independently . Not all R-M systems behave in this way , however . The Type IIG and Type IIL families comprise bifunctional R-and-M ( RM ) enzymes in which the two catalytic activities share the same target recognition domain ( TRD ) for sequence recognition [13 , 14] . These enzymes can change specificity more readily because any change affects both restriction and modification activities in the same way at the same time [15] . There is a selective advantage for cells to switch restriction specificity occasionally to counter resistance among infecting viruses . Accordingly , the TRDs of the bifunctional Type IIL MmeI-family RM enzymes have evolved structures that lend themselves to such changes; as a result , the DNA sequences that these enzymes recognize have diversified very widely [15] . The bifunctional RM enzymes provide a natural platform for engineering new DNA-binding specificities , and some success in this direction has been achieved already [15 , 16] . The cloning of MmeI , from the bacterium Methylophilus methylotrophus , and comparison of its sequence to genome database sequences led to the identification of a family of homologs that , despite significant amino acid similarity , recognize different DNA sequences . Analysis of covariation between the DNA sequences recognized by these enzymes and the amino acid sequences of their TRDs enabled pairs of amino acids specifying several of the base pair positions to be identified [15] . By interchanging these amino acids , derivatives of MmeI and NmeAIII were constructed that recognize new DNA sequences with high fidelity [15] . No structural framework exists for understanding the atomic basis for these specificity changes , however , and this has limited the repertoire that has been rationally engineered in this way . To better understand the structural basis of DNA recognition and cleavage by Type IIL enzymes , we have determined the crystal structure of MmeI in complex with its DNA substrate . MmeI is a large enzyme ( 919 amino acids , 105 . 1 kDa ) that integrates DNA recognition and methyltransferase and endonuclease activities within the same polypeptide [13 , 17 , 18] . MmeI recognizes the asymmetric DNA sequence 5’-TCCRAC-3’ ( R = purine; A or G ) and methylates the invariant adenine in the “top” strand ( underlined ) . When multiple unmodified sites are encountered , MmeI cleaves the DNA approximately two helical turns downstream , on average 20 nucleotides ( nts ) away from the methylated adenine on the top DNA strand and 18 nts away on the bottom DNA strand ( thus , TCCRAC 20/18 ) . The structure reveals the amino acids responsible for DNA recognition in MmeI and suggests a basis for the long “reach” of the enzyme between its DNA recognition and cleavage sites . The structure establishes a framework for rationally engineering further derivatives from MmeI and its homologs , which possess new , intentionally chosen specificities .
MmeI was co-crystallized with a 29-mer DNA duplex containing a single MmeI recognition site ( TCCGAC ) . The co-crystals were obtained in the presence of Sinefungin and diffracted to 2 . 6 Å resolution with synchrotron radiation . They belong to space group P1 with unit cell dimensions of a = 61 . 87 Å , b = 95 . 29 Å , c = 161 . 96 Å , α = 72 . 84° , β = 89 . 15° , and γ = 71 . 61° ( Table 1 ) , and contain two MmeI/DNA/Sinefungin complexes in the crystallographic asymmetric unit . Related by a non-crystallographic symmetry , the two complexes are almost identical ( root-mean-square [r . m . s . ] deviation ~0 . 16 Å over 748 Cα ) . The structure was determined by the single-wavelength anomalous diffraction ( SAD ) method and refined to 2 . 6 Å resolution ( Table 1 ) . The final refined model consists of two MmeI molecules ( residues 156–906 ) , two 13-mer DNA duplexes ( sense strand nucleotides 1–13 and antisense strand nucleotides 17–29 ) , two Sinefungin molecules , two calcium ions , and a total of 61 solvent molecules . Regions of protein with no electron density were omitted in model building , and amino acids with weak electron densities for their side chains were modeled as alanines . The current model lacks the endonuclease portion of MmeI due to the lack of electron density for this region . MmeI is composed of five domains . An N-terminal PD- ( D/E ) XK-type endonuclease domain ( residues 1–155 ) connects to a γ-class N6-adenine DNA-methyltransferase domain ( 6mA-MTase; residues 301–620 ) via a multi-helical spacer ( residues 156–300 ) ( Fig 1A ) [19] . These are followed by the TCCRAC-specific TRD ( residues 621–825 ) , and a final C-terminal helical bundle ( residues 826–919 ) ( Fig 1A ) . The endonuclease domain is disordered in the present structure , but its putative position—preceding the spacer—is in keeping with the ability of the enzyme to cleave DNA outside of the recognition sequence ( Fig 1A ) . The DNA is embedded between the TRD and the MTase domain with the adenine to be methylated ( TCCGAC ) flipped out of the DNA helix into the catalytic pocket of the MTase domain ( Fig 1 ) . The TRD makes contacts to the DNA bases primarily in the major groove , while the MTase domain makes several contacts to the DNA in the minor groove . The primary role of the MTase is to catalyze transfer of the methyl group from S-adenosyl methionine ( AdoMet ) to the 6-amino group of the target adenine , which resides in the active site cleft of the MTase domain . The overall conformation of the DNA is B-DNA , but it is severely distorted at the juncture where the target adenine is flipped from the helix ( Fig 1B ) . The sugar-phosphate backbone of the target adenine is displaced toward the MTase domain by several Angstroms , and the minor groove over this region widens by ~7 . 6 Å when compared to a regular B-DNA . The overall configuration of MmeI can be compared to that of the related Type IIG RM enzyme , BpuSI ( 878 aa; recognition sequence: GGGAC 10/14 ) . BpuSI cleaves roughly one turn of the DNA helix closer to its recognition sequence than MmeI , and creates a 4-base 5’-overhang rather than a 2-base 3’-overhang . The structure of BpuSI has been determined in the absence of DNA and reveals an ordered endonuclease domain that is sequestered by the helical spacer ( Fig 2 ) [14] . Superposition of the MmeI and BpuSI structures suggests that the main conformational change on DNA binding is an ~38° rotation of the TRD to clamp onto the DNA ( Fig 2 ) . The MTase domain of MmeI , and to some extent the TRD , also superimpose on M . TaqI ( 421 aa; recognition sequence: TCGA ) , a monofunctional 6mA-MTase of the same γ-class as MmeI and BpuSI . M . TaqI has been crystallized with and without DNA [20–22]; the position of the bound DNA in the former is nearly identical to that in MmeI . Concomitant with its inability to cleave DNA , M . TaqI lacks the N-terminal cleavage domain of MmeI ( and of BpuSI ) and the helical connector . It also lacks the C-terminal helical bundle that follows the TRD of MmeI . The TRD is composed of two α/β subdomains comprising residues 621–745 ( TRD-N ) and 746–825 ( TRD-C ) . These domains contact the bases of the recognition sequence exclusively in the major DNA groove . TRD-N mainly follows the backbone of the complementary strand of the recognition sequence and interacts with the first two base pairs of the recognition sequence ( TCCGAC ) . TRD-C tracks the DNA major groove and interacts with the remaining bases ( TCCGAC ) ( Fig 1A ) . These interactions are supplemented by contacts in the minor groove from the MTase domain . Altogether , ~2100 Å2 of solvent-accessible surface area is buried between the DNA and the TRD and the MTase domain ( S1 Fig ) , in the range observed with conventional Type II restriction enzymes such as BamHI and BglII [10 , 23] . The MTase domain ( aa ~301–620 ) consists of a twisted β-sheet flanked by α-helices on both sides ( Fig 1A ) . The two principal motifs characteristic of amino-methyltransferases , generically termed “FGG” ( motif I = AdoMet-binding site , aa 360–370 ) and “DPPY” ( motif IV = nucleotide binding and catalytic site , aa 481–484 ) extend from adjacent loops that connect secondary structure elements . Based on the order and sequences of these motifs , MmeI belongs to the γ class of amino-methyltransferases [19] , in which motif I is typically …FDPACGCGXFL… and motif IV , …NPPF… . The extrahelical adenine ( TCCRAC ) occupies the catalytic cleft between motifs I and IV and forms three H-bonds with residues of the catalytic-site . Consistent with other γ-class ( but not with β-class ) amino-methyltransferases [24] , motif IV residues face the Hoogsteen-edge of the flipped adenine base . The adenine N7 atom accepts one H-bond from the Phe484 main chain N ( 2 . 6 Å ) , and the N6-group donates one H-bond to Asn481 OD1 ( 2 . 8 Å ) and one to Pro482 main chain O ( 2 . 7 Å ) ( Fig 5 ) . A fourth , weak , H-bond might also be present between adenine N1 and Asn481 ND2 ( 3 . 5 Å ) . The extrahelical adenine is further stabilized by π–π interactions with the aromatic rings of His314 , Phe484 , and Trp570 , which form a box around the base . His314 stacks on one side of the adenine , Phe484 stacks on the other , and Trp570 stacks edge-on ( Fig 5 ) . All of these amino acids ( belonging to the NPPF motif IV , as well as His314 and Trp570 ) are absolutely conserved in the 341 MmeI-family enzymes whose sequences we have aligned to date . The acceptor atoms of Asn481 and Pro482 to which adenine N6 donates H-bonds lie above the plane of the flipped base , suggesting that the nitrogen atom possesses a tetrahedral , SP3 , orbital geometry , rather than the planar SP2 geometry it possesses when intrahelical . In this induced SP3 configuration , the electronegative lone pair orbital of the nitrogen points directly toward the electropositive methyl group of AdoMet modeled into our structure , appropriately positioned for methyl transfer by in-line nucleophilic attack ( Fig 5 ) . To avoid catalysis and methyl transfer in our complexes , we crystallized MmeI in the presence of the AdoMet analog , Sinefungin , which has a nontransferable amino group in place of the methyl group . This amino group is positioned 3 . 4 Å from the adenine N6 atom in our structure and is slightly displaced . When we aligned the structure of MmeI with that of M . TaqI ( pdb:2ADM ) , which was crystallized with AdoMet [21 , 22] , the cofactor and analog superimposed closely , and the methyl group of AdoMet was found to be closer to the adenine N6 atom ( 3 . 0 Å ) and in slightly better alignment . MmeI-family enzymes have the longest “reach” among Type II REases , cleaving DNA with some variability 21-22-nt away from the methylated adenine in the “top” DNA strand , and 19-20-nt away in the complementary , “bottom , ” strand . In the majority of these enzymes , the methylated A is the penultimate base in the recognition sequence , and so most cleave approximately 20/18 downstream from the sequence . By comparison , FokI , a Type IIS REase in which the DNA recognition and cleavage functions are also located on separate domains , cleaves DNA 9-nt/13-nt downstream of the recognition sequence [25] . The ability of MmeI to generate 20-bp “tags” has made it an attractive enzyme for certain applications , including serial analysis of gene expression ( SAGE ) and paired-end tags ( PET ) in next-generation DNA sequencing . Although the cleavage domain of MmeI ( residues 1–155 ) cannot be seen in our structure ( S2 Fig ) , its putative position , far from the TRD , is consistent with the ability of MmeI to cleave some distance away from the recognition sequence ( Fig 1A ) . The helical spacer likely plays a key role in positioning the cleavage domain correctly in this regard , 20-nt/18-nt , from the sequence recognized . Amino acid sequence analysis of MmeI family enzymes indicates that each contains only one catalytic site , belonging to the PD… ( D/E ) XK nuclease superfamily [11 , 12] . The two parts of this motif , PD and ( D/E ) XK , usually form the termini of adjacent β-strands and fold such that the acidic residues ( D and E ) coordinate one or more divalent metal ions , and the lysine ( K ) contributes to activation of a hydrolytic water molecule [11 , 12] . In the case of MmeI , the catalytic residues are V69-D70…E80-M81-K82 , and mutation of D70 , E80 , or K82 to alanine eliminates endonuclease activity [26] . REases generally cleave both strands of duplex DNA in one binding event , and so their active forms are often multimeric , comprising two , four , and sometimes more identical subunits [12 , 27] . At a minimum , MmeI must cleave DNA as a dimer in which the catalytic domains of two molecules interact and each cleave one DNA strand . There is “vacant” space in the crystals adjacent to the helical spacer that can accommodate a domain of the size of the cleavage domain . The lack of electron density in this region ( S2 Fig ) suggests that the cleavage domain is mobile and flexibly tethered to the helical spacer , and that it may only become ordered when dimerized with that of a second enzyme molecule to form a competent cleavage complex . A similar pattern ( disordered endonuclease domain in the crystal ) was also observed in structures of a Type III RM enzyme EcoP15I [24] and a Type IIS enzyme AspBHI [28] . Unlike Type IIG BpuSI , MmeI requires two DNA recognition sites for efficient DNA cleavage , suggesting that both molecules must be bound to recognition sites in order to dimerize productively .
We present here the first crystal structure of a Type IIL RM enzyme bound to its DNA substrate . MmeI differs from conventional Type II R-M systems ( such as BamHI or EcoRI ) in that the DNA recognition , methyltransferase , and endonuclease activities reside within the same polypeptide . The fact that the same DNA recognition module is responsible for host modification and endonuclease functions makes MmeI ( and related enzymes ) much more amenable to changes in DNA-binding and -cleavage specificities than conventional Type II enzymes . Based on bioinformatics analysis alone , we have rationally engineered dozens of MmeI-like enzymes with new specificities [15] . These specificity changes are at positions 3 , 4 , and 6 of the MmeI recognition sequence ( TCCRAC ) , and the engineered enzymes have specific activities that are comparable to the wild-type enzyme . The DNA-bound MmeI structure provides a molecular basis for these specificity changes and reveals new interactions to guide the engineering of additional enzymes . Overall , MmeI recognizes base pairs 3 , 4 , and 6 ( TCCRAC ) in a similar manner to that anticipated from previous bioinformatics analyses . As anticipated , base pair 3 is recognized by Glu751 and Asn773; base pair 4 by Arg810 and Ala774; and base pair 6 by Glu806 and Arg808 . This convergence between structure and bioinformatics analysis shows the utility of covariation analyses using MSAs in predicting amino acids that recognize DNA in Type IIL REases . The structure provides atomic-level details on how Glu751 , Asn773 , Arg810 , Ala774 , Glu806 , and Arg808 actually interact with DNA and a basis for specificity changes reported previously , including C:G to G:C at position 3 , R:Y to G:C at position 4 , and C:G to G:C at position 6 ( Fig 6 ) . Notably , previous bioinformatics and MSA covariation analyses did not yield insights into how MmeI ( and related enzymes ) recognizes DNA at positions 1 and 2 . Our structure suggests that the T:A base pair at position 1 ( TCCRAC ) is specified mainly by hydrophobic interactions between Tyr738 and the 5-methyl group of T . Interactions with the C:G base pair at position 2 are more extensive than to other base pairs , with specific hydrogen bond contacts from the major ( Tyr642 and Lys645 ) and minor ( Lys487/Ser488 ) groove sides . Previous sequence covariation analyses failed to pinpoint the positions corresponding to MmeI Tyr642 and Lys645 as specifying recognition at position 2 , because similar amino acid residues at these positions give rise to different sequence specificities in various MmeI family enzymes . For example , isoleucine and lysine at these positions , respectively , results in recognition of an A:T base pair in EsaSSI , MchCM4I , and AquIII , but C:G base pair in RmuAI . Several other enzymes that recognize an A:T base pair at this position contain a methionine at the position corresponding to Lys645 , paired with either tyrosine ( NlaCI ) or phenylalanine ( SdeAI , CstMI ) at the position corresponding to Tyr642 . Accordingly , when we change Lys645 to methionine in MmeI , the altered enzyme now preferentially recognizes an A:T base pair at position 2 ( Fig 4 ) , though it retains some partial activity toward the wild-type C:G base pair . It is likely that hydrophobic interactions between the methionine and the 5-methyl group of T contribute to this preference ( Fig 6 ) . Tyr642 seems readily able to contact an adenine in place of a cytosine , likely making similar interactions with the adenine N6 as with the cytosine N4 ( Fig 6 ) . Changing Tyr642 to Lys in combination with Lys645Met resulted in recognition of R ( both A:T and G:C ) at position 2 . MmeI homologs that recognize a G:C base pair at position 2 also have Lys or Arg at the 642 position ( RflFIII ) , often paired with Gln at position 645 . In our modeling , Lys642 appears well positioned to contact the N7 of the purine ( A or G ) and may be localized for this contact by interaction with the backbone carbonyl of Asn773 and the hydroxyl of Tyr776 . These results demonstrate the importance of both Tyr642 and Lys645 positions in specifying recognition at position 2 in the MmeI family enzymes . Overall , the creation of enzymes with programmable DNA-binding and -cleavage specificities has been a goal ever since the discovery of REases more than 40 y ago . However , attempts to rationally alter the DNA recognition specificities of conventional Type II REases have met with very limited success . Most of the current effort has thus shifted to artificial nucleases such as Zinc Finger Nucleases and transcription activator-like effector nucleases ( TALENs ) , or to homing endonucleases [29–36] . The many recently discovered MmeI-like enzymes offer an alternative approach to achieve “true” REase specificity engineering . The fact that a single DNA recognition module is responsible for host modification and restriction in these enzymes allows for rapid evolution of new specificities . The MmeI structure provides a basis for beginning to understand how Type IIL enzymes like MmeI recognize their DNA substrates and a framework for changing their specificities .
Both the native and selenium-methionine ( Se-met ) MmeI proteins were expressed and purified as described previously [37] . The native MmeI protein was successfully crystalized in complex with a 29-mer DNA using 2 μl hanging-drops over 1 ml reservoirs at 293 K . The optimized crystals were grown using a mother liquor of 20% PEG 4K , 0 . 1M Hepes ( pH7 . 5 ) , and 0 . 1M ( NH4 ) 2SO4 . Resolution was improved to 2 . 6 Å by replacing several thymines outside of the recognition site with 5-bromouracil ( 5'TATCCGACAUAACGCUAGUCACUAGCUUC-3'/3'ATAGGCTGUATUGCGAUCAGUGAUCGAAG-5'; where U is 5-bromouracil ) . The brominated DNA oligonucleotides were synthesized at New England Biolabs and PAGE purified prior to crystallization . For cryoprotection , the crystals were soaked for 5 min in solutions containing mother liquor plus increasing concentrations of glycerol ( final concentration of 30% glycerol ) and plunged into liquid nitrogen . Given the absence of an appropriate molecular replacement solution , co-crystals with Se-met MmeI ( 14 methionines per molecule ) were grown under similar conditions as the native enzyme . The Se-met crystals diffracted to 3 . 0 Å resolution . The X-ray diffraction data on the MmeI/DNA/Sinefungin co-crystals were measured at the Advanced Photon Source at the Argonne National Laboratory . The data on native crystals were measured at beamline 23ID-D at a wavelength of 0 . 91938 Å , while single wavelength anomalous data on a Se-Met crystal were measured at a wavelength of 0 . 97944 Å ( Se-K absorption edge ) at the beamline 24ID-C . The HKL2000 package [38] was used to merge and scale X-ray data . Both the native and Se-Met crystals belong to space group P1 . The unit-cell dimensions of native crystals are a = 61 . 87 Å , b = 95 . 29 Å , c = 161 . 96 Å , α = 72 . 84° , β = 89 . 15° , and γ = 71 . 61°; and unit-cell dimensions of the Se-Met crystals are a = 62 . 08 Å , b = 94 . 68 Å , c = 159 . 91 Å , α = 73 . 34° , β = 80 . 35° , and γ = 71 . 89° . The structure was solved using SAD phasing method using SHARP [39] . The electron density map derived from experimental phasing was readily interpretable and showed clear electron density of both protein and DNA molecules . The model was built manually using program Coot [40] and iteratively refined with the program package Phenix [41] to the 2 . 6 Å resolution limit of the native crystals ( Table 1 ) . The final model contains two molecules of MmeI bound to two separate DNA duplexes and two Sinefungin moieties . The quality of the structure is excellent , with >97% of the residues in the most favored regions of the Ramachandran plot ( Table 1 ) . Endonuclease activity was assayed by incubating various amounts of MmeI ( wt or mutant ) enzyme for 30 min at 37°C in NEBuffer 4 ( 20 mM Tris-acetate , pH 7 . 9 , 10 mM magnesium acetate , 50 mM potassium acetate , 1 mM DTT ) supplemented with AdoMet at 80 μM , containing 1 μg substrate DNA per 50 μl . Reactions were terminated by the addition of loading dye ( NEB B7024 ) and reaction products were analyzed by gel electrophoresis in 1% LE agarose gels .
|
Type II restriction endonucleases ( REases ) are the bedrock of modern biotechnology . Type II REases were essential for the recombinant DNA revolution and the development of gene technology . However , despite the discovery of more than 4 , 000 REases , the DNA recognition specificities are limited to only ~365 . The recently discovered Type IIL MmeI family of restriction-and-modification ( RM ) enzymes provides a framework for understanding and engineering new specificities . We report here a crystal structure of MmeI in complex with its DNA substrate and an S-adenosylmethionine analog ( Sinefungin ) . The structure uncovers for the first time the interactions that underlie MmeI-DNA recognition and methylation . The results establish a platform for rationally engineering further derivatives from MmeI and its homologs that will possess new , intentionally chosen , specificities .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
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2016
|
Structure of Type IIL Restriction-Modification Enzyme MmeI in Complex with DNA Has Implications for Engineering New Specificities
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We simulate deformable red blood cells in the microcirculation using the immersed boundary method with a cytoskeletal model that incorporates structural details revealed by tomographic images . The elasticity of red blood cells is known to be supplied by both their lipid bilayer membranes , which resist bending and local changes in area , and their cytoskeletons , which resist in-plane shear . The cytoskeleton consists of spectrin tetramers that are tethered to the lipid bilayer by ankyrin and by actin-based junctional complexes . We model the cytoskeleton as a random geometric graph , with nodes corresponding to junctional complexes and with edges corresponding to spectrin tetramers such that the edge lengths are given by the end-to-end distances between nodes . The statistical properties of this graph are based on distributions gathered from three-dimensional tomographic images of the cytoskeleton by a segmentation algorithm . We show that the elastic response of our model cytoskeleton , in which the spectrin polymers are treated as entropic springs , is in good agreement with the experimentally measured shear modulus . By simulating red blood cells in flow with the immersed boundary method , we compare this discrete cytoskeletal model to an existing continuum model and predict the extent to which dynamic spectrin network connectivity can protect against failure in the case of a red cell subjected to an applied strain . The methods presented here could form the basis of disease- and patient-specific computational studies of hereditary diseases affecting the red cell cytoskeleton .
Red cells possess a lipid membrane and cytoskeleton that together enclose a viscous cytoplasm characterized by a high concentration of hemoglobin . The elastic properties of the cell can be separated into contributions from the lipid bilayer , which supplies bending rigidity and resistance to local changes in area , and from the cytoskeleton , which is a polymer network of spectrin tetramers connected at actin-based junctional complexes that supplies shear resistance . In previous work [1] , we used a continuum neo-Hookean model [2] to describe the coupled membrane-cytoskeleton system , and we simulated the behavior of red cells in flow using the immersed boundary method , a numerical method for fluid-structure interaction problems [3] . However , applying the continuum approach to both the lipid membrane and cytoskeleton can be inadequate for certain applications because of the wide range of scales needed to describe the system ( e . g . the phospholipids that make up the membrane are approximately 8 Å apart [4] , whereas the average size of spectrin tetramers in the cytoskeleton is about 50 times larger [5] ) . On the one hand , continuum models correctly predict that red cells “remember” the positions of their biconcave dimples [6] , but on the other hand there is evidence that the cytoskeleton is constantly remodeling [7] so that the reference configuration changes over time , a property not taken into account in standard neo-Hookean continuum models . The sensitivity of measuring the shear modulus to the particular experimental setup [8 , 9] also suggests that neo-Hookean models of the cytoskeleton may be overly simplistic . Characterizing the cytoskeletal mechanics in detail , including the nature of network remodeling , is crucial for understanding the red cell’s exceptional deformability [7] and for explaining the experimental effects of repeated osmotic swelling and shrinking on red cell elasticity [10] . In light of these issues , the approach taken here is to build a model based on the molecular cytoskeletal structure . In particular , we retain the continuum description of the lipid membrane but replace the continuum cytoskeletal model with a discrete one . Significant steps in this direction have already been made , starting with Boal’s early work involving Monte Carlo simulations of small regions of the cytoskeleton that suggested the importance of volume exclusion effects for polymer models [11] . Later , Discher et al . studied the mechanical response of red cells during micropipette aspiration experiments using a discrete cytoskeleton model [12] . More recently , the group of Suresh simulated whole cells using a detailed cytoskeleton model that considered interactions between spectrin monomers via molecular dynamics and allowed for network reorganization [13–15] . Cytoskeletal structure has also been incorporated into composite models , in which the membrane and cytoskeleton are treated as distinct components , and which explicitly model the vertical connections between membrane and cytoskeleton that are affected in conditions such as hereditary spherocytosis [16–18] . In such composite models , the cytoskeleton is able to undergo changes in density , an effect that has been observed during micropipette aspiration [19] . Further , Pivkin et al . used coarse-graining techniques to calculate the spectrin network’s effective shear modulus as a function of the number of degrees of freedom used to represent the red cell surface [20] . Although some simulations of red cell cytoskeletons assume a hexagonal topology , our work allows for the irregular topology , as do e . g . Saxton [21] , Hansen et al . [22] , Li et al . [13] , and Gov [23] . Here , we propose a mesoscopic model of the cytoskeleton that we use for whole cell immersed boundary method simulations . Although others have used random graph models of the spectrin cytoskeleton , a novel feature of our work ( to the best of our knowledge ) is that we derive the statistical properties of the random graph from 3D electron tomographic images . Representing the cytoskeleton as an explicit polymer network allows us to investigate the effect of changes at the microscopic level , such as network remodeling , in a relatively straightforward manner . This approach allows us to investigate changes in the horizontal connections within the cytoskeleton , complementing the studies cited above that model the vertical connections between the cytoskeleton and membrane . We use the immersed boundary method to account for the coupling of the red cell to the surrounding fluid . The immersed boundary method has previously been applied to many problems in cellular mechanics , including cytoskeletal mechanics [24 , 25] , tumor cell adhesion [26] , flagellar motion [27 , 28] , and cytoplasmic streaming [29] . This article is structured as follows: we first describe an algorithm to generate random graphs on surfaces with a specified node density , edge length distribution , and mean number of edges per node . In our model , the edges represent spectrin polymers that span junctional complexes , and we treat these polymers as entropic springs . We next show , using a simple two-dimensional geometry , that properly-initialized random networks exhibit elastic responses that are in good agreement with continuum models . No parameter fitting is needed to obtain this agreement; the entropic spring constant and parameters needed to generate the random graph have been measured in experiments described in the literature . Next , we introduce a segmentation algorithm that we use to extract geometrical information from 3D images generated by electron tomography . We apply this segmentation algorithm to a tomogram and use the resulting distributions as geometrical and statistical constraints for our model . We generate a random cytoskeleton on a triangulated surface representing a whole red cell and perform three-dimensional immersed boundary simulations to study the behavior of red blood cells in a prescribed shear flow . We further show how network remodeling can be included in this model and consider its effects on the frequency of spectrin polymer failure when the cell is subjected to a prescribed strain , as can be done in optical tweezer experiments .
We represent the cytoskeleton as a random graph on a surface , with the junctional complexes serving as nodes and the spectrin tetramers serving as edges . The node positions are drawn from a uniform distribution with respect to area , and nodes are randomly connected by edges using an algorithm that recovers the prescribed end-to-end length distribution . Here , we describe this two-step process in the case of N total nodes . We assume that there is a maximum edge length Dmax such that p ( D ) = 0 for D > Dmax . As a result of Step 1 , there is a probability density function ϱ ( D ) such that ∫ a b ϱ ( D ) d D = Pr ( D i j ∈ ( a , b ) ) , ( 1 ) where the right-hand side denotes the probability that Dij is in the interval ( a , b ) . Note that ϱ ( D ) gives the distribution of distances between arbitrary nodes on the surface independent of whether those nodes are connected . The probability P that there is an edge between any given pair of nodes is given by P = ∫ 0 D max p ( D ) ϱ ( D ) d D . ( 2 ) Since an arbitrary pair of points is assigned an edge with this probability , independent of any other pair of points , the probability that exactly k edges touch any given node is given by the binomial distribution ( N - 1 k ) P k ( 1 - P ) N - 1 - k , ( 3 ) which has mean μ = ( N - 1 ) P . ( 4 ) We are interested in N large and P small , so that ( 3 ) is well approximated by the Poisson distribution μ k k ! e - μ . ( 5 ) Another quantity of interest is the distribution of edge lengths , σ ( D ) , which is defined by ∫ a b σ ( D ) d D = Pr ( D i j ∈ ( a , b ) | i and j are connected ) , ( 6 ) where the right-hand side denotes the probability that Dij is in the interval ( a , b ) , conditioned on nodes i and j being connected by an edge . Note that Pr ( D i j ∈ ( a , b ) and i and j are connected ) = P ∫ a b σ ( D ) d D = ∫ a b p ( D ) ϱ ( D ) d D . ( 7 ) Since a and b are arbitrary , this implies the following instance of Bayes’ Theorem: σ ( D ) = p ( D ) ϱ ( D ) P = p ( D ) ϱ ( D ) ∫ 0 D max p ( D ′ ) ϱ ( D ′ ) d D ′ . ( 8 ) It follows from ( 8 ) that ∫ 0 D max σ ( D ) d D = 1 ( 9 ) as required . The probability density functions σ ( D ) and ϱ ( D ) and mean node degree μ can be measured from tomograms or taken from the literature , so we treat them as known and use them to determine p ( D ) . From ( 8 ) , p ( D ) = P σ ( D ) ϱ ( D ) , ( 10 ) and we can use ( 4 ) to express P in terms of μ . Since N is large , we replace ( N − 1 ) by N in ( 4 ) . Thus p ( D ) = μ N σ ( D ) ϱ ( D ) . ( 11 ) Eq ( 11 ) is a recipe for p ( D ) that will produce a random graph with the prescribed edge-length probability density function σ ( D ) and mean node degree μ . In order to be a valid probability , p ( D ) must satisfy 0 ≤ p ( D ) ≤ 1 for all D . Two conditions necessary for this to be true are that σ ( D ) = O ( D ) as D → 0 and N ≥ μ maxD ( σ ( D ) /ϱ ( D ) ) . Since the maximum edge length Dmax is typically much smaller than the surface’s radius of curvature , we can neglect curvature so that to good approximation , ϱ ( D ) = 2 π D / S , ( 12 ) where S is the surface area as stated above . Substituting this formula into ( 11 ) gives p ( D ) = μ 2 π σ ( D ) D S N . ( 13 ) As a validation test , we have used the random graph algorithm to generate a graph on the surface of a sphere with N = 1 , 200 nodes and mean node degree μ = 5 , and setting the sphere radius so that the node density satisfies N/S = 300/μm2 . The node density is chosen to be in line with the observed density of junctional complexes in the red cell cytoskeleton , but note that the total surface area of the sphere used in this test is a small fraction of the surface area of an intact red cell . As shown in Fig 1 , the statistical properties of the resulting random graph agree with the target distributions . Note that in the case of the red cell cytoskeleton , the nodes are located in a thin layer below the membrane rather than on a true surface . This gives a correction to ( 12 ) that is described in the section “Density through slab” of S1 Text . However , since the formula ( 11 ) is valid in either case and given the continuous transition between surface and thin slab geometries we do not draw a significant distinction between the two cases here . The cytoskeleton may for many purposes be considered as a continuum neo-Hookean material with a shear modulus E satisfying E ≈ 2 × 10−3 to 6 × 10−3 dyn/cm , as established through experiments and model studies [9] . Here , we use a simple two-dimensional test problem to compare this continuum formulation , discussed in detail in [1] , to the discrete cytoskeleton model proposed in this article . We show that the energetic response to shear deformations of our discrete cytoskeleton model is in excellent agreement with a neo-Hookean material having a shear modulus in the experimentally determined range . We test several randomly generated model networks connected to a 2D sheet that resists changes in area . The random graph algorithm is used to generate a model cytoskeleton with mean node degree μ = 5 , node density N/S = 300/μm2 , and edge-length probability density function ( PDF ) σ ( D ) on a periodic patch of membrane . The formulas used for σ ( D ) and ϱ ( D ) are computed from electron microscopy data , as will be described in the section “Data analysis” . Further , we compute a triangulation of the plane associated with the random graph by performing a Delaunay triangulation on the nodes ( see Fig 2 ( a ) ) . This associated triangulation serves a dual role; it is used to enforce local area incompressibility , thus preventing the entropic spring network from collapsing , and to compute the continuum shear energy . To account for the periodicity , we surround the unit cell with periodic copies , triangulate the whole region , and keep the set of unique triangles that contain at least one point from the unit cell . There are two contributions to the total elastic energy . First , each edge of the model cytoskeleton represents a worm-like chain , which is a model of an entropic spring with restoring force Fwlc given by Eq ( 4 ) of S1 Text . The total entropic force on a node with position X is ∑ j F wlc ( X j - X ) , ( 14 ) where { X j } j = 1 n is the set of n nodes connected by an edge to X . Second , there is a continuum bulk force Fbulk ( X ) that penalizes changes in local area in the elastic sheet . The bulk energy is defined in terms of the reference configuration Z and the deformed state X via W bulk = κ 2 ∫ Z ( J − 1 ) 2 d a , ( 15 ) where κ is the membrane bulk modulus , J is the Jacobian relating areas in the reference and deformed configurations , and da is the area element in the reference frame . By using the same nodes for the discretizations of both the membrane and cytoskeleton , so that both lie in the same plane with no slip allowed between them , we have made the idealization of strong vertical interactions between the membrane and cytoskeleton . In reality , the cytoskeleton and membrane can move relative to one another , and their relative deformabilities become evident in experiments such as micropipette aspiration in which the lipid bilayer becomes uniformly distributed in the tip of the pipette , whereas the cytoskeleton dilates and develops protein gradients in the tip [19] . The idealization of strong vertical connections is useful since it allows us to isolate the effects of changes to horizontal connections within the cytoskeleton that occur in hereditary elliptocytosis . Further , one of the constituents that make up the junctional complex and connects the cytoskeleton to the lipid membrane , the transmembrane protein Band 3 , has a long-range diffusion timescale on the order of seconds [19 , 30] . Therefore , holding it fixed to a certain point in the membrane is reasonable for our simulations , which take place over a few hundredths of seconds and involve network remodeling timescales on the order of 0 . 01–0 . 1 seconds . Note that the cytoskeleton resides in a thin layer with a width of just 200 nm beneath the membrane , as observed for instance in the tomograms analyzed here . Since this thickness is small compared to the red cell diameter , it is reasonable to make the approximation that the membrane and cytoskeleton occupy the same surface . Since we consider the red cell membrane to be locally area-incompressible , in practice κ is a penalty parameter that is set such that the absolute value of local changes in area are not greater than 2–4% on average [31] . In our simulations , we use values for κ in the range 0 . 1–1 . 0 dyn/cm . We have neglected the cytoskeleton bulk modulus since it is smaller than the membrane bulk modulus by several orders of magnitude [32] . On a triangulated domain , Wbulk can be as discretized as in [1] with W bulk = κ 2 ∑ s ( area ( t ) area ( s ) − 1 ) 2 area ( s ) , ( 16 ) where s and t are triangle indices in the reference and deformed configurations , respectively . The force at node X due to bulk elasticity is given by Fbulk ( X ) = −∇X Wbulk . The total force is simply the sum of entropic and bulk elasticity forces . We fix the time step Δt = 1 ⋅ 10−9 s and κ = 0 . 2 dyn/cm , and move the nodes by overdamped dynamics ζ X ˙ = F ( X ) = F bulk ( X ) + ∑ j F wlc ( X j - X ) using the forward Euler method . In a pre-computation , we let the nodes equilibrate as shown in the energy curves of Fig 2 ( b ) . The friction coefficient is calculated through the Stokes relation ζ = 6πνr with a radius of 15 nm for the actin nodes [5] and dynamic viscosity ν = 1 . 2 cP , resulting in a value of ζ ≈ 3 . 3 ⋅ 10−7 g/s . Next , the nodes are advected in the prescribed incompressible flow u = ( cos 2 π y / R , sin 2 π x / R ) , ( 17 ) and changes in the entropic spring energy and continuum shear energy are observed over a 2 μs timespan . This test is performed using square unit domains of length R = 1 μm and R = 2 μm with periodic boundary conditions , repeating 10 times on each domain with different randomly generated networks and discarding any trials in which the mesh becomes tangled . On the timescale over which this deformation is applied , the nodes of the network undergo a maximum displacement of 14% relative to the contour length L . Although the displacements from the initial configuration are small , the initial edge lengths themselves are not necessarily small ( based on the tomogram data ) , and we find that the maximum strain ‖r‖/L experienced by edges is 93% . Although the mean strain is significantly smaller , nonlinearities in the polymer force do therefore play a role . We find that the behavior of the random entropic spring network falls within the experimental range of the continuum shear energy , as illustrated in Fig 2 ( c ) –2 ( d ) . Note that , since the deformation is prescribed , the shear energy could be computed analytically . However , we find it convenient to approximate using a discretization on triangles , as done later on to calculate bulk energies in the 3D simulations . Although the continuum shear energy changes deterministically on the elastic sheet , the error bars over the red curves in Fig 2 ( c ) –2 ( d ) come from calculating it on several different random triangulations . These tests show that the total entropic spring energy increases remarkably like the continuum shear energy , and that the discrete cytoskeletal network has an effective shear modulus within the experimentally determined range . Performing this simulation using several random networks , we find that the variance in the computed energies decreases as the domain gets larger . This is expected since the random networks generated appear quite different when viewed close-up , but are alike on larger scales since they all have identical statistical properties . These results demonstrate that , because of the cytoskeleton’s locally irregular structure , the variance in measurements of its shear modulus depends on the scale of observation . The edge-length PDF σ ( D ) and node-node distance PDF ϱ ( D ) used in the random graph algorithm are based on three-dimensional images of the isolated red cell cytoskeleton obtained by cryoelectron tomography ( Fig 3 ( a ) ) . The use of such image data ensures that our model cytoskeletons have realistic statistical properties . Methods for separating and preparing the cytoskeletons for electron microscopy are described in [5] . The tomographic images reveal the convoluted structure and irregular topology of the native cytoskeleton , in contrast to some early cytoskeleton models mentioned in the introduction that assumed the topology of the cytoskeleton to be that of a hexagonal lattice . Although such a hexagonal topology is suggested by electron microscopy images of the spread and negatively stained cytoskeleton , it has been suggested [5 , 33] that this topology results from negative staining and/or network reorganization in response to stretching and adsorption to a carbon substrate . To be able to reproduce the observed irregularity with our random graph model , we first analyze tomographic images to gather statistics on the network topology , contour lengths , and end-to-end distances of putative spectrin tetramers . Since the tomograms do not distinguish between different protein species , in order to extract these distributions from the three-dimensional images , a segmentation algorithm must be used to identify the cytoskeleton constituents . Our segmentation algorithm to extract the statistical data for our model consists of two steps . In the first step , actin polymers are identified within the tomogram using an existing method [34] based on correlation with the spatial electron density of a 13 subunit-long filament of actin ( Fig 3 ) . We use the software package MolMatch [34] , which rotates the electron density computed from the known crystal structure of actin through various Euler angles and computes the correlation at each voxel of the tomogram . The positions with the highest correlations are designated as junctional complexes , subject to the constraint that no two nodes are closer than 14 nm . Although the actin polymers take up a non-trivial volume relative to the cytoskeleton , they are considered to be points for the next step of the segmentation algorithm . In the second step , the tomogram is converted to a three-dimensional binary image by choosing a density threshold and setting all values above and below the threshold to be 1 and 0 , respectively . ( In what follows , the cytoskeleton refers to the set of voxels with value 1 . ) The threshold is chosen in order to produce a mean number of edges per node of μ = 5 , in accordance with published values based on direct inspection of cytoskeletons [5 , 33] . Next , the largest connected component of the cytoskeleton is found by using flooding , i . e . breadth-first search . Restricting attention to nodes within this largest connected component , we next segment the skeleton using a watershed algorithm [35] that , in addition to computing the topology , yields geometrical information about the end-to-end distances and contour lengths of spectrin tetramers . Though similar to a standard breadth-first search , the watershed algorithm is different in that instances of flooding are launched synchronously from each node , stopping in voxels where instances initiated from different nodes meet . The measure of distance implicit in this segmentation is not the Euclidean distance , but rather the shortest path length through the connected component , as approximated by counting the number of steps through adjacent ( non-diagonal ) voxels . Interpreting this procedure in terms of the cytoskeleton structure , the halfway points at which instances of flooding meet are the locations at which spectrin dimers join to form tetramers . These halfway points are also located near the binding sites of the protein ankyrin , which links the cytoskeleton to the red cell membrane . See S1 Video and Fig 4 ( a ) –4 ( d ) for illustrations .
Our first goal is to extract parameters relevant for our simulations from the 3D images produced by electron tomography . In particular , we use our segmentation algorithm to determine the edge-length PDF σ ( D ) and node-node distance PDF ϱ ( D ) , and test whether these distributions are consistent with the assumptions of the random graph algorithm and entropic spring model , respectively . We use two tests to establish that the nodes in the tomogram satisfy the assumption in the section “Generating the cytoskeleton” that the nodes are uniformly distributed . For the first test , we partition the tomogram ( with approximate dimensions 400 nm × 900 nm × 160 nm ) into 125 boxes of approximate size 80 nm × 180 nm × 32 nm . If the nodes are uniformly distributed , the probability that a given node lies within a particular box will be r = 1/125 = 0 . 008 , i . e . the ratio of the box’s volume to the volume of the entire tomogram . More generally , for N total nodes , the probability that k nodes lie within the same box is given by the binomial distribution , which may be approximated as Poisson with mean rN since r is small and N is large . Fig 4 ( e ) shows that the histogram of nodes per box is indeed in close agreement with the Poisson distribution . For the second test , ϱ ( D ) is extracted from the tomogram and compared to an analytic formula for uniformly distributed nodes derived in the section “Density through slab” of S1 Text ( see Fig 4 ( f ) ) . To extract ϱ ( D ) from the tomogram ( see Fig 5 ( a ) ) , we compute the distance Dij between each pair of nodes i and j and create a histogram on the interval [0 , 300] nm . Edge effects have been mitigated by placing periodic copies in the horizontal directions , and only counting those pairs with at least one member in the original volume . To correctly normalize this distribution , we compute the vertically-averaged volume of intersection VI ( η , Dmax ) between the slab of thickness η = 160nm and a sphere of radius Dmax . This computation is similar to those in the section “Density through slab” of S1 Text and we only state the result here: V I ( η , D max ) = π D max 2 η - π η 3 / 3 in the case of interest that Dmax > η . The resulting distribution is consistent with the presence of uniformly distributed nodes in the skeleton , as shown by the close match to the analytical formula computed in the case of uniformly distributed points in a 3D slab . To extract σ ( D ) from data , we make a histogram of the end-to-end distances between nodes identified as neighbors by the segmentation algorithm ( see Fig 5 ( a ) ) ) . We observe that the computed σ ( D ) vanishes below a certain cutoff value . The template-matching algorithm imposes a lower cutoff of 14 nm to prevent multiple identifications of the same junctional complex , but in fact the edge length distribution that arises from the segmentation algorithm reveals very few nodes connected by distances of less than 30 nm . This is consistent with the physical characteristics of the cytoskeleton: since the junctional complexes themselves have a radius of about 15 nm [5] , excluded volume prevents their centers from coming within 30 nm of one other . The contour-length PDF and probability mass function of connections per node are computed similarly ( Fig 5 ( b ) –5 ( c ) ) ) . We find that the distributions extracted by the segmentation algorithm are consistent with the constraint σ ( D ) ≤ ϱ ( D ) /P on inputs to the random graph model described in the section “Generating the cytoskeleton” . Recalling that P is the probability that two nodes are connected , that μ is the average number of connections at each node , and that N is the total number of nodes , P = μ/N under the assumption that all connections are independent . This assumption is justified by the data because , according to Fig 5 ( c ) , the observed node degree distribution is close to being Poisson . Since μ = 5 by design and the total number of junctional complexes satisfies N ≈ 40 , 000 in red cells [36] , the value of P is fixed . Therefore , the inequality σ ( D ) ≤ ϱ ( D ) /P becomes a constraint on the PDF’s ϱ ( D ) and σ ( D ) . Fig 5 ( a ) shows that the extracted distributions satisfy this constraint to within experimental noise ( i . e . the blue histogram lies nearly beneath the green dots ) . The template-matching algorithm for identifying junctional complexes results in a density of approximately 340 points/μm2 , which is in reasonable agreement with the experimentally-determined density of 290 points/μm2 computed using a total of 40 , 000 junctional complexes [36] and the surface area 138 μm2 [9] . However , visual inspection reveals both false positives and false negatives . It would be valuable to validate the template-matching algorithm experimentally , for instance by labeling actin or ankyrin and using super resolution fluorescence microscopy . In order to carry out simulations on whole red cells , we used the random graph algorithm of the section “Generating the cytoskeleton” to generate a full model cytoskeleton on a triangulated surface representing a whole cell using the distributions σ ( D ) and ϱ ( D ) extracted from data together with the target density of 290 nodes/μm2 . The number of random points on each triangle is drawn from a Poisson distribution . Each of these points is given a random position that is chosen independently from the uniform distribution on the corresponding triangle . This is done by generating candidate points within the bounding rectangle of the triangle and then rejecting points that fall outside the triangle . The end result of the above construction is that we have distributed the nodes according to a Poisson process on the whole triangulated surface with the target density ( see the section “Immersed boundary method” in S1 Text for a close-up of the nodes and triangulation ) . For the subsequent step of determining which pairs of nodes are connected by entropic springs , the large number of nodes makes it impractical to test each pair explicitly . Instead , we bin the nodes into Nbox boxes of edge length at least Dmax in each direction . This makes the determination of edges more efficient , since for a given node only the approximately O ( N / N box ) nodes in the same or adjacent boxes must be tested as candidate neighbors . We find that the resulting graph is percolated; over 99% of the total nodes belong to the same connected component . Note that the cytoskeleton tends to stay attached to the membrane in our simulations since all points move in the same interpolated velocity according to the immersed boundary formulation ( Eq ( 16 ) of S1 Text ) . In the absence of a membrane , the model cytoskeleton is compressible; however , in our simulations the cytoskeleton moves in the same velocity field as the incompressible membrane . This is analogous to considering the motion of tracer particles within an incompressible fluid; although the tracer particles themselves do not resist compression , their local density does not change over time by virtue of the incompressibility of the fluid . In order to test the response of our model skeleton to flow , we examined the response of the red cell to different flow conditions using the immersed boundary method ( see the section “Immersed boundary method” in S1 Text ) . The model cytoskeleton resists in-plane shear deformation , whereas the lipid bilayer resists bending and changes in local area . We simulate a red cell with equal internal and external fluid viscosities ( i . e . a red cell ghost ) and examine how the edge length distribution changes during the resulting motion . A shear flow is generated by applying equal and opposite body forces in two planes of the computational domain , as in [1 , 37] . The strength of the resulting flow is given in terms of the dimensionless capillary number G , defined by G = μ γ ˙ a / E , where μ is the dynamic viscosity , γ ˙ is the shear rate , a is the effective cell radius , and E is the shear modulus as above . Placing cells in shear flow produces tank-treading behavior , in which cells elongate and align their long axis toward the flow , with the membrane revolving around the perimeter of the cell in a periodic fashion [38 , 39] . This complex behavior is a good test of the model because tank-treading frequencies can be quantified and compared with existing values in the literature . This test not only helps validate the cytoskeleton model: it can also be used to demonstrate the effect of network dynamics on a cell’s response in flow . In the flow regime we investigate , red cell ghosts undergo a breathing motion that is intermediate between tumbling and tank-treading , the behavior seen at high shear rates . We find the dependence of the nondimensional frequency f = 2 π / ( T γ ˙ ) on the breathing period T computed in our simulations to be consistent with previous studies [1 , 40] ( see Fig 6 ( c ) ) . Over the course of the cell’s breathing motion , we monitor the edge length distribution of its cytoskeleton ( see Fig 6 ( d ) and S2–S4 Videos ) . S4 Video shows that the edge length distribution oscillates with each breathing period . In contrast to the above simulations in which the network connectivity has been taken to be static , there is experimental evidence that the cytoskeleton continually remodels over time . The rate of remodeling is not yet well-characterized; Ungewickell and Gratzer report that the timescale is of the order of 10 minutes for a red cell at rest [42] and Fischer reports a stress relaxation timescale ≳ 10 hours [43] , while others have reported a more rapid , highly shear-dependent remodeling in red cell ghosts with significant implications for the red cell’s deformability [7 , 44] . It has been suggested that hemoglobin may stabilize the cytoskeleton , which could explain the faster remodeling observed in red cell ghosts [43] , but there is no consensus in the literature on the reason for the discrepancy in remodeling timescales . To examine the hypothesis that network dynamics plays a key mechanical role , we test the effect of network dynamics on our model by incorporating rate constants kon and koff for edge formation and breakage , respectively . We model the network dynamics in a stochastic manner using an on-rate that is length-dependent and an off-rate that is independent of length ( see the section “Dynamic connections” in S1 Text and S5 Video , a close-up of the remodeling cytoskeleton in a cell at rest ) . We repeat the shear flow simulations , now including network dynamics , and observe the changes in the cytoskeletal structure over time . In order to follow shape changes , we define I1 ≥ I2 ≥ I3 ≥ 0 to be the ordered eigenvalues of the moment of inertia tensor of the red cell membrane . The moments of inertia are related to the principal axes of an ellipsoid , so that changes in the ratio I1/I2 correspond to shape deformations . Fig 7 ( a ) shows increasing tank-treading periods and overall deformations as koff increases from koff = 0 , 10 , and 100 s−1 , which we interpret as a loss of elasticity and an increase in viscoelastic creep as the network becomes more dynamic . Fig 7 ( a ) shows the breathing period to be ≈0 . 02 s , and in our simulations the network dynamics are observed to have a significant effect when the timescale of remodeling k off - 1 is of the same order , i . e . koff ≈ 100s−1 . One potential physiological advantage of having dynamic network connectivity is that it may decrease the chance of polymer failure . To model this behavior , we assume that a bond is broken irreversibly when its length exceeds L = 200 nm , which is the unfolded contour length of spectrin [45] . This failure model can be interpreted biologically as a spectrin tetramer unfolding upon being sufficiently stretched , so that it no longer acts like a spring , with refolding requiring times so long that it may be neglected over the course of the simulation . Experimental evidence for spectrin unfolding under extension has been presented in [46–48] and ankyrin has also been shown to unfold in response to large forces [49] ) . Upon incorporating polymer failure in this manner , we next ask: do network dynamics decrease the number of irreversibly broken bonds over time ? Somewhat counterintuitively , we find that the presence of network dynamics in shear flow increases the number of edges passing the threshold for irreversible breakage , as shown in Fig 7 ( c ) . The explanation for this observation is that network dynamics makes the cell less elastic , decreasing the shear modulus E and consequently increasing the dimensionless capillary number , which is inversely proportional to E . The benefit of having edges that spontaneously disconnect before the threshold is reached is outweighed by the cost of decreased shear resistance and greater extension seen by plotting the cell’s first principal moment of inertia ( Fig 7 ( b ) ) . To isolate the effect of network dynamics from that of extension in shear flow , we considered a situation in which we prescribe the strain , rather than the shear stress , on the cell . Inspired by optical tweezer experiments and simulations [13 , 50–53] , the cell is attached by stiff springs at both ends to small clusters of virtual tether points . Of the approximately 40 , 000 vertices composing the triangulated mesh , about 1 , 500 vertices are attached to tether points . The tether points are uniformly distributed over 3–4% of the red cell surface area . The motion of the tether points is prescribed to pull the ends of the cell in opposite directions at a constant rate and place the cell under increasing tension . As noted above , prescribing the extension rate rather than the force allows us to isolate the effect of network dynamics from changes in overall shape . To help validate the model , we compare the force-extension curves obtained from simulation to the results of optical tweezers experiments [53] . The force-extension curves calculated using a static spectrin network are in agreement with experiment results ( Fig 8 ( c ) ) . In the following simulations , we replace the worm-like chain model on each edge of the spectrin network by a linear entropic spring force given by Eq ( 3 ) of S1 Text that has the same behavior at small strains . Although the linear spring force allows for infinite extension in principle , large extensions will not occur in our simulations since spectrin tetramers break irreversibly upon reaching their contour length . Further justification is provided in the section “Dynamic connections” of S1 Text . Upon extending the cell by about 100% over the course of approximately 0 . 23 seconds , we find that the total number of irreversible breakage events decreases by about 20% in the presence of network dynamics ( Fig 9 and S6 Video ) . As shown in Fig 9 , the cytoskeleton edges become less dense in regions of high strain where more irreversible damage occurs . Since the spectrin network initially has approximately 110 , 000 edges , the 2 , 000 edges broken over the course of our simulation make up less than 2% of the total network and have a negligible effect on the overall cell mechanics . However , the difference in breakage rates is greatly magnified over the course of time and in the context of positive feedback . Whereas we have considered only one full deformation cycle because of computational constraints , the average transit time through the circulation is approximately twenty seconds , so that a red cell experiences on the order of 105 such cycles over their lifespans [32] . These deformations can be extreme , e . g . when passing across the spleen’s narrow endothelial slits having dimensions of approximately 2 μm ×1 μm [54 , 55] . There is positive feedback because the more broken bonds a cell has , the less able it is to return to its rest shape after large deformations , and therefore the more likely it is that remaining bonds will become progressively stretched and break . It is surprising that , depending on the type of deformation the red cell is subjected to , the presence of dynamics can either lead to more or less damage to the cytoskeleton , but it is consistent with previous studies showing that dynamics can generate both enhanced and deficient spectrin networks [17] . This prediction of how cytoskeletal dynamics decrease the number of polymer failures over time could be tested experimentally by using optical tweezers to deform red cell mutants in which the persistence of spectrin tetramer connections has been altered . For example , hyperstable spectrin tetramers that disconnect less frequently than wild type cells and have been produced in transgenic mice ( N . Mohandas , New York Blood Center , personal communication , 2015 ) .
We have used our image-based model to investigate the physiological consequences of certain cytoskeletal properties at microscopic scales , specifically the importance of network dynamics . We have used simulations to address the consequences of allowing the spectrin network to reorganize over time , an effect which is thought to take place in vivo . This model predicts that , in the presence of cytoskeletal reorganization , repeated deformations will lead to changes in the structure of the cytoskeleton . When a cell undergoes tank-treading in shear flow , we find that faster cytoskeletal reorganization leads to more irreversibly broken spectrin tetramers and a smaller dimensionless tank-treading frequency . This is because the loss of elasticity from remodeling leads to larger capillary numbers , which causes greater extensions and has previously been shown to generate smaller dimensionless tank-treading frequencies [41 , 56] . In contrast to the case of shear flow , results from our model suggest that , when the cell is placed under a repeated strains , cytoskeletal dynamics may play a protective role by allowing spectrin tetramers to disconnect before they would break . In particular , we found that by allowing transient disassembly of spectrin tetramers , the cytoskeleton suffered fewer irreversibly broken edges in response to applied strains that simulate the conditions of optical tweezer experiments . We used a relatively fast disassociation constant of at least koff = 10s−1 , based on the rapid remodeling that was previously reported [7 , 44] . However , recent measurements suggest that the stress relaxation timescale is at least 10 hours [43] , indicating that the true rate of remodeling is significantly slower than the value of koff = 10s−1 used for our modeling . Any potential protective effect of dynamics may therefore only be significant on the timescale of hours to days , over which cells undergo hundreds to thousands of deformation cycles . The same results also suggest that , under conditions of fixed strain , mutant red cells with static connectivity may accumulate damage more quickly than dynamic wild type cells . Further study using biophysically-realistic models of networks dynamics , together with optimized algorithms and higher thoroughput simulation techniques , is needed to quantify these predictions over a wider range of parameter space . Empirical testing of this hypothesis by using optical tweezers or microfluidic devices [57] to apply strain or shear stress to red cells with hyperstable spectrin tetramers would be an important step in validating the model and identifying any possible protective effect of network dynamics on the cytoskeleton . This model could be used to investigate in detail the consequences of mutations that occur in hereditary elliptocytosis , a genetic disorder that affects proteins responsible for horizontal connections within the spectrin cytoskeleton [58] . The statistical properties of the spectrin network are likely to be affected by these mutations . Our model makes it possible to investigate mechanisms of the disease , since we can define parameters that govern cytoskeletal structure , including the number of junctional complexes , the number of polymers attached to each junctional complex , the length of the polymers , the polymer elasticity , and network dynamics . The microscopic details that govern polymer connections in the cytoskeleton can have an effect on the macroscopic behavior , as evidenced by the change in the tank-treading frequency caused by dynamics within the spectrin network . As noted in the introduction , composite models have been developed [16–18] that capture the disruptions in vertical connections between the membrane and cytoskeleton that occur in hereditary spherocytosis and during extreme deformations . Although we have enforced strong vertical connections in the present work , it is possible to incorporate relative motion within the immersed boundary framework by including a slip velocity [59] . Given the recent progress on composite models this would be an interesting application of our image-based approach . This approach starts with structural tomography data , uses a random graph algorithm to generate a representative cytoskeleton , and then simulates the behavior of a cell with those cytoskeletal properties under realistic flow conditions . In order to make this computational framework generally valuable for studying questions in red cell physiology , the existing limitations of this method must be addressed . With regard to the segmentation algorithm used for processing tomographic images , one challenge is to accurately identify the junctional complexes linked by spectrin . The method used , which involves cross-correlation to actin’s known electron density , is appealing because it gives reasonable results and can be done using freely available third-party software . However , visual inspection suggests both false positives and false negatives . Establishing the accuracy of this method , e . g . through experiments in which different cytoskeletal components are labeled by streptavidin [60] , would be an important validation of the node identification step . Further , the threshold used to binarize the tomograms is presently determined by prescribing the mean degree μ , but it would be preferable if μ were an output out of the data analysis . We have not taken this approach since μ has been found to be sensitive to the threshold used , making it difficult to identify a value robust to the image processing parameters . It is possible that using a single threshold is too simplistic; by considering the known electron densities of spectrin and actin , it may be possible to compute more appropriate independent thresholds . Of course , tomograms with a higher signal-to-noise ratio would greatly aid our analysis and the new direct detectors currently being used for electron microscopy are likely to make this possible . Several simplifying assumptions have been made in the cytoskeletal model presented here , including the independence of edges in the random graph model , the treatment of spectrin polymers as Hookean springs with no self-avoidance , and our particular implementation of spectrin network dynamics . Further investigation may reveal the need for more complicated models . For example , in addition to forming tetramers , spectrin dimers can join together in the red cell cytoskeleton to form hexamers and other higher-order oligomers . Although the number of such higher-order oligomers is significantly less than the number of tetramers , it has been shown that including even a relatively small number of hexamers can drastically change the network elasticity [22] . We note that spectrin cytoskeletons play important physiological roles in other settings as well: they have been shown to be important regulators in Drosophila development and to be present in axons , to which they may provide structural stability to help the axons span long distances [61 , 62] . They also play a role in cardiomyocyte differentiation and heart development [63] . Dysfunction in the spectrin-dependent cytoskeleton in cardiomyocytes has been shown to underlie severe arrhythmia associated with aberrant calcium phenotypes , identifying spectrin as critical for normal myocyte electric activity [64] . Although we have focused here on spectrin networks , the modeling of networks made up of polymers besides spectrin is of course of significant interest . For example , Lee et al . [65] examined cytoskeletal remodeling in fibroblasts , in which the cytoskeleton is made up of actin-based stress fibers , and Magatti et al . examined the complex polymer networks that occur in fibrin gels during blood clotting [66] . These works are related to ours in that they also involve initializing a random network of polymers , so that the algorithm described here to generate a random graph with specified statistical properties may be applicable . With regard to the image processing , our segmentation algorithm falls into the general class of thinning algorithms [67] , which have been used elsewhere for biological applications such as extracting the structure of collagen gels [68 , 69] . Although the present work has not been focused on the numerical details of the immersed boundary method , we nevertheless wish to mention a few challenges and potential research directions related to simulating red cells under flow . Whereas red cells in our bodies repeatedly experience many cycles of deformation over their lifespans , at present we are unable to simulate more than a few cycles because of the prohibitive computational cost of long simulations at high resolution . Continual deformations may also lead to numerical challenges in the form of long and skinny triangles in the mesh . In our simulations the aspect ratio , i . e . the ratio of the longest triangle edge length to the shortest triangle edge length , ranges from an average of 1 . 1 to an average of 1 . 3 in the tank treading simulations and of 3 . 3 in the optical tweezer simulations . The maximum aspect ratio over all triangles ranges from 1 . 8 initially to 13 in the tank treading simulations and to 920 in the optical tweezer simulations . Given the nature of the deformation in the optical tweezer simulation , it is physically reasonable that triangles become severely skewed in that case . This interplay of numerical and structural stability merits further study given that skewed triangles can decrease the accuracy of discretizations on the mesh [70] . Taken as a whole , this work describes a method to use tomographic data as a basis for simulating the effects of changes in cytoskeletal structure and dynamics on how red cells respond to different flow conditions . By applying this framework to a wider selection of tomographic samples , we believe it can provide a more detailed understanding of the cytoskeleton and its role in disorders affecting red cell fluid mechanics .
|
Red blood cells are responsible for delivering oxygen to tissues throughout the body . These terminally differentiated cells have developed a fascinating flexibility and resiliency that is critical to navigating the circulatory system . Far from being rigid bodies , red blood cells adopt biconcave disk shapes at equilibrium , parachute-like shapes as they move between large vessels and small capillaries , and more extreme shapes as they traverse the endothelial slits of the spleen . Understanding the remarkable mechanical properties that allow red cells to experience such large deformations while maintaining structural integrity is a fundamental question in physiology that may help advance treatments of genetic disorders such as hereditary spherocytosis and elliptocytosis that affect red cell flexibility and can lead to severe anemia . In this work , we present a model of the red blood cell cytoskeleton based on cryoelectron tomography data . We develop an image processing technique to gather statistics from these data and use these statistics to generate a random entropic network to model the cytoskeleton . We then simulate the behavior of the resulting red blood cells in flow . As we demonstrate through simulations , this method makes it possible to examine the consequences of changes in microstructural properties such as the rate of cytoskeletal remodeling .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
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"physiology",
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"spectrins",
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] |
2017
|
Image-based model of the spectrin cytoskeleton for red blood cell simulation
|
New Caledonia and French Polynesia are areas in which arboviruses circulate extensively . A large serological survey among horses from New Caledonia and French Polynesia was carried out to investigate the seroprevalence of flaviviruses in the horse population . Here , 293 equine sera samples were screened for flaviviruses using a competitive enzyme-linked immunosorbent assay ( cELISA ) . The positive sera were then confirmed using a flavivirus-specific microsphere immunoassay ( MIA ) and seroneutralization tests . This serosurvey showed that 16 . 6% ( 27/163 ) and 30 . 8% ( 40/130 ) of horses were positive for cELISA tests in New Caledonia and French Polynesia , respectively , but the MIA technique , targeting only flaviviruses causing neuro-invasive infections in humans and horses ( i . e . West Nile virus [WNV] , Japanese encephalitis virus [JEV] and tick-borne encephalitis virus [TBEV] ) , showed negative results for more than 85% ( 57/67 ) of the cELISA-positive animals . Seroneutralization tests with the main flaviviruses circulating in the South Pacific revealed that 6 . 1% ( 10/163; confidence interval [95% CI] 3 . 0%-11 . 0% ) of sera in New Caledonia and 7 . 7% ( 10/130; 95% CI 3 . 8%-13 . 7% ) in French Polynesia were positive for dengue virus serotype 1 ( DENV1 ) and 4 . 3% ( 7/163; 95% CI 1 . 7%-8 . 6% ) in New Caledonia and 15 . 4% ( 20/130 , 95% CI 9 . 7%-22 . 8% ) in French Polynesia were found positive for Zika virus ( ZIKV ) . Seroprevalence of the JEV and WNV flaviviruses on the 293 samples from both island groups were comparatively much lower ( less than 2% ) . This seroprevalence study in the horse population shows that horses can be infected with dengue and Zika viruses and that these infections lead to seroconversions in horses . The consequences of these infections in horses and their role in ZIKV and DENV epidemiological cycles are two issues that deserve further investigation .
French overseas territories located in the South Pacific include New Caledonia , French Polynesia and Wallis and Futuna . New Caledonia is composed of one main island and several archipelagoes , and French Polynesia is composed of five archipelagoes ( Society , Marquesas , Tuamotu , Gambier and Austral islands ) . Many arthropod-borne viruses ( arboviruses ) circulate in the Pacific Islands , with a series of epidemics caused by the four serotypes of dengue virus ( DENV ) documented during the last 50 years , and the presence of emerging arboviruses such as Zika virus ( ZIKV ) , which first occurred in the Yap Islands ( Federated States of Micronesia ) in 2007 or chikungunya virus ( CHIKV ) reported since 2012 [1 , 2] . DENV , a flavivirus that causes a disease with varying descriptions–ranging from asymptomatic infection to hemorrhagic dengue fever sometimes progressing to a shock syndrome–is the most prevalent arbovirus infection in humans in tropical and subtropical countries . Viruses belonging to the dengue virus species are classified in four distinct serotypes ( DENV1-DENV4 ) and infection with one viral serotype does not provide protection against the other three [3] . DENV has been circulating for a long time in the Pacific region with sporadic or rare epidemic outbreaks . Its circulation is characterized by waves of one dominant serotype [4 , 5] . In 2013 , for the first time , all four serotypes were circulating in the Pacific Islands [6] . In New Caledonia , an important outbreak recorded in 2013 was due to the DENV1 serotype [1]; in French Polynesia , not only DENV1 , but also DENV3 ( which had not been reported since 1996 in the South Pacific ) reemerged and spread during the 2013–2017 period for DENV1 and in 2013 for DENV3 [7 , 8] . Conversely , ZIKV , another flavivirus involving mild symptoms similar to those caused by DENV , was not present in New Caledonia or French Polynesia until October 2013 when the virus emerged in French Polynesia and caused a large outbreak [9 , 10] . In New Caledonia , the first autochthonous cases were reported by mid-January 2014 due to cases imported from French Polynesia to New Caledonia in late November 2013 and subsequently spread extensively in late August 2014 [11] . Other globally spreading mosquito-borne flaviviruses can circulate in the Pacific Islands . West Nile virus ( WNV ) is present in Asia and considered enzootic in Australia ( Kunjin virus , lineage 1b of WNV ) , whereas Japanese encephalitis virus ( JEV ) causes the most common mosquito-transmitted encephalitic disease in Asian countries , its presence stretching from Oceania to northern Australia [12–14] . Nevertheless , in New Caledonia and French Polynesia , no active circulation of these two neurotropic flaviviruses has been documented [15 , 16] . In New Caledonia and French Polynesia , DENV and ZIKV are transmitted to humans by mosquitoes belonging to genus Aedes , with Aedes aegypti present in most Pacific Islands as well as local mosquito species , such as Ae . polynesiensis in French Polynesia [1] . Humans are the primary amplification host for DENV and ZIKV . However , female mosquitoes–even the highly anthropophilic Ae . aegypti thriving in human-occupied habitats , such as urban areas–can feed on other vertebrate hosts , and anti-ZIKV antibodies have been detected in several domestic species such as horses , goats and cows [17–20] . A large serological survey conducted in horses from New Caledonia and French Polynesia with a competitive enzyme-linked immunosorbent assay ( cELISA ) revealed positive sera against flaviviruses . Our study sought to ( 1 ) identify the flaviviruses , including DENV and ZIKV , circulating in the horse population and causing this seroconversion , ( 2 ) compare horse seroprevalence in New Caledonia and French Polynesia , and finally ( 3 ) identify possible risk factors associated with this flavivirus seropositivity .
Two equine serological serosurveys were carried out in September 2015 and April 2016 in New Caledonia and French Polynesia , respectively , to determine the seroprevalence of flaviviruses in the equine population . The serological samples were collected once with the kind support of the chief veterinary officers from the two territories ( Dr Denise Desoutter and Dr Hervé Bichet , chief veterinary officers of New Caledonia and French Polynesia respectively ) . Our research was performed in accordance with an approved French Institutional Animal Care and Use protocol during surveillance studies . In New Caledonia , the 163 equines of the study were located in 20 municipalities distributed across different parts of the main island ( Fig 1 ) . In French Polynesia , the survey was conducted on the Marquesas Islands ( 24 in Nuku Hiva and 27 in Hiva Oa ) and on 79 equines in Tahiti Island . Data collected for each animal included the year of birth , location at the sampling date ( municipality for New Caledonia and island for French Polynesia ) , origin ( resident [i . e . born in New Caledonia or French Polynesia , respectively] or imported ) and the date of importation when relevant . In New Caledonia , 40 horses were imported ( 29 from Australia , 4 from New Zealand and 7 from France ) while in French Polynesia , 17 were imported ( 15 from New Zealand and 2 from New Caledonia ) . Horse sera were screened using a cELISA test ( ID Screen West Nile Competition ELISA kit , ID Vet , Montpellier , France ) . This ELISA uses plates pre-coated with the envelope ( E ) protein of WNV and measure the competition between antibodies present in the animal serum tested and a monoclonal anti-WNV . E antibody conjugated to horseradish peroxidase ( HRP ) . 50μL of samples mixed with equal volume of dilution buffer were added to the wells coated with recombinant WNV . E protein during 90 min at room temperature ( RT ) . The anti-E antibodies , if present , formed an antigen-antibody complex . 100μL of monoclonal anti-WNV . E antibody peroxidase ( HRP ) conjugate was then added to the wells during 30 minutes at room temperature ( RT ) , possibly forming an antigen-conjugate-HRP complex . After addition of 100μL of substrate solution during 15 minutes at RT , coloration was stopped by the addition of 100μL of stop solution and micro-plates were read with a spectrophotometer at 450 nm wavelength . Results were validated if the mean value of the optical density ( OD ) of the negative control ( NCOD ) was > 0 . 7 , and the mean value of the positive control OD ( PCOD ) was < 30% of the NCOD . Then , we computed the S/N percentage ( S/N% ) : 100 * sample OD / NCOD . Samples showing a S/N% ≤ 40% were considered as positive . Those with 40% < S/N% ≤ 50% were considered as doubtful . Samples with S/N% > 50% were considered as negative . This test detects antibodies directed against the WNV envelope glycoprotein , but cross-reactions with mosquito-borne flaviviruses ( USUV , JEV ) and also more distant tick-borne flaviviruses ( TBEV ) have been reported [21–27] . WNV belongs to the JEV serocomplex , sharing 76% of the envelop aminoacid ( aa ) homology with JEV . Other serocomplexes include tick-borne encephalitis virus ( TBEV ) , ZIKV and DENV1 sharing 35% , 51% and 46% aa homology respectively with the E protein of WNV . Furthermore , conserved epitopes beyond flaviviruses , are present at the surface of this protein . The cELISA test represents though , a convenient tool to detect antibodies directed against viruses belonging to the genus Flavivirus ( WNV or other flaviviruses ) . A flavivirus microsphere immunoassay ( MIA ) was performed on positive cELISA samples with detection of flaviviruses that cause severe neuro-invasive infections in humans and horses . Briefly , the recombinant soluble ectodomain of the WNV envelope ( E ) glycoprotein ( WNV . sE ) and the E domain III ( rEDIII ) of WNV , JEV and tick-borne encephalitis virus ( TBEV ) containing virus-specific epitopes was kindly provided by Dr Philippe Desprès and were synthesized using the Drosophila S2 expression system [28] . Antigens produced were covalently bound to fluorescent beads following the protocol previously described in refs . [25 , 28] . Diluted serum samples ( 1:100 ) were incubated with the beads for 1 h . A secondary biotinylated goat anti-horse IgG ( dilution 1:500; Jackson Immuno Research Inc . ) was then added . After an incubation of 45 min , the reaction was revealed with streptavidin R-phycoerythrin conjugate ( SAPE; 1 μg/mL; Qiagen ) , diluted to 1:100 . The median fluorescence intensity ( MFI ) of each microsphere set was quantified using a Bio-Plex 200 instrument ( Bio-Rad Laboratories ) . The cutoffs of WNV . sE , WNV . EDIII , JEV . EDIII and TBEV . EDIII antigens were found to be 17 , 54 , 55 and 61 , respectively , as described in ref . [28] . Flaviviruses identified by MIA and all undetermined ELISA-positive flavivirus samples were further investigated using virus-specific microneutralization tests ( MNT ) against the main flaviviruses reported in the area where the sera were collected ( i . e . JEV , WNV , ZIKV and DENV1 ) . The World Organization for Animal Health ( OIE ) and the World Health Organization ( WHO ) recommend the use of the plaque reduction neutralization test ( PRNT ) with a threshold plaque reduction level of 90% ( PRNT90 ) as the gold standard confirmatory assay [29 , 30] . The MNT is a modification of the PRNT90 . Its performance is comparable to the PRNT90 and allows a larger number of samples to be screened using cell microplates [31] . Neutralizing antibody titers were assessed in 96-well cell culture plates on Vero NK cells ( kindly provided by Philippe Desprès ) with the JEV genotype III Nakayama strain ( GenBank accession no . EF571853 ) , the WNV lineage 1 IS-98-ST1 strain ( AF481864 ) following the protocol described in ref . [28] . Heat-inactivated sera , serially diluted ( 1/5 to 1/3645 by serial 1/3 dilutions ) were mixed with an equal volume ( 50 μL ) of DMEM containing 100 tissue culture infectious dose 50 ( TCID50 ) of WNV or JEV strains . After incubation of the plates at 37°C for 1 . 5 h , 2x 10^4 Vero cells in 100 μL of DMEM were added to every well . Plates were incubated at 37°C for 3 days and presence of cytopathogenic effects ( CPE ) were observed under a light microscope . Results were validated if ( i ) CPE were absent in the mock infected control , ( ii ) CPE were observed in the infected control , ( iii ) virus titre was equal to the defined titre ±25% TCID50 per well , ( iv ) no protective effect was seen with the negative reference serum and ( v ) the positive reference serum protected Vero cells from infection . A serum was considered negative if CPE were observed at all the serum concentration tested . A serum was considered as positive , if it displayed protection ( no CPE ) at the 1/10 dilution; its titre was calculated as the inverse of the last dilution at which cells were protected . MNT with the ZIKV strain/PF/2013 ( GenBank accession no . KJ776791 . 2 ) [32] and DENV1 , strain Djibouti/2000 were performed according to the following protocol described in ref . [33]: Serial dilutions of heat inactivated serum samples ( 1/10 for the first dilution followed by serial half dilutions until 1/80 ) were mixed with an equal volume ( 50 μL ) of DMEM containing 50 TCID50 of ZIKV or DENV1 strains . After incubating the mixtures at 37°C for 1 h , each virus-diluted serum sample ( 0 . 1 ml ) was inoculated onto 96-well microtitre plates that had been seeded 24 h earlier with Vero NK cells , 105 cells/ml . Plates were incubated at 37°C for 4 days ( ZIKV ) and 6 days ( DENV1 ) and cytopathogenic effects were observed under a light microscope . The assay was validated as above and the serum was considered positive if cells were protected at the 1/20 serum dilution . Due to serological cross-reactivity induced by viruses belonging to the Japanese encephalitis virus serocomplex ( i . e . JEV and WNV ) , the MNT results were interpreted according to the following rules: ( 1 ) if the antibody titer was positive for only one flavivirus or if one titre was fourfold greater than the other , the serum was identified as containing antibodies against the virus displaying the highest positive serum dilution; ( 2 ) for differences in antibody titers less than fourfold , the virus identification could not be determined . ZIKV does not belong to the dengue serocomplex [3] . For this reason and due to high endemicity of DENV1 in these areas and emergence of ZIKV at least 1 . 5 years before horse sampling , a serum was classified positive for both DENV1 and ZIKV in case of positive results against these two viruses and the corresponding animals were assumed to have been exposed to both viruses . In cases of negative MNT results , but positive ELISA , the serum was classified as positive against an undetermined flavivirus . Seroprevalence rates were computed and compared according to the area ( New Caledonia or French Polynesia ) , and for resident and imported horses , using Fisher’s exact tests . Doubtful cELISA test results were considered as negative when computing seroprevalences . The relationship between age and seropositivity was investigated using Student’s t-tests . All statistical analyses were performed using R vers . 3 . 2 . 3 [34] .
Out of 163 sera from New Caledonia , 27 ( 16 . 6% , 95% confidence interval [CI]: 11 . 2%-23 . 1% ) were positive , 3 ( 1 . 8% ) doubtful , and 133 ( 81 . 6% ) negative . Out of the 130 sera collected in French Polynesia , 40 ( 30 . 8% , 95% CI: 23 . 0%-39 . 5% ) , were positive , 5 ( 3 . 8% ) doubtful and 85 ( 65 . 4% ) negative ( Table 1 ) . The flavivirus-positive equines were located in different parts of New Caledonia and were located on Marquesas ( 8 ) and Tahiti ( 32 ) islands in French Polynesia . Based on the cELISA results , the overall immunoglobulin ( Ig ) -anti-flavivirus seroprevalence was significantly lower in New Caledonia than in French Polynesia ( Fisher’s exact test , p = 0 . 0005 ) . This difference was observed in resident horses ( Fisher’s exact test , p = 0 . 02 ) and in imported animals ( Fisher’s exact test , p = 0 . 03 ) . An in-house flavivirus multiplex assay was used to screen specific IgG antibodies against encephalitic flaviviruses among the 67 cELISA-positive sera from French Polynesia or New Caledonia . Based on the cut-offs defined in ref . [28] , 91% ( 61/67 ) of cELISA positive sera were also positive to WNV . sE beads confirming the infection with a flavivirus . The WNV . sE based MIA was found to be less sensitive ( Se ) than ELISA for the detection of anti-flavivirus antibodies . Only 9 . 0% ( 6/67 ) and 6 . 0% ( 4/67 ) of sera were also positive for JEV and WNV , respectively . Therefore , more than 85 . 1% ( 57/67 ) were deemed infected with undetermined flaviviruses . To confirm cELISA and MIA results , an MNT against WNV and JEV was carried out at ANSES , and another MNT–against DENV1 and ZIKV–was done at the Armed Forces Biomedical Research Institute ( Institut de recherche biomédicale des armées [IRBA] ) .
Our study based on three different and complementary serological methods allowed the detection and identification of flaviviruses responsible for seropositivity in horses sampled in New Caledonia and French Polynesia . Generic anti-flavivirus antibodies have been detected by cELISA in 67/293 horse sera and 91% ( 61/67 ) of them were also found reactive to the WNV . sE bead by MIA . These two independent methods corroborated the detection of anti-flavivirus antibodies in a significant proportion of the horse population [28] . Identification of the infecting flavivirus is challenging because most serological tests identifying past-exposure to flaviviruses through IgG detection , and in particular MIA and cELISA used in this study , are based on the envelop ( E ) antigen sharing flavivirus cross-reactive epitopes located in particular within the highly conserved fusion peptide in E . DII [35] . To improve the specificity of diagnostic assays , flavivirus positive sera were tested by MIA using WNV . EDIII , JEV . EDIII and TBEV . EDIII antigens which contain virus-specific-epitopes [36 , 37] and by MNT against WNV and JEV . The results of the two methods were in accordance on three sera ( two WNV positive sera and one JEV positive sera ) . Seven MIA samples were found positive against JEV ( 5 sera ) and WNV ( 2 sera ) and negative with JEV and WNV MNT respectively . Such discordant results could originate from a higher sensitivity or a lack of specificity of the flavivirus MIA technique . The fact that 4 out of the 7 WNV/JEV MIA positive and MNT negative sera were found ZIKV positive by MNT support the second hypothesis . Correspondingly , WNV . EDIII and JEV . EDIII share 74% of aa homology and a higher homology with ZIKV . EDIII ( aa homology of 59% and 52% respectively ) than with DENV1 . EDIII ( aa homology of 40% and 47% respectively ) or TBEV . EDIII ( aa homology of 29% and 36% respectively ) . Based on the low percentage of horses found positive against WNV and JEV , the sera were tested by MNT against ZIKV and DENV1 at the IRBA laboratory . The standard operating procedures of the two MNT performed at ANSES ( WNV and JEV ) and IRBA ( DENV1 and ZIKV ) are comparable ( with input virus 2-fold lower for ZIKV or DENV1 than for JEV and WNV and with positivity thresholds two times higher for ZIKV and DENV1 than for WNV and JEV ) , affording good specificity in flavivirus identification by MNT . Consequently ZIKV and DENV infections in horses can be established with high confidence in French Polynesia and New Caledonia . In the Pacific Islands , mosquito-borne flaviviruses such as DENV or ZIKV are transmitted by Aedes mosquitoes with Ae . aegypti being the main competent vector present in French Polynesia and New Caledonia and Ae . polyniensis the secondary vector [1 , 17] . No comparative competence studies in Aedes mosquitoes , between DENV and ZIKV local isolates , are available in South Pacific . However , sequential studies support higher dissemination and transmission rates of indigenous mosquito species for DENV1 only [38–40] . The presence of specific antibodies against DENV and ZIKV in horse populations suggests that , as referenced in the literature , the Aedes genus , known to have primate reservoir hosts , can opportunistically feed on other mammalian hosts . In Indonesia , antibodies against ZIKV have been detected in various vertebrate hosts such as ducks , goats , horses , bats and buffaloes [18] . Blood feeding studies of Ae . aegypti mosquitoes indicated that 99% and >85% engorged Ae . aegypti females fed on humans in Thailand and in islands of India respectively [41 , 42] . One study also demonstrated that horses correspond to minor feeding hosts for Ae . aegypti , far below humans and dogs ( ≤0 , 8% vs 76–79% and 18–21% for humans and dogs respectively ) [19] . Such a finding can help explaining differences in exposure levels of humans and horses to Aedes-borne viruses: 80% and 49% seroprevalence , for DENV1 and ZIKV respectively , in the 2014 French Polynesia human population in comparison with 7 . 7 and 15 . 4% in the French Polynesia equine population [43 , 44] . However , if Ae . albopictus , with a more flexible feeding pattern , spread to South Pacific , horse exposure to DENV or ZIKV could be enhanced . The South Pacific has faced successive outbreaks due to one of the four DENV serotypes [4 , 6 , 45] . DENV1 had been circulating for several years with epidemic periods in 2006–2007 in French Polynesia and 2008–2009 in New Caledonia before reemerging again in 2012–2013 in New Caledonia and French Polynesia [8 , 46] . Accordingly , there were no significant differences in the DENV1 seropositivity rates in resident horses in New Caledonia ( 6 . 5%; 95% CI 2 . 1%-10 . 9% ) and French Polynesia ( 7 . 1%; 95% CI 2 . 4%-11 . 8% ) . This result suggests similar circulation dynamics for DENV1 in both areas . However , interestingly , the differences between New Caledonia and French Polynesia in recent flavivirus circulation correlate with the different seroprevalence levels observed in our study . This study shows that the age of the horse was associated with DENV1 seropositivity in French Polynesia , but not in New Caledonia . In 2012–2013 , New Caledonia experienced an important outbreak with more than 10 , 000 human cases reported [1 , 46] . This large wave of DENV1 infections may have randomly affected the different age classes in the New Caledonia equine population . Conversely , in French Polynesia , although DENV1 reemerged in 2013–2017 , the age-seroprevalence correlation suggests that the positive horses detected in our study likely result from older DENV1 circulation events , because the seroprevalence rate is expected to increase with increased duration of exposure to the virus [8] . The situation is clearly distinct for ZIKV . The first Pacific ZIKV outbreak occurred in 2007 on Yap Island before its reemergence in French Polynesia in October 2013 [17 , 47] . This new introduction resulted in an explosive outbreak in French Polynesia and spread throughout the South Pacific to New Caledonia [48] . At the end of the outbreaks , French Polynesia reported 8723 suspected human cases and more than 30 , 000 estimated clinical visits due to ZIKV , whereas 11 , 000 estimated cases and 1 , 400 confirmed cases had been reported in New Caledonia [1] . Similarly , our study demonstrated a higher seroprevalence rate in French Polynesia resident horses than in New Caledonia . The absence of a significant age effect in French Polynesia suggests that horse population was naïve before this new emergence . In New Caledonia , due to the low number of seropositive resident horses ( 3 animals or 1 if the 2 sera positive for both ZIKV and DENV1 are not taken into account ) the link between seropositivity and age is more difficult to assess . Finally , the low seroprevalence of JEV and WNV flaviviruses in equines ( 2 . 4% in NC and no positive samples in FP ) suggests the absence of past active circulation of these viruses in New Caledonia and French Polynesia , which is consistent with levels of JEV and WNV seroprevalence ( < 1 . 5% ) among blood donors in French Polynesia in 2011–2013 , for example [16] . The positive results for WNV or JEV obtained for four New Caledonia horses native to Australia may attest to past vaccination or exposure to these viruses before their importation in New Caledonia and French Polynesia . We did not identify which flavivirus was involved in the infection of 6 . 1% and 9 . 2% of cases in New Caledonia and French Polynesia , respectively . The lower sensitivity of MNT compared to cELISA and/or the circulation of other flaviviruses , particularly the three other DENV serotypes , may explain why no virus could be identified [21] . Several other flaviviruses have been specified in Australasia , but not described in New Caledonia nor in Polynesia . In Australia or Papua New Guinea , Murray Valley Encephalitis Virus , a Culex-borne flavivirus classified in the JEV serocomplex , the Kokobera virus ( and its Stratford subtype ) associated with polyarticular disease in humans also found in the Culex-group but ranking in a separate serocomplex have been described . Finally two Aedes-borne flaviviruses ( Sepik/Fitzroy River virus , closely related to the African Wesselsbron virus , and the Edge Hill virus ) have been regularly reported in recent years . Animals infected with such flaviviruses would be expected to generate false positive reactions with the ID Vet WNV cELISA kit , while not reacting in specific WNV/JEV ( apart from MVEV classified in the JEV serocomplex ) , or ZIKV/DENV MNT , as inferred from the low genetic and antigenic relationships between Kokobera and WNV/JEV and between Sepik River virus , Edge Hill virus , DENV and ZIKV [49–52] . In conclusion , our study clearly shows that horses can be infected by Aedes mosquito-borne viruses such as DENV1 or ZIKV , which are known to have a primate reservoir . The seroprevalence rate for these viruses associated with serological cross-reactions involving related flaviviruses challenges the diagnosis of flaviviruses in horses [21] . Although the DENV1 and ZIKV seropositivity rates are clearly lower in horse populations than in the general human population ( 7 . 7 and 15 . 4% of antibodies against DENV1 and ZIKV respectively , in the French Polynesia equine population compared to 80% and 49% , respectively , in the 2014 French Polynesia human population ) [43 , 44] , our study emphasizes the need to acquire additional data regarding domestic animals in ZIKV and DENV epidemiological cycles .
|
New Caledonia and French Polynesia , located in the South Pacific , are facing circulation of dengue virus ( DENV ) for a long time and emergence of Zika virus ( ZIKV ) since 2013 . A large serosurvey among horses’ population from these two islands was carried out to investigate the seroprevalence of the main flaviviruses circulating in the South Pacific . We find out that 6 to 7% of equine sera tested were positive for DENV serotype 1 in the two islands and 4% and 15% were positive for ZIKV in New Caledonia and French Polynesia respectively . Our study highlighted serological evidence of DENV serotype 1 and ZIKV infections of horses leading to meaningful seroconversion . Seroprevalence of other mosquito-borne flaviviruses ( i . e . Japanese encephalitis and West-Nile viruses ) were comparatively much lower ( less than 2% ) in New Caledonia and French Polynesia groups suggesting the absence of past active circulation of these viruses in both islands . This finding emphasized the need to investigate the consequences of such infections in the horse population and to determine the role of domestic animals in ZIKV and DENV epidemiological cycles .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
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"geomorphology",
"dengue",
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"laboratory",
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"geographical",
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2019
|
Serological evidence of infection with dengue and Zika viruses in horses on French Pacific Islands
|
The Catabolite Activator Protein ( CAP ) is a showcase example for entropic allostery . For full activation and DNA binding , the homodimeric protein requires the binding of two cyclic AMP ( cAMP ) molecules in an anti-cooperative manner , the source of which appears to be largely of entropic nature according to previous experimental studies . We here study at atomic detail the allosteric regulation of CAP with Molecular dynamics ( MD ) simulations . We recover the experimentally observed entropic penalty for the second cAMP binding event with our recently developed force covariance entropy estimator and reveal allosteric communication pathways with Force Distribution Analyses ( FDA ) . Our observations show that CAP binding results in characteristic changes in the interaction pathways connecting the two cAMP allosteric binding sites with each other , as well as with the DNA binding domains . We identified crucial relays in the mostly symmetric allosteric activation network , and suggest point mutants to test this mechanism . Our study suggests inter-residue forces , as opposed to coordinates , as a highly sensitive measure for structural adaptations that , even though minute , can very effectively propagate allosteric signals .
The regulation of DNA transcription is one of the key factors for the control of cell functions , a classic example of which is the lac-operon model . In this gene regulatory mechanism , the catabolite activator protein ( CAP ) promotes DNA transcription initiation . The homodimeric protein CAP ( Fig 1A ) binds cyclic adenosine monophosphate ( cAMP ) and DNA . The N-terminal nucleotide binding domain ( NBD; Fig 1B blue ) acts as a dimerization domain and is globular . The C-terminal DNA binding domain ( DBD; Fig 1B green ) is bound via a flexible linker to the cAMP binding domain and consists of a helix-turn-helix motif . CAP activity is regulated mainly through negative cooperativity of the two cAMP binding events: the binding affinity of the second cAMP is reduced by nearly two orders of magnitude [1–3] . Once CAP is activated by cAMP , it binds to a specific DNA region , and thereby enhances downstream transcription [4–6] . While the binding of CAP to DNA is largely understood [7] , the allosteric mechanism of cAMP binding is still elusive . The puzzling fact that the binding of the first cAMP leads to no recognizable structural change at the second binding pocket [8] but still reduces its binding affinity by two orders of magnitude [1–3] has led to numerous investigations . The crystal structure of singly cAMP bound CAP shows a distance of more than 20 Å between the two binding pockets [9] , and therefore direct Coulombic interaction between the two cAMP cannot explain the negative cooperativity . Also , CAP structures , resolved by X-ray or Nuclear Magnetic Resonance ( NMR ) , with no or two cAMP molecules bound , do not explain the observed allostery . Instead , NMR measurements and isothermal calorimetry ( ITC ) on a truncated construct ( no DBD ) [8] revealed that the binding of cAMP gives rise to a primarily dynamics-driven allosteric mechanism . More specifically , a change in dynamic fluctuations in the unliganded protomer upon cAMP binding to the other protomer render the binding of a second cAMP molecule entropically unfavorable . Similarly , changes in the conformational entropy of CAP due to a global redistribution of internal dynamics was shown using NMR and ITC to also decisively impact DNA binding . Molecular Dynamics simulations and free energy calculations have supported this view on CAP dynamic allostery [10] . These and similar results of other allosteric proteins have led to an ensemble-based view of protein allostery [11 , 12] , according to which local or global conformational motions on various time scales are affected by ligand binding [8 , 13] , thereby triggering a change in protein affinity or activity . Nevertheless , even in the absence of conformational changes , allostery requires the distant effector and functional sites of a protein such as CAP to be coupled . Thus , we here propose a well-defined communication pathway , i . e . a subset of residues primarily involved in correlating effector binding with protein activity , as the basis of the dynamic allostery of FAK [14] . We here aim at determining the distinct allosteric pathways that could explain the negative cooperativity of cAMP binding to CAP and its impact on CAP-DNA association . To this end , we used Force Distribution Analysis ( FDA ) [15 , 16] , a Molecular Dynamics ( MD ) based analysis of inter-atomic forces propagating through the structure upon an external perturbation , which here is cAMP binding . This method is similar to calculations of molecular stresses [17 , 18] . FDA has previously allowed to track pathways underlying structural allostery [19 , 20] as well as dynamic allostery as observed in the methionine repressor MetJ , another gene regulatory protein [21] . We compared inter-atomic forces within three different forms of CAP: the apo , the single and double cAMP bound forms , which we are going to refer to in the following as apo , cap1 and cap2 , respectively . FDA allows to highlight atomic interactions involved in allosteric signal transmission even for very small atomic displacements , in contrast to other computational methods which typically rely on large-amplitude motions . In particular , even if atomic displacements are largely absent , and allostery instead is largely based on fluctuations redistributing within the protein upon effector binding , changes in mean inter-atomic forces capture these dynamic effects due to the anharmonic nature of the underlying potential [21] . We thoroughly validate the MD simulations of the three states of CAP by comparison to experimental X-ray and NMR data . We detect a primary signalling pathway between the two cAMP binding site of the CAP homo-dimer similarly involved in both cAMP binding events , which can explain the anti-cooperativity of cAMP binding . Also , we put forward a symmetric pathway between the NBD and DBD of CAP critically involved in signalling towards the DNA binding site . Our results highlight hot spots of CAP’s dynamic allostery , which are testable by experiments , and suggest that allosteric signalling pathways and entropy driven allostery do not exclude each other but instead can represent different perspectives of the same mechanism .
In the following , we will refer to the apo state , the single and double cAMP bound states of CAP as apo , cap1 and cap2 . Starting from the high-resolution crystal structure of cap2 ( PDB id: 1G6N [9] ) , we performed 9 independent Molecular Dynamics ( MD ) simulations of each of the three differently liganded states ( see Material and Methods section ) . The production time of the 27 simulations was 100ns , resulting in 2 . 7μs of CAP trajectories . We validated the accuracy of the simulations by comparing atomic fluctuations with X-ray ( B-factor ) and NMR ( Squared Generalized Order Parameters for the Methyl Group Symmetry Axis , S2axis ) data as well as biologically relevant motions with available experimental data . The calculated residual root mean square fluctuation ( RMSF ) of cap2 correlates reasonably ( R = 0 . 84 ) with the experimental B-factors of the X-ray structure ( PDB id: 1G6N , S1 Fig ) . Both the crystal structure and the cap2 simulations show ( S1 Fig ) high fluctuations only in the outer loops of the DNA-binding domain ( DBD ) . Interestingly , the B-factors and the RMSF indicate higher fluctuations for one protomer than the other , an asymmetry that is not reflected in the NMR measurements , for which signals inherently represent averages over the two protomers . To compare our simulations even more directly with available NMR data , we additionally calculated CH3 order parameters ( S2axis ) for cap2 as described by Hu et al . [22] ( see Material and Methods section ) . We obtained an average order parameter of 0 . 511 , which is in satisfying agreement with the average value of 0 . 529 from NMR experiments [23] , and follows nicely the trend between MD and NMR based average order parameters observed previously for other proteins [24] . Interestingly , the 2-cAMP bound CAP falls into the more flexible regime of the seven other proteins compared therein . A residue-to-residue comparison ( S2 Fig ) yielded a correlation of 0 . 61 between MD and NMR data for cap2 ( whole protein ) , and of 0 . 71 considering only the NBD . Correlation between S2axis derived from MD and NMR has been previously found to be similarly low for other proteins [24] . In our case , it might be particularly challenged by starting the MD simulations from an X-ray structure ( pdb id: 1G6N ) instead of a cAMP-bound solvent NMR structure , for which no wild-type homo-dimer structure with 2 cAMP has yet been solved . Overall , with regard to entropies and order parameters , we find our MD simulations to largely reproduce the cAMP-dependent flexibility of CAP . We next analysed the similarity of the collective motions observed in our MD simulations with those described by the corresponding experimental structures . We computed collective modes of motion for the three different states from each set of 9 simulations , and compared them to the ‘inactivation motion’ as described by the apo and cap2 NMR structures ( 2WC2 and 1G6N , respectively [9 , 25] ) . Interestingly , the 4th eigenvector , obtained from the apo simulations which is expected to relax towards the apo state , indeed correlates with the experimentally suggested inactivation motion ( S1 Movie ) with a correlation coefficient of up to 0 . 54 ( or 0 . 61 for 4th eigenvector of cap1 ) . By contrast , a lower correlation coefficient , of maximum 0 . 42 , was found between the 20 largest amplitude eigenvectors of the 2cAMP bound state and the experimental inactivation motion . This indicates that the computed dynamics partially and yet specifically capture the functional motions described by experimental structures . As a final sanity check of our models , we also compared the experimentally and computationally observed motions of the DBD involved in DNA binding . To this end , we projected our simulations on the first eigenvector derived from 11 X-ray structures , all of which possessed two cAMP , but for which the DNA could be absent or present . This eigenvector corresponds to the previously described large-scale DNA binding motion [9 , 26] , and involves an almost rigid rotation of the DBD by ~25 degrees . X-ray structures with bound DNA clearly separated from structures not coupled to DNA along this eigenvector , with only two intermediate conformations ( which show non-canonical cAMP binding ) . Remarkably , in spite of the limited time scale of our MD simulations , projections of conformations explored during the MD simulations and experimentally resolved structures [9 , 26–29] , on this particular eigenvector showed that the cap2 state is able to follow this motion further than the apo and cap1 states and even overlaps with DNA-bound X-ray structures ( Fig 2B ) . This suggests that even though we started from the same active cap2 structure , the sampled conformational space partially diverged during the unbiased MD simulations along directions in agreement with experiments . We note that even , the cap2 ensemble preferentially populates a region to the left of the experimental structures , a diversion which however , is minor , and could be due to crystal packing effects . Because it has been shown experimentally that the binding anticooperativity in CAP is entropy driven [8 , 23] , we compared experimental and simulated entropy changes upon cAMP binding ( Fig 2A ) . Configurational entropies were obtained from both our recently developed force covariance ( FC ) estimator [30] and the more established quasi-harmonic ( QH ) approximation [31] for the full protein . Briefly , QH and FC entropy estimators both assume the system to be harmonic and estimate the vibration frequencies from correlated atomic coordinates ( QH ) or from correlated atomic forces ( FC ) . Atomic forces have been shown to deviate less from harmonics than coordinates , such that the harmonic approximation becomes more accurate if based on force covariance [30] . An entropic penalty for the overall process of −TΔScap2-apo = 20 kcal . mol-1 due to the binding of two cAMP ligands has been measured for the full protein construct with NMR , through S2axis order parameters [23] . This result is in qualitative agreement with our simulations , which corroborate an estimated overall entropic penalty ranging from 6 to 54 kcal mol-1 , depending on the selection of atoms for the analysis as well as the estimation method used ( Fig 2A , magenta ) . Available experimental NMR NH-bond order parameters of a truncated construct ( without DBD ) suggested a small entropic penalty for the first cAMP binding of −TΔScap1-apo = 3 . 2 kcal . mol-1 , followed by a much larger penalty of −TΔScap2-cap1 = 18 . 1 kcal . mol-1 for the second cAMP binding event [8] . From our simulations , both methods ( QH and FC ) instead consistently estimate the entropy changes of the first binding event −TΔScap1-apo to be small but favourable ( −10 vs −4 kcal mol-1 for FC and QH , respectively ) . QH , however , is very sensitive to the selection of atoms included in the analysis , and implausibly estimates an entropy change of 25 kcal . mol-1 for the full protein . For the second binding event , QH and FC both corroborate the experimentally measured entropic penalty , ranging from 10 to 26 kcal mol-1 depending on the selection of atoms for the analysis . The partial discrepancy for the first binding event may partly be attributed to the fact that the NMR experiments used a truncated construct without DBD . We hypothesize that full length CAP in contrast to the truncated protein shows a favourable entropy change upon binding the first cAMP , which is yet to be tested experimentally . Having validated the set of MD simulations comprising the apo , cap1 , and cap2 states , we next asked how cAMP binding is allosterically regulated such that it occurs anticooperatively . To reveal the allosteric network of cAMP binding in CAP , we calculated the change in pairwise residue forces upon each nucleotide binding event , averaged over the nine independent 100 ns simulations . We observed convergence of the force differences after approximately six of the nine MD simulations ( S3 Fig ) . By convention , the first protomer will refer to the one where a cAMP molecule is present in the cap1 state . In order to distinguish amino acids from both protomers , we added a prime to residue numbers of the second protomer , such as Arg123’ . To explore the perturbation and potential allosteric communication caused by the first binding event , we first analysed the network of force difference between the apo and the 1cAMP bound ( cap1 ) states . Fig 3 shows a graphical representation of the changes of residue-residue forces upon binding the first cAMP , with edges drawn between residues that exhibit force differences beyond a given cut-off . Only the largest connected network ( in terms of number of amino-acids involved ) is shown to further reduce the noise due to the undersampling in the MD simulations . At a force difference cut-off of 50 pN , the largest network is located around the site of perturbation , i . e . the binding site of the first cAMP ( Fig 3A ) . This network involves mainly the first protomer , especially residues in direct contact with the nucleotide , but also residues His31 to Ala36 in contact with the P-loop ( see Fig 1B ) and residues from the loop Leu73-Gln80 . Remarkably , the force network spans residues all the way down to the N-terminal half of the H3-helix ( see Fig 1B ) of the first protomer and five residues of the H3-helix of the second protomer ( Ala122’ , Arg123’ , Leu124’ , Gln125’ and Thr127’ ) . We also observed signal propagation from Asp68 to the anti-parallel β4/β5-sheet ( Ser46 , Val49 , Ser62 , Tyr63 , Leu64 and Asn65 , Fig 1B ) . A larger yet weaker force network becomes visible at a decreased cut-off of 40 pN ( Fig 3B ) , where we observed signal propagation from the first to the second cAMP binding site . This network shows two distinct connection pathways , referred to in the following as pathways A and B , with a distinct set of molecular interactions at the protomer interface ( Fig 3C and 3D ) . Pathway A is composed of only three specific residue pairs: Leu73-Leu124’ , Leu124’-Arg123’ and Arg123’-Glu72’ . The presence of the ligand in the binding pocket results in a stronger hydrophobic packing interaction between Leu73 and Leu124’ ( Fig 4A ) . The signal is then propagating through backbone interactions from Leu124’ to Arg123’ . Finally , the Arg123’ side-chain gains flexibility upon binding of the first cAMP ( Fig 4B ) , leading to altered pairwise interactions between Arg123’ and Glu72’ . Arg123’ is in close contact with the cAMP molecule in the second protomer , and mutation of this residue drastically decreases CAP activity [32] . Likewise , Glu72’ interacts directly with the second cAMP in the 2 cAMP-bound X-ray structure ( the starting structure of our MD simulations ) through a hydrogen bond with the sugar moiety . Also this residue has been shown to be crucial for nucleotide binding and CAP activity , as shown in site-directed mutagenesis studies [33] . Pathway A as revealed by FDA now suggests these two residues to be directly involved in the allosteric communication for anticooperative binding of cAMP . Pathway B mainly involves Arg123 and the residue pair Arg122-Glu77’ , both bridged by Val126 ( Figs 3D and 4B ) . Nucleotide binding strongly modifies the Arg123 conformation . Indeed , the force difference network identified a number of residues with different interaction patterns with this arginine ( including Asp68 , Phe69 , Ile70 and Glu72 ) . This shift in Arg123 conformations triggers slight modifications in the arginine backbone that are transmitted to the Arg122 through the H3-helix . As a result , the Arg122 side-chain is more mobile and has weaker interactions with Glu77’ . We observed even stronger repulsion in the cap1 state , as the non-bonded energy term is more positive ( S4 Fig ) . Glu77’ shows stronger interactions with its surrounding residues , including Gln80’ through side-chain interactions ( a hydrogen bond was observed over 13% of the apo trajectory against 34% in the cap1 state , S4 Fig ) . The modified polar interaction network of pathway B bridges the two promoters and thereby complements the first signal transduction ( pathway A ) between the nucleotide binding pockets . Interestingly , the observed structural adaptations upon the first cAMP binding event partially also involve dynamical changes of the same residues ( S5 Fig ) . Most importantly , we observed stiffening , measured by residual dihedral order parameter differences , in the loop comprising residues 68–72 and the P-loop region of the first protomer as well as residues Glu72’/Glu77’ of the second protomer , all of which highlighted by FDA . Binding of the second cAMP entails local force changes within host protomer resembling those already seen for the first binding event ( Fig 5A , also compare Fig 3A ) . Again , large residue-residue force differences were observed in the protomer hosting the additionally bound nucleotide , including the P-loop region and residues nearby ( His31’ to Ala36’ ) , the loop from Leu73’ to Gln80’ and also again the upper β4/β5-sheet ( see Fig 1B ) . Similar interaction changes , upon the binding of the first and second cAMP , at least in the proximity of the binding pockets , are expected , given the high similarity of ligand-protein interactions . FDA encouragingly recovered this expected behaviour . However , at the same cut-off of 50 pN , the network after the second binding event now extends over ~15 Å from the rather local network around the cAMP binding cleft detected upon the first binding event . It now reaches the β-strands 2 and 7 to finally join the P-loop of both protomers , and even residues in close contact with and within the DBDs ( Fig 5A and 5B ) . This new distant signal propagation towards the DBD is likely connected to the DBD activation motion observed in our MD simulations ( Fig 2 ) and in available experimental structures [9 , 25] . The long-range nature of the cap1-cap2 force network is also in agreement with the global stiffening of CAP after the binding of the second cAMP observed experimentally [8] . An even larger , but slightly weaker ( 40 pN ) force network now reaches all the way to the C-terminal region of the H3-helix ( see Fig 1B ) . We would like to emphasize that this network was absent before binding of the second cAMP . This region is crucial for regulation and is known to undergo large conformational changes after CAP activation . Experimental structures showed that cAMP binding promotes addition of two helix turns at this C-terminal region [25 , 34] . FDA identifies a single connection pathway between the two protomers for the second nucleotide- binding event , involving almost the same residue pairs we observed for the allosteric propagation of the first binding event . ( Fig 5C ) Indeed , the previously described pathway B is very similar to the one involved in the signal transmission upon the second cAMP binding event , again including Glu77’-Arg122 and Arg123-Asp68 , bridged by modified backbone interaction between Arg122 and Arg123 . Interestingly , Asp68 , which played a key role in the first binding event , is also important after the binding of the second nucleotide . Asp68 propagated the perturbation due to cAMP binding symmetrically to the anti-parallel β-sheet core and then reach the β4/β5-hairpin in close contact with the DBD This symmetric pathway , bridging Asp68 to Glu58 , involves sequentially Leu64 , Val47 , Ser46 , Lys89 , Ala88 and Arg87 . In this network , main chains play an important role . The β-bundle which connect the cAMP pocket with the NBD-DBD interface acts as a good signal propagator through H-bond interactions . We observed in both protomers a significant change in the interaction between Glu58 ( β-hairpin ) and Arg87 ( β-strand 2 , Fig 5D ) side chains , the last residue pair of this network . Structural data has suggested the distance Glu58-Arg87 to critically change as a function of the activation state [9 , 25] , namely to decrease by about 5 Å upon CAP activation . We observed the same trend in our MD simulations: the Glu58-Arg87 minimal distance was larger in the apo and cap1 states in comparison to the cap2 state , for both protomers ( Fig 6 ) . Interestingly , the network further reaches Gln174 located on the DBD in the second protomer . We computed dihedral order parameter difference for each residue from the cap1 and the cap2 simulations ( S6 Fig ) , and obtained significant changes for some of the key residues highlighted by FDA . We observed stiffening of Glu77’ , Glu78’ , Gly79’ and Gln80’ , due to their enhanced interaction with Arg122 .
We here aimed at deciphering the allosteric mechanism of CAP to explain the anticooperativity of cAMP binding and the cAMP-dependent activation of CAP for DNA binding . We performed MD simulations of three CAP states , without cAMP ( apo ) , with a single ( cap1 ) and two bound cAMP molecules ( cap2 ) . Experimental NMR data [8] showed that the anticooperativity in CAP is of mainly entropic nature , with changes in atomic fluctuations upon ligand binding around largely unaffected mean positions of the protein coordinates . We were able to semi-quantitatively reproduce the entropic penalty of anticooperative cAMP binding from atomic force correlations using a recently developed force-covariance ( FC ) entropy estimator [30] . Furthermore , to investigate the mechanism of negative cooperativity and CAP activation , we used FDA to reveal changes in the protein’s internal force network upon cAMP binding . FDA gave insights into the allosteric mechanisms of CAP , which helped to identify minute allosteric rearrangements at the domain interfaces . We obtained allosteric networks for the first and second binding event , which both span the two nucleotide binding pockets , but only for the second cAMP binding also reach into the DBD . Calculated pathways involve the amino acid pairs Glu72’-Arg123’ and Leu73-Leu124’ ( pathway A ) and Arg122 , Glu77’ and Arg123 ( pathway B ) . Using FDA and subsequent structural analysis , we identified critical residues along the signal propagation pathways , the functional role of which are partially supported by mutational studies . For instance , it has been shown that mutating Glu72 , a residue we find within an allosteric link to Arg123 , impacts cAMP binding and allostery in CAP [32] . Likewise , a mutation of Arg123 , which FDA suggests to be implicated in both communication pathways , modifies CAP activity [33] . Our studies additionally revealed Glu58 , located at the β4/β5 hairpin , as being involved in force transmission towards the DBD , as this crucial site symmetrically stands out in the force networks of both protomers ( Figs 5D and 6 ) . This suggests this residue as an interesting candidate for mutations that we predict to result in decoupling of the allosteric regulation of DNA binding from that of cAMP binding . Similarly , residue pairs involved in the signal transmission toward the DBD ( Ser46 , Val47 , Arg87 , Ala88 and Lys89 ) could be good candidates for mutations in order to decouple cAMP binding from CAP activation . In this network , FDA delineates especially Asp68 to be crucial for global signal transmission in CAP , suggesting it as another interesting previously untested mutagenesis candidate . Our data complements and allows the interpretation of the enormous collection of insightful X-ray and NMR data on CAP structure , dynamics and allostery . Previous conclusions on CAP allostery have relied on averaged NMR data ( chemical shifts or order parameters ) from the two CAP protomers justified by assuming symmetry of the CAP dimer . By contrast , we here find this symmetry to hold only partially . In particular , we obtained allosteric pathways between the CAP protomers , crossing the α-helix H3 , which break the symmetry . Our results indicate that it would be desirable to address the challenge to distinguish between the protomers of CAP even in the apo or cap2 states , when collecting experimental data . We expect the CAP force networks to differ from networks of correlated fluctuations , as the latter relies on high amplitude motions and thus occur in more flexible regions such as loops [21] . Systematically analysing the different features revealed by correlated coordinate fluctuations as obtained from PCA or driven MD simulations [35] as opposed to those from our force network for a set of allosteric proteins would in this regard be of interest . While our simulations recover the experimentally observed entropic penalty of the second binding event , we also identified shifts in the mean structure of the protein that can additionally give rise to the anti-cooperativity for cAMP binding . More specifically , significant side chain adjustments right at the cAMP binding pocket and the protomer-protomer interface hamper the binding of the second cAMP . In particular , Arg123’ ( Fig 4A ) , which we proved that it is a key residue for the allosteric signalling in CAP , in the cap1 state populated a region normally occupied by cAMP , thereby occluding the empty binding cleft more than in to apo state . This “enthalpic” component is not in contradiction with NMR experiments , for which minor mean displacements of side-chains , in particular those lacking methyl groups , are challenging to track . Overall , our work extends the entropy-centric view on CAP . It suggests atomic forces and stresses , which intriguingly have been previously shown to be a signature of folded proteins [36] , as a useful measure of protein regulation—with force covariance as an entropy estimator and force distribution as a tool to reveal allosteric communication independently of the nature of the allosteric mechanism , being it structural , dynamic , or both .
The MD simulations were performed using GROMACS 4 . 0 . 5 [37] with the AMBER force field 03 [38] . The parameters for the cAMP molecule were determined with the Generalized Amber Force Field [39] in conjunction with the program Antechamber [40] . The crystal structure of the E . coli catabolite activator protein ( PDB id:1G6N [9] , Uniprot id: P0ACJ8 ) was used for the MD simulations . The protonation states of the amino acids were calculated with the WHAT IF software package [41] . A triclinic simulation box was filled with TIP3P water [42] and sodium/chloride ions at a physiological concentration of 120 mM with a resulting overall system charge of zero . All simulations were run in the NpT ensemble . The temperature was kept constant at 300 K by coupling to the Nose-Hoover thermostat with τt = 0 . 1 ps . The pressure was kept constant at p = 1 bar using isotropic coupling to a Parrinello-Rahman barostat with τp = 1 ps and a compressibility of 4 . 5x10-5 bar−1 . After energy minimization , the LINCS algorithm [43] was used to constrain all bonds . Lennard-Jones interactions were calculated using a cut-off of 1 nm . Long-range electrostatics were calculated by Particle-Mesh Ewald ( PME ) summation [44] . Every state ( apo , one cAMP bound—cap1 , two cAMP bound—cap2 ) of the CAP system was minimized , using the steepest descent algorithm . For each state , nine trajectories with different random starting velocities were calculated , first , in a 200 ps position restraint run ( restraint force constant = 1000 kJ•mol−1 . nm2; time step = 2 fs ) , followed by a 6 ns equilibration run and a 100 ns production run ( time step = 2 fs ) , resulting in 27 trajectories with a total length of 2862 ns . Only the 100 ns production runs were further analysed . System coordinates were saved every 20 ps , resulting in 45 , 000 conformations for each state ( apo , cap1 and cap2 ) . The average size of the triclinic simulation box was 89 x 101 x 97 Å , resulting in a system volume of about 87 , 0000 Å3 with about 87 , 000 atoms . We performed Principal Component Analyses ( PCA ) on concatenated trajectories of either apo , cap1 or cap2 states . A PCA consists in diagonalizing the co-variance matrix of Cartesian coordinates , in order to delineate collective motions sampled during our MD simulations . Eigenvectors describe motions and eigenvalues inform about the amplitude of the corresponding motions . To reduce the number of degrees of freedom , only the main-chain atoms were taken into account to build the co-variance matrices . We further compared only the first five most collective motions to experimentally known ones , such as the activation or the DNA binding motions . The motion were compared in the 3D space using motions matrices which is described in detail elsewhere [45 , 46] . A motion matrix is created using a difference of two Cα-Cα distance matrices . We then computed a correlation coefficient between the two motion matrices . The method does not need any fitting as Cα-Cα distance matrices are signatures in internal coordinates . In order to validate our simulations , we have computed squared generalized order parameters for the Methyl Group Symmetry Axis ( S2axis ) . S2axis were computed for all terminal C-CH3 bond vectors ( or S-CH3 in the case of methionines ) using multiple windows of 3 ns , as described by Hu et al . [22] . A S2axis C-CH3 bond vector in the 3D space x , y and z , is defined as follow: Saxis2=32[〈x2〉2+〈y2〉2+〈z2〉2+2〈xy2〉2+〈xz2〉2+〈yz2〉2]−12 ( 1 ) To have a broad view of the CAP dynamics , and not only dynamical behaviour residues which possess a methyl group , we computed dihedral order parameters for every residues of the protein , as implemented in GROMACS 4 . 5 . 3 [47] and described by Van der Spoel and Berendsen [48] . This analysis was done on concatenated trajectories of the three states ( apo , cap1 and cap2 ) . We then computed order parameters differences residue per residue , to determine dynamical behaviour modification of amino acids after the two cAMP binding events . In the Force Distribution Analyses ( FDA ) [15 , 16] , the forces between each atom pair i and j were analysed at each trajectory step . All terms in the force field were considered , including both non-bonded and bonded terms , except forces including water and ions , as well as PME forces . The more recent FDA version [16] in conjunction with GROMACS 4 . 5 . 3 [47] was used here . For a residue-wise analysis , inter-residue forces Fuv were calculated from the norm ( magnitude ) of the force vector resulting from summing up over all force vectors Fij between atom pairs i and j within the two residues u and v: Fuv=‖∑ijF→ij‖;i∈u , j∈v ( 2 ) We note that time averaged pairwise forces can be different from zero even at equilibrium , in contrast to atomic forces which average to zero over time . To enhance the signal to noise ratio , the pairwise forces Fuv calculated from each frame in the trajectories were averaged over time and over the 9 independent runs of the 3 states , apo , cap1 and cap2 . We then computed the changes in time-averaged residue pairwise forces between cap1 and apo as well as cap2 and cap1 , to track the signal propagation due to the first and the second cAMP-binding event , respectively . The networks shown in Figs 3 and 5 are connected graphs of force differences beyond a given threshold , which further reduced the noise [20] . The stress S , also known as punctual stress , for a residue u was here used to monitor convergence of our simulations ( S3 Fig ) . The stress is defined as the sum of all residue pairs Fuv acting on the residue u: Su=∑v‖F→uv‖ ( 3 )
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The Catabolite Activator Protein ( CAP ) is a well-studied example for how cellular catabolite levels are integrated into the gene regulation . Its affinity for a specific stretch of DNA can be switched on by the binding of two nucleotide molecules termed cAMP to its two protomers . Even though the nucleotides occupy structurally identical binding pockets , the second cAMP binding occurs at an affinity orders of magnitude lower than the first cAMP binding . The question arises how , in the absence of structural changes , the first binding can affect the second . An answer from experiments has been that the communication is largely of entropic nature , i . e . the second cAMP binding would lead to a pronounced reduction in atomic fluctuations of the protein without affecting the atomic mean positions . We here revisited this question by performing Molecular Dynamics simulations . By measuring correlations of forces , a newly derived method outperforming the more common coordinate-based approach , we could recover the previously determined entropic penalty . In addition , however , we observed unobtrusive structural changes of side-chain interactions leading to the occlusion of the second binding pocket that add a critical ‘enthalpic’ component hitherto overlooked . Our study provides a mechanistic view onto the intriguing anti-cooperativity of CAP .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Material",
"and",
"Methods"
] |
[] |
2015
|
Dynamic Allostery of the Catabolite Activator Protein Revealed by Interatomic Forces
|
Recent studies led to the proposal that meiotic gene conversion can result after transient engagement of the donor chromatid and subsequent DNA synthesis-dependent strand annealing ( SDSA ) . Double Holliday junction ( dHJ ) intermediates were previously proposed to form both reciprocal crossover recombinants ( COs ) and noncrossover recombinants ( NCOs ) ; however , dHJs are now thought to give rise mainly to COs , with SDSA forming most or all NCOs . To test this model in Saccharomyces cerevisiae , we constructed a random spore system in which it is possible to identify a subset of NCO recombinants that can readily be accounted for by SDSA , but not by dHJ-mediated recombination . The diagnostic class of recombinants is one in which two markers on opposite sides of a double-strand break site are converted , without conversion of an intervening heterologous insertion located on the donor chromatid . This diagnostic class represents 26% of selected NCO recombinants . Tetrad analysis using the same markers provided additional evidence that SDSA is a major pathway for NCO gene conversion in meiosis .
Homologous recombination is essential for meiosis , the cellular division process specific to gametogenesis . The reduction in ploidy that occurs during meiosis is necessary for sexual reproduction and is achieved by a single round of DNA replication followed by two rounds of chromosome segregation . Reductional segregation is distinguished from mitotic or equational segregation in that sister centromeres remain together during the metaphase-to-anaphase transition of the former , but separate to opposite poles during the latter . For the two pairs to be accurately segregated , they must first become physically connected to one another . Homologous recombination forms physical connections called chiasmata between the replicated pairs of homologs ( reviewed in [1] ) . In addition to being required for reductional chromosome segregation , meiotic recombination makes a major contribution to genetic diversity by generating both new alleles and allele combinations . There are two major classes of meiotic recombination , both of which arise from a common precursor , a DNA double-strand break ( DSB ) . If chromosome arms on opposite sides of the recombination initiation site swap partners , the event is designated a reciprocal crossover ( CO ) . If the original configuration of chromosome arms is retained , the event is designated a noncrossover ( NCO ) . Both CO and NCO events can result in a type of non-Mendelian segregation , called gene conversion , of heterozygous markers near the recombination initiation site . Only COs form the chiasmata needed for chromosome segregation , yet approximately two-thirds of recombination events in budding yeast are NCOs . The proportion of NCOs may be higher in mammals [2] , as the number of total recombination events , estimated from Rad51/Dmc1 foci , dramatically exceeds that of CO-specific events . Understanding the mechanisms that give rise to both CO and NCO recombinants is critical to understanding how the decision is made to convert a meiotic DSB into a CO or an NCO recombinant . Tetrad analysis in fungi showed that gene conversion of a marker is frequently associated with reciprocal exchange of flanking markers [3 , 4] . This association was neatly accounted for by Robin Holliday's proposal that recombination involved an intermediate in which only two of the four single DNA strands were exchanged [5] . Depending on which single strands are nicked , the Holliday junction ( HJ ) intermediate can be resolved to form either a CO or a NCO . The subsequent evolution of models for recombination retained the HJ as a common intermediate explaining the origin of COs and NCOs . This feature was retained even when Szostak et al . proposed the Double-strand Break Repair model [6] ( Figure 1A ) , in which two HJs form and are resolved during each recombination event . Hereafter this model will be referred to as the “dHJ model” ( double Holliday Junction model ) . With respect to the meiotic recombination mechanism in budding yeast , many of the predictions of the dHJ model have been fulfilled ( for reviews see [7–9] ) : Meiotic recombination is initiated by DNA DSBs [10 , 11] . The breaks are processed by resection of the 5′ ends , resulting in a pair of 3′ single-stranded overhanging ends [10 , 12] . Homologous joint molecules are formed when these ends invade an intact donor chromosome , creating hybrid DNA with complementary strands from the donor [13 , 14] . the 3′ invading ends in the joint molecule serve as primers for repair synthesis [10] . The ends of newly synthesized segments are ligated to the resected 5′ ends , forming a specific type of joint molecule , the predicted dHJ [15–19] . According to the dHJ model , the dHJ is converted into recombination products via the action of a nicking endonuclease . The orientation of one HJ resolution event relative to the other determines whether the dHJ intermediate will give rise to a CO or an NCO . Although several of its key predictions were fulfilled , some observations are not compatible with the original dHJ model . Of interest to the current work are studies suggesting that this model does not account for the observed properties of NCO recombinants . The dHJ model predicts a specific configuration of hybrid DNA regions relative to the site of the initiating DSB: one of the two recombining chromatids is predicted to have a heteroduplex patch to the left of the initiating DSB , whereas the other recombining chromatid is predicted to have a heteroduplex patch to the right . Several studies designed to test this prediction found that the expected bi-directional configuration of heteroduplex segments was rare [20–24] . Instead , there was a prevalence of events in which evidence of heteroduplex DNA was found only on one side of the initiating DSB . Other unexpected configurations of heteroduplexes were also seen . Of particular note was a class of recombinants with two tracts of heteroduplex , both on the same chromatid [21 , 24] . The original dHJ model does not account for this type of recombinant . In addition to heteroduplex DNA configurations that did not match predictions of the original dHJ model , studies of mutants displaying partial defects in meiotic recombination challenged the notion that CO and NCO recombinants result from alternative orientations of HJ resolution ( reviewed in [25] ) . Mutations that cause specific defects in CO formation were found to result either in accumulation of dHJs [26] or in preventing the appearance of dHJs [27] . One such mutation is zip1 [27 , 28] . These results suggested that the dHJs seen on 2D gels are predominantly intermediates to the formation of COs , but not NCOs . Differences in the properties and timing of appearance of COs and NCOs led Allers and Lichten [26] to propose that a major fraction of NCOs results not from a mechanism involving a ligated dHJ , but rather from synthesis-dependent strand annealing ( SDSA ) . In this model , which we have referred to as the “early CO decision” model ( Figure 1B ) [25] , a meiotic DSB is designated to become a CO or NCO before the formation of a ligated dHJ . SDSA is a mechanism in which homology-mediated repair of DSBs occurs without formation and resolution of ligated HJs . Resnick proposed the earliest model with the critical features of SDSA [29] , although it did not receive its current name until later [30] . During SDSA , repair of a DSB is achieved by invasion of an overhanging 3′ end into the intact donor chromatid . The joint formed by invasion may be subject to mismatch repair , leading to shortening of the invading end . Following this opportunity for mismatch excision , repair synthesis can extend the invading end past the site of the DSB . Once the end is extended , disruption of the joint occurs . The extended end can then anneal with its partner . The product of annealing is then converted to an intact duplex by repair synthesis and ligation . SDSA differs from models that involve HJ intermediates in that its simplest version accounts only for NCO products , although models for SDSA giving rise to CO products have been suggested [7 , 31–33] . Versions of the SDSA model were proposed to explain properties of budding yeast mating-type conversion that did not fit well with the HJ intermediate model , including the fact that mating-type conversion is not associated with crossing over [34–37] . Critical evidence for SDSA was obtained by induction of DSBs by P-element excision in mitotic cells of the Drosophila germ line [30 , 38 , 39] . A key aspect of these studies was the demonstration that a recipient chromatid could collect sequences from more than one donor locus during a DSB repair event [30] . This finding implied that end extension at one locus can be followed by the disruption of the homologous joint prior to the formation of a second homology-mediated connection between donor and recipient molecules [30] . In addition , the ability of a broken DNA molecule to collect sequences from separated donor loci was shown in mitotic budding yeast using plasmids or endonuclease induction of chromosomal events [33 , 40–42] . Other studies provided additional support for the conclusion that SDSA is a predominant mechanism for mitotic NCO recombination in budding yeast and other organisms ( reviewed in [7] , see also [43–45] ) . Furthermore , SDSA provides a reasonable explanation for the patterns of heteroduplex DNA seen among NCOs in budding yeast meiosis . Although several observations are consistent with the possibility that SDSA contributes to NCO recombination in meiosis , there have been no specific tests of this hypothesis . To address this issue , we created a recombination system that provides evidence for SDSA in a manner analogous to the previously described mitotic systems [30 , 33] , in which recipient ends collect sequences from separated donor loci . Our results provide evidence that SDSA is an important mechanism of NCO recombination in meiosis .
A reporter strain was constructed to test the SDSA model for meiotic NCO recombination . The reporter strain carries a configuration of markers designed to allow the identification of a diagnostic class of NCO recombinants whose origin can be simply explained by SDSA but not by the dHJ model . This diagnostic class is one in which two markers on opposite sides of a DSB are converted , without conversion of an intervening heterologous insertion on the donor chromatid . The system is designed to provide relevant data by analysis of random spores rather than of tetrads . The advantage of random spore analysis is that a much larger number of relevant recombination events can be scored than would be possible by tetrad analysis . Accompanying tetrad data provide evidence that the recombination events selected in the random spore analysis are representative of typical gene conversion events . What follows is a description of the reporter system that we designate the “ends apart” system ( Figure 2 ) . The ends-apart system uses a cassette containing a functional copy of the LEU2 gene inserted downstream of the HIS4 locus ( Figure 2A ) [11 , 46] . The HIS4::LEU2 construct is a well-characterized recombination hotspot ( [27] and references therein ) . Hotspot activity at HIS4::LEU2 results from two strong DSB sites , one downstream of LEU2 ( site 1 ) and , more importantly for this system , a second break site ( site 2 ) in between HIS4 and LEU2 . Two derivatives of the standard HIS4::LEU2 locus exist ( his4B::leu2K and his4X::leu2R ) , each carrying a single mutation in HIS4 and a single mutation in LEU2 . These mutations are 4-bp insertions that cause frame shifts; haploid strains carrying these derivatives are auxotrophic for histidine and leucine . As shown in Figure 2A , the mutations carried by his4B::leu2K are relatively close to site 2 ( 1 . 2 and 0 . 8 kb , respectively ) . This derivative is termed “Recipient” , or “R , ” because its configuration of markers is such that the majority of events yielding an NCO chromatid with the two functional alleles required to satisfy the selection will be those in which the R chromatid is the “recipient” of genetic information . The mutations carried by the second derivative ( his4X::leu2R ) are both farther from site 2 than are those on R ( 2 . 8 and 1 . 2 kb , respectively ) . This derivative is called “Donor” , or “D , ” because the markers it carries make it most likely to be the donor chromatid during the events that give rise to the selected recombinants . The configuration of markers is such that a single DSB at site 2 on the R chromatid in an R/D heterozygote can yield a HIS4+::LEU2+ chromatid via end-directed mismatch repair or by extension of invading ends past the DSB proximal marked site but not the DSB distal site ( reviewed in [7]; Figure 2B ) . Conversion of these break-proximal markers will yield an R chromatid carrying HIS4+::LEU2+ . These His+Leu+ products will be referred to as “double prototrophs . ” The haploid spores that inherit such HIS4+::LEU2+ chromatids can be selected by germination and growth on medium lacking both histidine and leucine . The HIS4::LEU2 region of R/D heterozygotes is flanked by heterozygous markers that allow noncrossover recombinants to be distinguished from crossover recombinants . The D chromatid also carries DSB site 2 , but the strong tendency of DNA ends to impose directionality on mismatch repair events in favor on the unbroken chromatid dictates that single breaks on D will only very rarely lead to the formation of double prototrophs . In the ends-apart system , a single meiotic initiation event could yield a double-prototrophic haploid recombinant from an R/D diploid by more than one mechanism . In one scenario , bi-heteroduplex tracts could form; i . e . , both DNA ends generated by a site 2 DSB on R could invade D to form two hybrid DNA segments ( as in the canonical dHJ model ) . Both such segments may incorporate a break-proximal marker into hybrid DNA . Efficient correction of the mismatched sites would then be expected to occur favoring the sequence on the D strand . The repair synthesis that follows will copy homologous sequences from the donor chromatid and may extend past the site of the break . Following repair synthesis , the homologous joint may then be resolved in one of three ways . First , displacement of both invading strands from the joint may lead to SDSA , creating a chromatid that carries HIS4+::LEU2+ . Secondly , additional repair synthesis may be followed by ligation of ends to form a dHJ . Such a ligated junction could then be resolved by structure-specific endonucleases as in the canonical dHJ model or by topoisomerase activity , ( as proposed by Gilbertson and Stahl in 1996 [24] ) . Only NCOs formed from dHJ intermediates are expected to place both HIS4+ and LEU2+ recombinants on the same chromatid as required to satisfy the selection for double prototrophs among haploid spores; resolution of a dHJ to form a CO is expected to place the two prototrophic alleles on different recombinant strands ( Figure 3 ) . A third scenario that could yield double prototrophs from a single break would involve invasion of only one of the two ends . In this case , invasion and subsequent mismatch repair within the hybrid DNA region could be followed by the formation of a synthesis tract extending not just past site 2 , but also past the opposite break-proximal marked site . Disruption of the joint containing this twice-extended end could lead to annealing and formation of the selected HIS4::LEU2 recombinant via SDSA . The three scenarios described above illustrate that an R/D diploid can form a HIS4+::LEU2+ haploid spore either by transient interaction ( of one or both ends ) or by the stable interaction of both ends , yielding a dHJ intermediate . To distinguish between these mechanisms , we created a class of recombinants that are diagnostic for SDSA . This was accomplished by additional modification of the D chromosome . One such modified construct is called D::KanMX-dup ( Figure 2A ) . The insertion in D::KanMX-dup has two elements . The first element is a directly oriented duplication of a 300-bp fragment from the 5′ end of the leu2 cassette . The duplicated segment ends at a region corresponding to site 2 . The second element , which separates the two copies of the duplicated region of the leu2 cassette , is a 1 . 5-kb KanMX cassette [47] conferring resistance to the fungicide geneticin thereby giving a geneticin-resistant ( GenR ) phenotype . In an R/D::KanMX-dup diploid , the position of the duplicated segment relative to the KanMX cassette makes it possible to form an extended end with sufficient homology to anneal back to the partner end . Formation of such “annealing competent” intermediates could occur in either of two ways ( Figure 3A and 3B ) . First , both ends could invade ( Figure 3A ) and , via mismatch repair and synthesis , collect sequences needed to form HIS4+ and LEU2+ , with one of the ends extending past site 2 before the disruption of both joints . Alternatively , two rounds of invasion by a single end ( Figure 3B ) may occur . The first invasion results in mismatch resection and extension across the break site into the proximal copy of the duplicated sequence . The joint is then disrupted , and the extended end reinvades the distal copy of the duplicated sequence . If additional end extension proceeds past the break-proximal marked site , the strand generated may form HIS4+::LEU2+ recombinants via SDSA . Both the two-end and one-end mechanisms described require dissociation of hybrid DNA as proposed for SDSA . SDSA could also yield GenR products ( Figure 3A ) , but this class can also be accounted for by the mechanism involving a dHJ . Thus , the ascospore phenotype of the class diagnostic for SDSA is His+Leu+GenS . In the ends-apart system , the ability to recover His+Leu+GenS recombinants via SDSA is predicted to depend on the use of both copies of the duplicated 5′-leu2 segment as templates for end-extension during the recombination event . This prediction was tested by construction of a second modified D chromosome lacking the duplicated copy of 5′'-leu2 , called D::KanMX ( Figure 2 ) . Dependence of the yield of the His+Leu+GenS on the presence of the duplicated segment is taken as evidence that SDSA contributes to this class . In Figure 3C , both ends of the R chromosome are shown invading D::KanMX , but the invasion of either one or two ends is predicted to result in the same His+Leu+GenR product . A final feature of the system is a pair of linked heterozygous flanking markers located on either side of the HIS4::LEU2 locus , far enough away from site 2 that they will not influence conversion tracts . These markers make it possible to determine if a particular His+Leu+ recombinant is a CO or an NCO product . The R chromosome carries two marker insertions that the D constructs lack: ( 1 ) an insertion of a cassette carrying the ARG4 gene replaces the entire coding region of the normal LEU2 locus , which is located 23 kb ( 11 cM ) centromere-proximal to HIS4::LEU2; and ( 2 ) an insertion of a cassette carrying the URA3 gene at the CHA1 locus , which is located 40 kb ( 34 cM ) centromere-distal to HIS4::LEU2 . The configuration of markers in the ends-apart system was designed to allow the detection of the signature of SDSA while avoiding interaction of ectopic or heterologous sequences . KanMX insertion was placed at the site of a DSB to allow the selected events to occur by a mechanism very similar to that which would occur in the absence of any heterology . This is important because neighboring heterologies can alter the properties of a homologous recombination event [48 , 49] . Comparison of the two strains used to test for SDSA with a control strain lacking the heterologous insertion showed that all three strains yield single and double prototrophs at equivalent frequencies ( Table 1 ) . This finding provides evidence that the mechanism generating the selected recombinants is not substantially altered by the presence of the heterologous KanMX insertion and is likely to be representative of normal allelic recombination . The ends-apart system is not designed to select CO recombinants that form by the canonical dHJ mechanism . This is because that mechanism yields pairs of conversion tracts in “trans , ” meaning that one tract ends up on each of the two recombinant chromatids , rather than both ending up on the same chromatid . Because pairs of recombinant chromatids segregate to different spores , the selection for His+Leu+ double prototrophy is not expected to reveal the subset of events formed through dHJs . The “early decision model” for NCOs predicts that NCOs are more likely than COs to arise by a non-dHJ mechanism [25 , 26] . Given these considerations , the level of associated COs was expected to be much lower among double prototrophs than among single prototrophs . The observed total frequency of COs was about 60% among single prototrophs ( Figure S1 ) and 24% among double prototrophs ( Figure 4 ) . This difference is statistically significant ( p < 10−40 ) . Given that the canonical dHJ model does not readily account for the association of double-prototroph formation with the formation of a CO ( as illustrated in Figure 3 ) , how can the 24% of double prototrophs with CO configurations of markers be explained ? There are two possible sources of COs; those that form as a result of the same event that forms the double prototroph and those that form in an incidental event . The frequency of incidental COs can be estimated from the KanMX gene conversion data in Table 3 . In a tetrad exhibiting gene conversion , incidental COs are detected when a spore has both the CO configuration of flanking markers and the minority genotype at the converted locus ( e . g . , the GenS spore in a 3GenR:1GenS tetrad ) . This diagnostic class for incidental COs represents one-half the total number of incidental exchanges . Pooling the data from two tetrad experiments , we found 17% ( 12 of 71 ) of conversions were diagnostic for incidental exchange and thus , approximately 34% of conversion tetrads have an incidental exchange . Incidental exchanges will alter the genotype of the spore containing a converted chromatid in 50% of events . Using the binomial distribution to obtain 95% confidence intervals , we estimate incidental exchanges in this system to alter the genotype of 9% to 25% of random spores . This implies that 43% ± 8% of events forming single prototrophs , but only 7% ± 8% of events forming double prototrophs are associated with a CO . Thus , many , if not all , of the CO events observed among double prototrophs are incidental . This result implies that double-prototroph selection strongly enriches for NCO recombinants as expected if NCOs form via SDSA . Any COs that do form in association with double prototrophs are likely to do so by a noncanonical mechanism such as the “strand-displacement–mediated” crossing over mechanism proposed by Allers and Lichten [31] ( Figure S3 ) . To further characterize COs in this system , we examined the role of ZIP1 on the array of double-prototroph genotypes . As mentioned previously , ZIP1 is one of several genes required for normal levels of CO recombinants . At 30 °C , the temperature at which these experiments were performed , zip1 reduces the frequency of COs from 1 . 4- to 4 . 8-fold in an interval-dependent manner [28 , 55] . A zip1/zip1 mutant derivative of R/D::KanMX-dup was tested and found to show a modest ( 1 . 3-fold ) but significant ( p = 0 . 049 ) reduction in the level of CO recombinants among double prototrophs as compared to the ZIP1+ strain ( Figure 4 ) . Most notably , the diagnostic class for SDSA ( His+Leu+GenS ) among double prototrophs was 1 . 4-fold higher in zip1 than in the wild-type control ( p = 0 . 002 ) . This finding is as expected if the frequency of SDSA events increases when CO formation is blocked . Three alternative explanations for the appearance of the diagnostic His+Leu+GenS recombinants were eliminated by additional experiments . First , because the duplication in D::KanMX-dup is a direct repeat flanking the heterologous KanMX insert , we considered the possibility that the diagnostic class of products could arise by intrachromatid single-strand annealing or “pop-out” type recombination . To address this possibility , we created an R/D::KanMX-dup diploid in which both copies of chromosome III contained the KanMX insertion at the break site between the his4 and leu2 heteroalleles . The diploid was allowed to sporulate , and His+Leu+ double prototrophs were selected . Examination of 500 single spores for the loss of geneticin resistance showed that all 500 spores had retained the KanMX insert , eliminating the possibility of a significant contribution from intrachromatid events . Second , we considered that the diagnostic class of spores could be disomic in chromosome III , with one chromosome carrying HIS4+ and the other LEU2+ . CHEF gel analysis was performed on 39 His+Leu+GenS spores , 15 from R/D::KanMX-dup , and 24 from R/D::KanMX . No evidence of disomy was found in the products from either of the parental diploids ( unpublished data ) . Last , an alternative scenario compatible with the canonical dHJ resolution model would invoke the formation of a large single-stranded loop in the heteroduplex DNA as an intermediate in the formation of the diagnostic His+Leu+GenS recombinants . This could occur if a heteroduplex tract forms with one end between the two markers in his4 and the other end between the two markers in leu2 . In this case , the KanMX region would form a large single-stranded loop . Previous studies have shown that such large loops can form and are repaired during meiosis [56 , 57] . However , two considerations make it highly unlikely that loop repair accounts for His+Leu+GenS recombinants . Because essentially all NCO recombinants recovered are R chromatids , a loop repair scenario would have to involve three correction events using alternating templates: D ( at his4B ) , R at ( at the KanMX insertion site ) , and D ( at leu2K ) . If loop repair were the source of the GenS recombinant class , we would expect the total yield of double prototrophs from the parental strain not containing the KanMX insertion ( the R/D control strain ) to be much higher than that from the strains containing it , because a single continuous tract could give rise to that class . A second consideration is that the construct was specifically designed such that the vast majority of recombinogenic DSBs would be directly opposite the KanMX heterology , and not between the two his4 heteroallelic loci or the two leu2 heteroallelic loci . For the ends-apart system to provide evidence for SDSA , the properties of the system must reflect the expectation that the majority of double-prototrophic recombinants result from a single site 2 DSB on the R chromatid . The following considerations show that the system meets this requirement . The first line of evidence indicating that double prototrophs result from a single DSB is provided by analysis of the flanking markers ARG4 and URA3 . The formation of prototrophs from mutant heteroallele pairs occurs mainly via gene conversion of one of the two markers . This expectation has been confirmed for the single prototrophs formed by both the his4X/his4B and the leu2K/leu2R heteroallele pairs used here ( unpublished data ) . The established properties of gene conversion indicate that formation of a functional allele from mutant heteroalleles occurs predominantly via conversion tracts that extend from a DSB site past the break-proximal , but not the break-distal mutation [58 , 59] . Assuming that most conversion tracts yielding prototrophy are of this type , the configuration of mutations in R/D heterozygotes dictates that a single DSB is far more likely to give rise to a double prototroph if it occurs on the R rather than the D chromosome . Flanking markers allow unambiguous identification of the chromatid that initiated the formation of NCO double prototrophs . Importantly , almost all NCO double prototrophs had flanking markers from the R chromosome ( 99% for R/D::KanMX-dup and 98% for R/D::KanMX , Figure 4 ) . This result indicates that the events that form HIS4+ alleles in double prototrophs initiate to the right of his4B on R ( as drawn in Figure 2 ) and those forming LEU2+ alleles initiate to the left of leu2K on R . Therefore , the vast majority of NCO double-prototrophic recombinants are explained by a single initiation event on R , located between the his4B and leu2K markers ( Text S1 ) . A second line of evidence supporting the assumption that the majority of double prototrophs result from a single DSB is provided by estimates of double-prototroph event frequency derived under the converse prediction . The converse prediction is that the double prototrophs result from two independent events , each of which yields a single prototroph . This analysis takes into account the frequency at which single prototrophs form , as well as information provided by the flanking markers . As mentioned above , nearly all NCO recombinants have R-flanking markers . This finding places a constraint on which types of single prototroph–generating events are capable of combining to yield the observed genotypes of NCO double prototrophs . The simplest contribution to the double-prototroph class would be the combination of events that both give the recipient-derived NCO ( R-NCO ) configuration of flanking markers ( i . e . , ARG4+URA3+ ) ( Table S1 , Equation 1 ) . The product of the two relevant single- prototroph frequencies is multiplied by 0 . 5 , because the two events are equally likely to initiate on either of two recipient chromatids , thus are only expected to involve the same recipient chromatid half the time . There are two additional sources of R-NCO recombinants resulting from the combination of two single-prototroph CO events . The first of these ( Table S1 , Equation 2 ) combines ARG4+ HIS4+ ura3 and arg4 LEU2+ URA3+ Cos , while the second ( Table S1 , Equation 3 ) combines ARG4+ LEU2+ ura3 and arg4 HIS4+ URA3+ COs . The sum of Equations 1 , 2 and , 3 gives an estimate of the number of observed NCO double prototrophs that might have resulted from two independent events . This method gives a modest overestimate , because it does not take crossover interference into account . The analysis indicates that double independent events account for less than 10% of observed double-prototrophic NCO recombinants obtained from the relevant parent diploids . Another approach to eliminating the possibility that double prototrophs result from two independent events is to reduce the frequency of recombination initiation and examine the effect on the frequency of double prototrophs . We used spo11-D290A-HA3-HIS6::KanMX4 ( hereafter referred to as spo11D290A ) , a leaky allele of SPO11 [60] . SPO11 encodes the transesterase responsible for forming meiotic DSBs . The leaky allele was shown in previous work to reduce meiotic recombination frequencies about 3-fold [61] . In our system , this allele reduced recombination frequencies to 29% of wild-type levels ( 3 . 4-fold decrease ) for HIS4+ , and 15% of wild-type levels ( 6 . 9-fold decrease ) for LEU2+ . In our ends-apart system , if double prototrophs arise from two independent breaks , then we would expect the double-prototroph frequency to be reduced in spo11D290A by the product of the two single-prototroph reductions ( i . e . , 0 . 29 × 0 . 15 = 0 . 04 ) . On the other hand , if double prototrophs arise only via single breaks , then we would expect the reduction of double-prototroph frequency to be the same as the reduction in single prototrophs ( i . e . , reduced to between 29% and 15% of the level seen in SPO11+ ) . As shown in Table 1 , the spo11D290A mutation reduced double-prototroph formation to 20% of wild-type levels , well within the range of the effect on single prototrophs . In summary , we have shown three lines of evidence collectively showing that the vast majority of the selected double prototrophs arise from a single DSB between his4B and leu2K . Numerous observations point to the fact that COs and NCOs arise via a common intermediate . The hypothesis that dHJ resolution is the molecular process responsible for divergence of CO and NCO pathways in budding yeast had , until recently , been long-standing . However , a growing body of evidence that commitment to the CO pathway occurs before the stage when dHJs form has been mounting [15 , 26 , 27 , 62] . The results presented here provide evidence that NCO recombinants can result from the ejection of extended 3′ ends from joint molecules and subsequent annealing . If most , or all , NCOs do result from SDSA and most COs from HJs , then what is responsible for determining whether a recombination event will become a CO or an NCO ? In addressing this question , it is important to distinguish “commitment” to a particular pathway from execution of the first detectable molecular event at which the two pathways diverge . Commitment may occur at a recombination stage when methods available for assaying intermediates do not distinguish the two pathways . The critical event for executing the CO/NCO decision could be the loading of a helicase at a heteroduplex joint . Previous studies have shown that helicases can act to enhance or reduce the ratio of CO to NCO recombinants . The first intermediate appearing to be CO pathway-specific is one in which only a single end is stably engaged with the donor duplex [15] . This single end invasion ( SEI ) intermediate is converted to a ligated dHJ , which in turn is resolved to a CO [15 , 26 , 27] . Formation of both SEIs and CO recombinants depends on Mer3 . Mer3 is a branch-specific helicase that appears to stabilize single-end intermediates by increasing the hybrid tract lengths [27 , 63 , 64] . A different helicase may be required to disrupt intermediates once 3′ end extension has occurred , allowing progression through the NCO pathway . One candidate for a joint disruption helicase is Sgs1 [65 , 66] . A modest enhancement of CO frequency can been seen in sgs1 mutants [67–69] , consistent with a role in promoting SDSA . However , if Sgs1 does promote NCOs , there must be an efficient alternative pathway to NCOs that operates in its absence , as sgs1 mutants still show high levels of NCOs . It is also worth noting that end-extension during SDSA may involve replication-driven bubble migration [70] . In this case , joints may dissociate without the aid of a helicase . Although joint disruption could be the first detectable step in distinguishing the two pathways , this step must follow the stage at which sites are designated to follow one path or the other; mechanisms that dictate the nonrandom distribution of meiotic COs appear to act prior to the actual disruption or ligation of homologous joints ( reviewed in [25] ) . The data presented here provide evidence that a major fraction of NCO recombinants in budding yeast result from SDSA rather than from dHJ-mediated recombination . Future studies will be required to determine the relative contribution of SDSA and dHJs to NCO recombinants . It will also be of interest to learn if the prevalence of SDSA varies between genetic loci and/or between species .
All strains ( Table S2 ) are isogenic heterothallic derivatives of the S . cerevisiae strain SK-1 [71] . Yeast strains used in all experiments were constructed by standard genetic crosses , or by LiAc transformation [72] . Previously described conditions were used for growing and maintaining strains [12] . All strains contain the synthetic recombination hotspot HIS4::LEU2 [11] . This hotspot construct contains a copy of the LEU2 gene inserted centromere-distal to the HIS4 coding region . The relevant mutant heteroalleles of these two genes were created by restriction digest fill-ins: his4B and his4X heteroalleles were generated from BglII and XhoI sites , respectively [46] , and leu2K and leu2R were generated from KpnI and EcoRI sites , respectively [73] . The experimental strains D::KanMX ( DKB2564 ) and D::KanMX-dup ( DKB2558 ) were created by transformation of the his4X::leu2R-containing control strain ( DKB2562 ) with a PCR-amplified fragment of the G418-resistance KanMX2 cassette [47] . Primers for the amplification ( Text S2 ) contained targeting tails homologous to the regions up- and downstream of DSB site 2 , which is located approximately 500 bases upstream of the HIS4::LEU2 junction [11] . The targeting tails were engineered such that , upon transformation , 300 bases of sequence would be duplicated in D::KanMX-dup ( DKB2558 ) and not in D::KanMX ( DKB2564 ) . This was achieved by taking advantage of the fact that DNA fragments can recombine into the yeast genome in an “ends-in” or “ends-out” configuration [74] . D::KanMX was constructed using the ends-out targeting tails , creating a disruption insertion directly between the two continuous target sequences . D::KanMX-dup was constructed using the ends-in targeting tails , which invade target sequences separated by 300 bp; this targeting reaction will “gap repair” across the 300-bp region and recombine into the genome , thereby duplicating that region . All parental strains contain a complete deletion of all coding sequence from the LEU2 locus , located 23 kb centromere-proximal from the hotspot . The deleted locus is marked either with ARG4 or arg4 . The leu2Δ::ARG4 allele was created by transforming a LEU2 strain with a PCR-amplified copy of the ARG4 gene , using primers with 40-bp tails of terminal homology to regions directly up- and downstream of the LEU2 open reading frame . Chromosomes designated R carry leu2Δ::ARG4 . A derivative of leu2Δ::ARG4 , designated leu2Δ::arg4 , was generated by isolation of ectopic recombinants among random spores derived from a leu2Δ::ARG4/leu2Δ::ARG4 arg4/arg4 diploid strain . Recipient strains are marked by insertion of a 0 . 8-kb fragment containing the URA3 gene located 40 kb centromere-distal from the hotspot . Donor strains lack this insertion . An SK-1 derivative containing the spo11-D290A-HA3-HIS6::KanMX4 allele [61] was generously provided by S . Keeney and introduced into strains carrying D and R chromosomes by conventional genetic crosses . Random spore analysis of recombination in strains containing heterozygous chromosome markers was carried out by a method designed to minimize any contribution by mitotic recombination to the population of selected recombinants . Diploid strains examined were created at the time of each experiment by first isolating single colonies of parental haploid strains on YPDA plates . Assays were performed in triplicate by selecting three single colonies from each strain . These were grown in large patches on YPDA plates for 12 h , or 24 h for spo11-D290A-HA3-HIS6::KanMX4-containing strains . Parental haploids were then mated on fresh YPDA plates for 8 h . After 8 h , the mating patches containing newly-formed diploids were scraped off the plates and suspended in 50 ml liquid SPS at an optical density at 600 nm ( OD600 ) of 0 . 5 . This suspension was incubated at 30 °C , with shaking , until the culture reached an OD600 of 0 . 9 , approximately 5 h later . Meiosis was induced by shaking the cells in 50 ml SPM+1/5 COM liquid sporulation medium at 30 °C for 18–24 h . Ascus walls were digested for 3 h at 37 °C with 20 μg/ml zymolyase 100-T and 0 . 4% β-mercaptoethanol . Three volumes of NP-40 lysis buffer ( 0 . 02% ( v/v ) NP-40 , 50 mM Tris , 150 mM NaCl , 2 mM EDTA ) was added and incubated at room temperature for 30 min . A suspension of single spores was then generated by sonication . Serial dilutions were made to 10−6 , and spores were plated on both complete and selective media ( i . e . , plates lacking histidine , leucine , or both ) . After 3 d growth , recombination frequencies were calculated according to the following formulae: f ( His+ ) = #His+/#Com , f ( Leu+ ) = #Leu+/#Com , and f ( H+L+ ) = #His+Leu+/#Com , where “#Com” indicates the number of spores growing on complete medium , representative of the entire population of viable spores . For tetrad analysis , recipient parental strains were modified to include additional heterozygous markers , ade2 and lys2 , to avoid the chance that a 3:1 segregation of KanMX resulted from a false tetrad . All 3:1 segregations of KanMX showed 2:2 segregation for both ade2 and lys2 . Parent haploids were mated and allowed to sporulate immediately , as above . Tetrads were treated with 500 μg/ml zymolyase 100-T for 5 min at 37 °C before dissection using a conventional micromanipulator ( Zeiss ) .
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In organisms that reproduce sexually , sex cells ( gametes ) are produced by the specialized cell division called meiosis , which halves the number of chromosomes from two sets ( diploid ) to one ( haploid ) . During meiosis , homologous DNA molecules exchange genetic material in a process called homologous recombination , thereby contributing to genetic diversity . In addition , a subset of recombinants , called crossovers , creates connections between chromosomes that are required for those chromosomes to be accurately segregated . Accurate segregation ensures that gametes contain one and only one copy of each chromosome . Recombination is initiated by chromosome breakage . A regulatory process then selects a subset of breaks to be healed by a mechanism that forms crossover recombinants . Many of the remaining breaks are healed to form so-called “noncrossover” recombinants ( also referred to as “gene conversions” ) . Until recently , it was thought that crossovers and noncrossovers were formed by nearly identical pathways; which type of recombinant arose was thought to depend on how the last enzyme in the pathway attacked the last DNA intermediate . However , more recent observations suggested that noncrossover recombinants might arise by a mechanism involving less-stable intermediates than those required to make crossovers . In the present work , a yeast strain was constructed that allowed the detection of a genetic signature of such unstable recombination intermediates . This strain provided evidence that meiotic crossovers and noncrossovers do indeed form by quite different mechanisms .
|
[
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"Methods"
] |
[
"genetics",
"and",
"genomics"
] |
2007
|
Synthesis-Dependent Strand Annealing in Meiosis
|
Adaptation is at the heart of sensation and nowhere is it more salient than in early visual processing . Light adaptation in photoreceptors is doubly dynamical: it depends upon the temporal structure of the input and it affects the temporal structure of the response . We introduce a non-linear dynamical adaptation model of photoreceptors . It is simple enough that it can be solved exactly and simulated with ease; analytical and numerical approaches combined provide both intuition on the behavior of dynamical adaptation and quantitative results to be compared with data . Yet the model is rich enough to capture intricate phenomenology . First , we show that it reproduces the known phenomenology of light response and short-term adaptation . Second , we present new recordings and demonstrate that the model reproduces cone response with great precision . Third , we derive a number of predictions on the response of photoreceptors to sophisticated stimuli such as periodic inputs , various forms of flickering inputs , and natural inputs . In particular , we demonstrate that photoreceptors undergo rapid adaptation of response gain and time scale , over ∼ 300 ms—i . e . , over the time scale of the response itself—and we confirm this prediction with data . For natural inputs , this fast adaptation can modulate the response gain more than tenfold and is hence physiologically relevant .
The ability of neurons to modulate their response as a function of the environment or the task is at once a staple of neural information processing and an achievement of neural biophysics . Adaptation is at play throughout sensory systems . In peripheral sensory cells , one expects significant adaptation as these cells convert wide-ranging natural inputs into neural activity confined to a comparatively restricted range . This is true , in particular , of peripheral visual cells [1]–[11] and especially of photoreceptors [11]–[25] . A wealth of experimental data gathered over more than four decades , across species , allows the identification of universal trends in their response and adaptation properties , and renders photoreceptors an ideal testing ground for our quantitative understanding of neural adaptation . In a typical experiment , photoreceptors are probed with flashes or steps of light , presented either in the dark or against a light background [11] , [12] , [14] , [16] , [18]–[20] , [22] . In these simple protocols , ‘adaptation’ refers to the dependence of the flash or step response upon the background intensity . In photoreceptors , the response to a transient input depends strongly upon background light intensity: both the response amplitude and its dynamics are affected . In the dark , a photoreceptor responds to a small flash of light with a relatively large , slow hyperpolarization . Under bright background conditions , the response amplitude to the same flash decreases ( reflecting a smaller gain , or ‘gain suppression’ ) and the dynamics of the response speed up [11] , [12] , [14] , [16] , [19] , [20] , [22] . The situation is further complicated in more elaborate protocols , in which the ‘background’ light intensity—thought of as a ‘conditioning stimulus’—itself varies in time [8] , [19] , [21] . Then the amplitude and dynamics of the response to a light flash or step—the ‘probe stimulus’—depend not only on the intensity but also upon the time course of the conditioning stimulus that precedes the probe stimulus . Similarly , the non-trivial dependence of the neural response on the amplitude and frequency of a periodic stimulus reflects a form of adaptation [3] , [9] , [17] , [26] . Thus , the distinction between conditioning and probe stimuli , though useful within the contexts of some experimental protocols , may be misleading . Any given photoreceptor relies upon a single stream of absorbed photons , based upon which it produces a response at each instant in time . It is artificial—as a number of authors have noted in the past and as some of the above-referenced literature observes—to treat adaptation and response as distinct phenomena , especially if they occur on similar time scales , and a consistent model ought to address both on equal footing . Adaptation is dynamical in two respects . First , photoreceptor adaptation reflects a memory of the time course of the light intensity input—we can call this ‘the dynamics of adaptation’ . Second , adaptation affects the dynamics of the response itself: quite generally , gain suppression is accompanied by a speed-up of the dynamics—we can call this ‘the adaptation of dynamics’ . This phenomenon is often referred to as the ‘gain-bandwidth trade-off’ [27] . Furthermore , roughly speaking , photoreceptors in the dark or in dim backgrounds tend to respond in proportion to the incident light intensity , while in bright backgrounds they respond to the time derivative of light intensity , for instance responding transiently to steps in intensity [18] . This qualitative modulation of the response as a function of background intensity supplements the quantitative effect of response speed-up . ( For references on these phenomena in photoreceptors of different organisms , see Table 1 below . ) Any model of photoreceptor response to light intensity ought to capture this phenomenology of adaptation , namely ( i ) gain suppression , ( ii ) gain-bandwidth trade-off , and ( iii ) the transition from proportional response ( to light levels ) to differentiating response ( to temporal derivatives of light levels ) with increasing background intensity . In order to model such phenomena , it is natural to turn to the biochemical phototransduction cascade , which converts light into neural activity and which has been studied in great detail [22] , [28]–[31] . Yet some of its parameters have not been measured and some of its modules , such as those involved in calcium feedback , are still a matter of investigation and , possibly , controversy ( see , e . g . , Ref . [22] ) . Of greater concern is the difficulty to extract an intuitive understanding or derive qualitative predictions from the large set of coupled biochemical equations that represents phototransduction . Here , we instead introduce a simple , phenomenological model , in the spirit of pioneering models of photoreceptor response [12] , [15] , [32] but differing from these in important ways . Throughout , we refer to it as the dynamical adaptation ( DA ) model . It is characterized by a dynamical non-linearity without feedback , the interplay of two time scales , and no more than a few numerical parameters . The DA model has three merits . First , it is simple enough to be solved exactly , at least formally , for any input . Second , the DA model remains rich enough to capture the phenomenology of short-term adaptation on the scale of milliseconds to seconds . Indeed , we show that it reproduces precisely a wide array of light adaptation phenomena recorded in classic experiments on turtle cone photoreceptors with flash and step inputs . Third , the DA model allows one to make new qualitative predictions on the adaptive behavior of photoreceptors , for example in response to inputs more complicated than mere flashes and steps; this is much more difficult to achieve using complicated biochemical models with many equations and a great number of numerical parameters . As an example of the predictive power of the DA model , we apply it to fluctuating inputs such as periodic or randomly flickering inputs . Such inputs are often employed in modern experiments as a means to explore a greater range of stimulus variability . It is also generally assumed that these prevent photoreceptors from undergoing significant adaptation . Contrary to this assumption , the DA model predicts that fluctuating inputs induce fast adaptation that depends upon both the intensity and the time course of the input . We find that the DA model reproduces the response of a salamander cone exposed to flickering light with great precision , and we indeed uncover fast adaptation in an analysis of the data . Motivated by this result , we use the DA model to make predictions about the adaptive properties of photoreceptors when these are presented with either periodic or randomly fluctuating inputs . In the case of sinusoidal light intensity , the character of the frequency-dependence depends upon the contrast of the input—an essentially non-linear effect . In the case of natural time series of light intensity , the instantaneous gain can vary more than tenfold on a fast time scale of ∼ 300 ms .
We start by presenting the equations of the DA model . Subsequent sections apply these equations to various light inputs and compare the outcome to data . In formulating the DA model , we look for simple equations that capture the dynamics and adaptation of photoreceptor response . That is , we set the parameters in the DA model equations to be fixed once and for all for a given cell , so that they need not be re-fitted for different choices of conditioning and probe stimuli: any adaptive behavior is to follow entirely from the dynamics prescribed by the equations . Furthermore , we limit as much as possible the number of parameters . Saturation and adaptation effects derive from a non-linearity in the equations . Specifically , we construct this non-linearity so that it informs both gain control and temporal modulations in agreement with the ‘gain-bandwidth trade-off’: smaller gains are associated with faster responses . We present the DA model equations , then explain the intuition that lies behind them and their merit in an analytical approach . The DA model describes the photoreceptor membrane potential , , but it is more natural to write down equations in terms of the photoreceptor response , , defined as the difference between the instantaneous membrane potential and the resting membrane potential in the dark: ( 1 ) The main DA model equation reads ( 2 ) where , , and are constants . Vertebrate photoreceptors hyperpolarize in response to light , so that vanishes in the dark and is negative otherwise . By convention , we define to be negative , while all other quantities are positive . The time-dependent quantities and are filtered versions of the incident light intensity , , given by ( 3 ) ( 4 ) The kernels and are products of monomial and exponential functions; each integrates to unity and is fully specified by a few parameters . ( Explicit expressions for these functions are given in the Methods section . ) The essential feature of the kernels is that they extend over comparable time scales , but with broader than and somewhat delayed ( see Fig . 1 ) . We note that all the time scales that enter the DA model are of the same order of magnitude—several tens of milliseconds: we focus on modeling the ‘fast’ adaptation that occurs on time scales comparable to that of the photoreceptor response , and we ignore long-term adaptive phenomena which take place over seconds or even minutes [22] , [25] . Equations ( 1–4 ) define the DA model . The motivation for the form of Eq . ( 2 ) becomes apparent if we consider in turn its linear and non-linear components . If , the equation is linear and the response , , is a low-pass filtered version of the input , ( 5 ) Since the kernel integrates to unity , in this linear version the response gain is entirely represented by the value of the parameter . As for dynamics , the response is smoothed over the time scale of and the ‘relaxation time’ . If , the multiplicative -term modulates both gain and dynamics . One way of seeing this is to divide both sides of the equation by a factor , to obtain the equivalent equation , ( 6 ) The -term yields effective , time-varying gain , , and time scale , . These co-vary in a manner that satisfies the gain-bandwidth trade-off: large yields both small effective gain and small effective time scale , and vice versa . Actually , the -term has a more involved effect on the dynamics than the mere rescaling of the relaxation time , , as will become clear in subsequent sections . In brief , because the effective gain is time-varying the response is in fact governed by an ‘effective kernel’ that results from a combination of the kernels and ; its time scale and dynamics depends upon the input's recent history . We refer to Eq . ( 2 ) as ‘non-linear’ even though the variable enters it only linearly; even though , in other words , the model is purely feedforward . The model is non-linear in that the output is not a linear function of the input . Throughout , when we refer to the ‘non-linearity’ in the DA model , we mean the multiplicative term , , which is the only term responsible for the non-linearity of the input-output relation . A great merit of the DA model is that its feedforward form allows one to write down an exact solution for any input choice . Indeed , it is easy to verify that Eq . ( 2 ) is solved by ( 7 ) One can ‘plug in’ the stimulus into Eqs . ( 3 , 4 , 7 ) , analytically or numerically , and produce the model photoreceptor response . These equations provide an alternative definition of the model , and are illustrated schematically in Fig . 1 . All adaptive phenomena arise because of the stimulus-dependent term that appears in the argument of the exponential and which modulates the gain and dynamics of the response . This non-linearity couples ‘conditioning’ and ‘probe’ stimuli to create a response to the stimulus history as a whole . Our model bears similarities to various mathematical models that have been introduced in the context of molecular signal transduction and which also display interesting adaptive behaviors [33]–[38] . We return to these in the Discussion . Classic experiments on photoreceptors have characterized their response and adaptation properties with the use of light flash and step stimuli . The resulting phenomenology is shared by different species ( see Table 1 ) . In order to assess the ability of the DA model to capture this phenomenology , we compare its output to data on one of the best-characterized photoreceptors , the turtle red-sensitive cone cell . We focus on experiments performed by three sets of researchers ( Baylor , Hodgkin , and Lamb [13]–[15] , Daly and Normann [16] , and Burkhardt [18]—henceforth , we refer to these with the acronyms ‘BHL , ’ ‘DN , ’ and ‘B . ’ ) , which include five stimulus protocols: single and paired light flashes in the dark ( Figs . 2 and 3 ) , light steps in the dark ( Fig . 4 ) , bright and dark flashes against a fixed light background ( Fig . 5 ) , and bright steps against a fixed light background ( Fig . 5 ) . We emphasize that DA model parameters were fixed across all experiments for each of the three data sets ( see Methods , Table 2 ) . We used an optimization routine for the choice of parameters , but even parameters found by a coarse search by hand yield very similar results . In fact , it is possible to derive satisfactory curve-fitting to the three sets of data by varying only a small subset of parameters from one data set to the next . The robustness of results with respect to parameter variations is one of the strengths of the DA model . The simple protocols we considered in the previous section make use of a transient input ( the ‘probe’ stimulus ) superimposed upon a constant light intensity background ( the ‘conditioning’ stimulus ) . They provide a complete characterization of photoreceptor activity if adaptation is governed by time scales much longer than those that control the response to transient inputs . By contrast , the DA model suggests that response and adaptation occur over comparable time scales , because the quantities and follow similar dynamics . When inputs fluctuate in time , and in particular when the fluctuations take place on time scales comparable to the photoreceptor time scales , the distinction between ‘conditioning’ and ‘probe’ stimuli fades . In order to investigate adaptive properties more broadly than with flashes and steps of light , we presented cones of the salamander with a time-varying white noise , whole-field light stimulus , and we measured their responses with sharp intracellular electrodes ( Fig . 6A ) . The DA model output closely follows the experimental membrane potential traces ( Fig . 6B ) . As a benchmark for the DA model's performance , we compare its output to that of a model devoid of dynamical adaptation , namely the linear-non-linear ( LN ) model [43]–[45] . The LN model is made up of a linear filter , derived by reverse correlating the data trace with the filtered input trace , followed by a static non-linearity ( Fig . 6C ) . Operationally , the non-linear function is extracted from a scatter plot of the linearly filtered input against output data ( such as the one in Fig . 6D ) . The DA model trace follows data more faithfully than the LN model trace ( Fig . 6B ) . In particular , the LN model tends to miss the peaks and troughs of the activity . This discrepancy suggests that dynamical adaptation is at play in salamander photoreceptors even under conditions of rapid light flicker: the observed instantaneous gain appears to depend upon the recent input history , whereas in the LN model any gain control is fixed as it results from a static non-linearity . In the DA model , history-dependent adaptation is embodied by the non-linear -term , and its effects indeed are strongest at peaks and troughs of the response , which reflect an unusually high or low light intensity level in the recent input history . One way to examine variable gain is to divide the data set into groups that correspond to different mean light intensities in a sliding time window of fixed duration . Here , we compared the entire data set to the 10% brightest and 10% dimmest preceding 300 ms time windows ( Figs . 6D–F ) . The data corresponding to these two extreme regimes is not captured by a purely linear fit of the entire data set: ‘corrective gains’ have to be applied in each regime and these differ by a factor of 1 . 8 ( Fig . 6D ) . Though it deviates more modestly from the data , the LN model output is not satisfactory either as corrective gains are still required to reproduce the two extreme regimes and differ by a factor of 1 . 4 ( Fig . 6E ) . In contrast to the LN model's static non-linearity , the DA model accounts for moment-to-moment adaptation and captures the data without the need for corrective gains ( Fig . 6F ) . To be precise , if corrective gains are applied in the extreme regimes , they differ by only a factor of 1 . 04 from each other . A statistical analysis reveals that the two corrective gains for high and low of light intensity were significantly different for the linear and LN models ( both with ) , while the discrepancy was not significant in the case of the DA model ( with , see Methods ) . We emphasize that this close agreement is obtained by fitting a few numerical parameters in the DA model ( see Methods ) , while in principle the LN model requires fitting an entire non-linear function . We also mention that the comparison , here , is with the usual formulation of the LN model , which makes use of a single temporal filter . Generalizations of the LN model that make use of more than one temporal filter ( see , e . g . , [46] ) would naturally achieve a higher performance . However , in the absence of a general prescription on how to combine the various filters , an LN model with , e . g . , two temporal filters would require fitting an entire surface ( rather than a line ) to the data . The DA model actually suggests a specific prescription for the case of photoreceptors; namely , that the second , slower temporal filter should act as a divisive modulation of the first , faster temporal filter . Indeed , if is small with respect to intrinsic times scales of the input or in general at high background light levels , the DA model reduces to a two-filter LN model in which the non-linearity is a simple divisive one ( see , e . g . , Eq . ( 15 ) below ) . The temporal filters used in the DA model indicate that dynamical adaptation occurs over a time scale of ∼200 − 300 ms , and indeed we obtain a clear negative correlation between the ‘instantaneous gain’ of the salamander cone response and the mean light intensity over the preceding 300 ms ( Fig . 6G , see the figure caption and Methods for a definition of the instantaneous gain ) . These data also suggest that the response time scale varies in a correlative manner with the instantaneous gain ( Fig . 6H ) : the response of the salamander becomes faster at moments following periods of high light intensity ( Fig . 6I , see also Methods ) . As before , a careful analysis reveals that this trend is statistically significant ( with 0 . 05 , see Methods ) . Periodic stimuli represent a standard choice for probing the temporal aspects of a response function , and we use them here to illustrate the temporal properties of adaptation in the DA model . We present the model photoreceptor with a sinusoidal fluctuation superimposed upon a constant light intensity background . Here , we define the ‘stimulus contrast’ as the fractional maximum deviation from the mean light intensity . The DA model predicts that the frequency-dependence of the response itself depends upon stimulus contrast . Low-contrast inputs induce a linear , phase-shifted response at all frequencies ( Fig . 7A ) . High-contrast inputs generate qualitatively different output traces , with shapes that depend upon frequency . At intermediate frequencies ( 1 . 25 Hz and 2 . 5 Hz in Fig . 7A ) , the skewed output traces predicted by the DA model are reminiscent of measurements in the primate outer retina , for which a model akin to the DA model has been advanced [9] . The data presented by Lee et al . [9] corresponds to horizontal cell recordings , the retinal neurons postsynaptic to photoreceptors; while it reflects adaptive processing in photoreceptors , it also includes further steps of processing in photoreceptor terminals and horizontal cells . For this reason , we have not attempted a direct quantitative comparison with the output of the DA model . At low frequencies ( 0 . 1 Hz in Fig . 7A ) , the response follows the input closely , without appreciable delay , with only near-instantaneous gain modulation at play . The response plots discussed above yield an interesting prediction on gain control . When we consider the frequency-dependent gain — the ratio of the trough-to-peak amplitude of the ( periodic ) response to the through-to-peak amplitude of the periodic input — two qualitatively different behaviors emerge in bright backgrounds ( Fig . 7B ) . As expected from flash responses , for low-contrast input the gain is suppressed at low frequencies and has a maximum at a given frequency that reflects the time scales in the DA model , provided that the background light intensity is appreciable . ( In fact , if and are defined to each integrate to unity , as we have it here , the gain vanishes in the limit of low frequency . ) Such band-passing behavior was observed in turtle cone [17] and salamander cone [26] experiments . For high-contrast inputs , the gain remains appreciable at low frequencies . When the model photoreceptor is exposed to high-contrast , slow , periodic input , its response simply follows the input with saturation; in the limiting case of a very intense light background , the response oscillates between zero and its saturation value , . There remains a small gain suppression at low frequencies due to the non-linear , saturation property of the photoreceptor response . Thus , when exposed to high-contrast inputs the model photoreceptor responds in a ‘low-passing’ manner . While we expect experiments to confirm this behavior , quantitative comparisons remain to be carried out . We note that the frequency dependence of the gain in the high-contrast case obtained from the DA model is very similar to the experimental frequency dependence presented in Fig . 1A of Ref . [3] . As in the case of Ref . [9] , Ref . [3] reports on recordings of horizontal cells submitted to periodic light input . It proposes a model that makes use of a feedback , frequency-dependent , divisive non-linearity . The DA model offers an alternative explanation . Flicker stimuli are used quite commonly to measure receptive fields ( see , e . g . , [45] ) . These can change as a result of adaptation ( to different levels of light intensity , contrast , or other stimulus parameters ) , and studies of adaptation often use flicker stimuli to evaluate the receptive field under different conditions ( see , e . g . , [1] , [6]–[8] , [47] ) . Thus , the flicker stimulus is meant as a ‘probe’ to test performance of the system in different ‘states’ . Here , we show that this stimulus itself induces substantial adaptation in the system , so the system actually experiences a range of states even during the probe stimulus . The issue is not a purely academic one , since in many natural situations individual photoreceptors indeed receive a flickering input . The time scales in natural situations are a function of spatial modulations and motion in the visual scene , as well as observer and eye movements . In humans , saccades occur every ms [48] , thus producing flicker at individual photoreceptors with a time scale of the order of response time scales . Furthermore , in a natural visual scene , light intensity varies in space by about four orders of magnitude [48] , yielding large-amplitude flicker from eye movement . Endeman and Kamermans [49] recorded from a goldfish cone which was presented with a clip of the naturalistic light intensity time series measured by van Hateren [50] ( Fig . 8A , top panel ) . We digitized the goldfish cone voltage trace and fitted the DA model to it ( Fig . 8A , bottom panel , see also Methods ) . For this specific movie clip , the gain in the goldfish cone response varies in time by a factor of three; the quantitative agreement ( with 0 . 934 ) between the experimental and theoretical traces demonstrates that the DA model replicates the modulation in the reponse properties also in the case of natural inputs , in which fluctuations can be more severe than in laboratory conditions . In order to explore the properties of the DA model in response to natural stimuli further , we calculated its response to a different clip of the same naturalistic stimulus ( Fig . 8B , top panel ) , deeper in the non-linear regime ( see Methods for details on model parameters ) . The series varies over close to three orders of magnitude on scales ranging from tens of milliseconds to seconds . The DA model response to this input ( Fig . 8B , bottom panel ) exhibits overall gain compression: the output varies over less than a single order of magnitude . But how can we extract the rapid , moment-to-moment adaptation induced by the fluctuating input ? An intuitive way to uncover rapid adaptation is to superimpose a set of dim flashes upon the natural light intensity series . The ‘impulse response’ ( i . e . , the response to the complete input minus the response to the natural time series alone ) reveals the moment-to-moment adaptation that occurs in the model photoreceptor: its amplitude varies as a function of time . It can be either smaller or larger than the response ( Fig . 8C ) to an identical flash superimposed upon a fixed light background matched to the mean intensity in the natural series . Furthermore , moment-to-moment adaptation is significant: impulse response amplitudes vary by more than twentyfold ( Fig . 8C ) . From the non-linear structure of the DA model it further follows that the mean impulse response has a greater amplitude than the impulse response in the case of a matched constant light background ( Fig . 8C ) . In other words , on average the model photoreceptor is more sensitive in a fluctuating visual environment than in a static one . The variations of impulse response amplitude follow from the fact that the instantaneous gain depends upon light received during the preceding 300 ms . And the enhanced average impulse response follows from the model's property that , in a bright visual environment , moments of brighter light only suppress the gain by a little bit while moments of dimmer light boost the gain appreciably . Yet another manifestation of dynamical adaptation as captured by the DA model lies in the difference between the average response to a flickering input and the response to a constant light input with matched mean . In order to examine this effect , we constructed an input in which 1 s windows of natural intensity time series alternated with 1 s windows of constant light with matched mean ( Fig . 8D , top panel ) . We calculated the model response over instantiations of natural time series , and derived two conclusions from the average response trace ( Fig . 8D , bottom panel ) . First , on average a transient hyperpolarization follows the onset of the constant light input , while a transient depolarization follows the onset of flicker . Second , the ‘steady-state’ average response to flicker is depolarized as compared to the steady-state response to constant light . The transient hyperpolarizing and depolarizing responses arise because a light-adapted photoreceptor is more sensitive to negative ( i . e . , hyperpolarizing ) deflections in the input than to positive ( i . e . , depolarizing ) ones ( Fig . 5D ) . This asymmetry biases the average steady-state response to flickering input toward depolarization , as compared to the response to constant , mean-matched light . The above arguments appear to be quite general , and should apply to cases in which flickering inputs are drawn from distributions other than the van Hateren series used here . In Methods , we discuss the case of Gaussian flickering inputs , often used in experiments . For that choice of inputs , we can work out analytical results , which confirm the above arguments and agree with numerics . Furthermore , the analytical results illustrate the fact that adaptive effects depend not only upon that magnitude of the flicker but also upon its temporal structure ( see Methods ) . The DA model is a phenomenological model that makes no explicit reference to the mechanisms of phototransduction . A number of studies [22]–[24] , [28]–[31] have examined the mechanism by which light is converted into electrical activity in photoreceptors . They have revealed the beautiful intricacies of the biophysics of phototransduction at the molecular level , but the resulting set of equations is too complicated to be used , as a whole , for developing intuition or making qualitative predictions . Phenomenological and mechanistic approaches are complementary in the purpose they serve; nonetheless it is worthwhile to look for possible connections . The hyperpolarizing response of a photoreceptor to light results from the closing of channels , due to the transformation of cyclic GMP ( cGMP ) into GMP through the action of activated phosphodiesterase . The molecular steps of the phototransduction cascade are illustrated schematically in Fig . 9 . In order to explain the properties of cone response at the molecular level , it is necessary to understand the nature and relative relevance of the non-linearities at each stage of the feedforward cascade . Because the reduction of the cGMP concentration , , depends upon the concentration of activated phosphodiesterase , , and upon its own concentration—the concentration of cyclic GMP ( i . e . , the activated form of the substrate ) —the corresponding phototransduction step is non-linear even at relatively low light intensities ( see Eq . ( 9 ) below ) . This reaction is often presented as the dominant source of non-linearity in the cascade ( see , e . g . , Refs . [23] , [25] and references therein ) . At high light levels , other sources of non-linearity may come into play . Pigment bleaching becomes relevant over the three or four upper decades of illumination , up to 1010 photons/µm2/s [18] . That is , in this range of illumination the very first reaction in the cascade , between photons and rhodopsin , becomes non-linear due to the limited pool of rhodopsin molecules . Whether similar substrate-limited non-linearities occur at intermediate light levels in the case of transducin and phosphodiesterase is as yet unclear for cones . ( While there are indications that the transducin and phosphodiesterase steps in the cascade may be substrate-limited in rods [51] , experiments on cones suggest that phosphodiesterase does not become limiting until the photopigment is already completely bleached [52] . These experiments , though , were performed on membrane preparations from cones and hence do not take into account morphological effects of natural phototransduction . Cone morphology may have a significant influence on the activation of phosphodiesterase by diffusing activated transducin molecules . ) It thus seems that the site of adaptation moves from the back end of the feedforward cascade—namely , the GMP step—to the front end at very high light intensities . ( In an interesting analogy , within the retina as a whole , the main site of light adaptation also moves from downstream processing—namely , the transfer from bipolar to ganglion cells—to the front end—namely , cones—at high light intensities [11] . ) Here , we focus upon the GMP step of the phototransduction cascade , as its non-linearity appears to play a dominant role over a major part of the natural range of light intensities . The analysis of earlier non-linearities would be similar , as their mathematical form is similar . The inactivation of GMP by phosphodiesterase can be modeled as ( 9 ) where , , and are constants . The photoreceptor response grows in proportion with the deviation from the resting value of the cGMP concentration , ( 10 ) where and are the resting values of the concentrations of cGMP and phosphodiesterase , respectively . Inserting Eq . ( 10 ) into Eq . ( 9 ) , we obtain an equation for , as ( 11 ) where is the deviation of the phosphodiesterase concentration from its resting value . Equation ( 11 ) is similar to the central equation of the DA model , Eq . ( 2 ) : here plays the role of the response , , and is an intermediate , light-responsive quantity analogous to and in the DA model . The essential difference between Eqs . ( 2 ) and ( 11 ) is that , in the former , the quantities and vary on different time scales , whereas in the latter , the two phosphodiesterase-related quantities vary on the same time scale . In the DA model , the action of is somewhat slower and slightly delayed with respect to that of . Indeed , can be written as resulting from a convolution with the sum of two kernels , one corresponding to the dynamics of and the other to slower dynamics ( see Eq . ( 13 ) in Methods ) . The fast component of , which operates on the time scale of the response , i . e . , the time scale of , can be identified with the non-linearity inherent in the feedforward pathway of the phototransduction cascade , discussed above . The slow component of can be interpreted as mimicking the delayed effects of feedback loops in phototransduction , i . e . , biophysical reactions that occur beyond the main cascade ( discussed above ) . The quality of our fits to data suggests that , at least within the experimantal range we considered , the complicated feedback processes involved in phototransduction may be well approximated by a simple feedforward non-linearity .
We introduced a new phenomenological model that captures the response and adaptation properties of cone photoreceptors . The DA model is expressed as a first-order differential equation in time ( Eq . ( 2 ) ) and relies upon a single non-linearity . Because of the interplay of a few time scales , response properties depend upon recent history . Both response gain and dynamics are influenced by the input history . Thus , the DA model provides an example of truly dynamical adaptation . The simplicity of the model allows for an exact analytical solution for any input time course ( Eq . ( 7 ) ) and for straightforward numerical calculations . We evaluated DA model outputs for inputs that have been used historically to characterize adaptation , namely flashes and steps of light , and we found that the DA model captures the phenomenology of adaptation qualitatively , and in most cases also quantitatively . Specifically , it reproduces gain compression and dynamical modulation of the response to large ‘probe’ stimuli ( flashes and steps ) ( Figs . 2 and 4 ) , as well as gain control and dynamical modulation as a function of ‘conditioning’ stimuli ( Fig . 5 ) . What is more , we found that the transition from a monophasic flash response in dim backgrounds to a biphasic flash response in bright backgrounds emerges naturally from the DA model ( Fig . 5 ) . Interestingly , also , while we fitted the model to data that did not present these , it predicted double-peaked ( ‘camel hump’ ) responses to intense flashes ( Fig . 2A , bottom panel ) ; responses of this characted indeed have been recorded experimentally [39] . When we stimulated the DA model with randomly flickering inputs , we found that it can reproduce salamander cone data with great precision ( Fig . 6 ) . In particular , it corrects systematic errors that appear if the dynamical character of adaptation is ignored ( as in LN models ) . Furthermore , the DA model predicts fast , moment-to-moment adaptation , controlled by a time scale of about 300 ms , even in the presence of rapid flicker . A careful analysis of salamander cone data indeed uncovered this form of fast adaptation ( Fig . 6 ) . The fundamentally dynamical nature of adaptation in the DA model implies other non-trivial response behaviors when the model photoreceptor is exposed to fluctuating inputs , such as periodic inputs or flickering inputs . In the case of periodic inputs , it predicts a qualitative change of the frequency-dependence of the response when contrast in varied: At low contrast slow inputs are suppressed , while at high contrast slow inputs elicit maximum gain ( Fig . 7 ) . In the case of randomly flickering inputs , the gain in response to transient stimulation varies significantly on a fast time scale ( Fig . 8 ) . Furthermore , the mean photoreceptor output itself is modulated by the amplitude of fluctuations ( Fig . 8 ) . Such a coupling between the mean and fluctuations about the mean is a signature of non-linearity . The DA model is a worthwhile compact description of phototransduction , especially as several of the important numerical parameters involved in the molecular cascade have not been measured , the forms of some of the non-linearities have not been determined , and the feedback mechanisms—in particular the multiple calcium feedback mechanisms—are still a matter of investigation ( see , e . g . , Ref . [22] ) . We have given a heuristic interpretation of the DA model in terms of phototransduction biochemistry . In the light of this interpretation , adaptation is seen as the result of a fast process inherent to the feedforward branch of phototransduction , supplemented by a slower , presumably feedback , process still accurately mimicked in the DA model by an additional feedforward term . Here , ‘feedback’ refers to a process in which the output state of the photoreceptor would affect an ‘upstream’ biophysical interaction . But this does not mean that the DA model provides a complete description of ( feedback ) adaptation . In the feedforward DA model , gain and time scales co-vary . Some calcium-related processes in feedback adaptation may work differently . Experiments indicate that calcium concentration can modulate response gain while leaving time scales unchanged [53] . Furthermore , calcium dynamics seem to involve much longer time scales than those of concern here [25] , [54] , [55] . Historically , light adaptation was defined with experiments that used a ‘conditioning’ stimulus and a ‘probe’ stimulus . The neuron under study was exposed to a conditioning stimulus for some time , and then its response to a probe stimulus was measured; adaptation was defined in terms of the difference between the responses to the probe stimulus with and without conditioning stimulus . Typically , conditioning stimuli were chosen to vary slowly in time or not vary at all , as in the case of a constant light intensity background , and probe stimuli were devised as transient variations in light intensity , such as flashes or steps . Quite generally , neural activity saturates in response to large stimuli . One concern , in defining adaptation , was to disambiguate this simple gain compression from a more involved effect of the conditioning stimulus [13] , [14] . Clearly , for this one needed a model of the gain compression . The LN model was often used as such a model: instantaneous gain compression was ascribed to the shape of the non-linear transfer function ( the ‘N’ part of the LN model ) , while ‘true adaptation’ was inferred from conditioning stimulus-dependent changes in the amplitude and shape of the linear filter ( the ‘L’ part in the LN model ) [6] , [8] , [46] . Thus , light adaptation is often defined in a model-dependent manner that may lead to some amount of confusion . For example , if a system is invested with dynamical non-linearities—as is generally the case for neural systems—it is unnatural to disambiguate ‘gain compression’ and ‘true adaptation’ with the use of a static non-linearity . But even if one ignores this caveat , the definition of adaptation in terms of ‘conditioning’ and ‘probe’ stimuli may be problematic . The definition is suitable if the time scales of response and adaptation are very far apart . Then any stimulus can be divided into a slowly varying component , which ‘conditions’ the system , and a rapidly varying component , with which the system is ‘probed’ . But if the time scales of response and adaptation are comparable , as is the case for photoreceptors , then the distinction between ‘conditioning’ and ‘probe’ stimuli becomes artificial . This is especially true when the input itself varies over these time scales . Put differently , photoreceptors adapt and respond concomitantly . In experiments in which the response properties of a cell are modulated by the intensity of the input fluctuations , rather than by changes in its mean , it is customary to invoke ‘contrast adaptation’ . In our case , too , one can say that the photoreceptor undergoes a kind of contrast adaptation , as its sensitivity is modulated by the intensity of input fluctuation ( see Fig . 8 ) . But this terminology may be misleading because , again , all three time scales—that of response , that of adaptation , and that of flicker—are comparable . For this reason , we prefer to talk about dynamical adaptation . In a model such as the DA model , and in reality , adaptation is dynamical in at least two ways . There is ‘the dynamics of adaptation’: the way in which response properties adjust depends upon the structure of the history of the stimulus , not only upon a single number . There is also ‘the adaptation of dynamics’: not only does the gain change as a function of the experimental conditions , but the response kinetics also vary . Regardless of the specific form it takes , adaptation is often viewed as a change of model parameters—gains or time scales , for example . But a complete model should incorporate the apparent change of parameters , on several nested time scales , as a natural result of its ( possibly very complicated ) dynamics . A number of studies have addressed this issue , and in particular have proposed models with temporal properties that vary adaptively [6] , [10] , [46] , [47] , [56]–[59] . Similarly , the simple DA model can account for the phenomenology that can appear as a change of LN model parameters , namely fast adaptation over a few hundred milliseconds . We have argued that , in a case such as this , response and adaptation are inseparably intertwined concepts . In the case of longer-term adaptive phenomena ( for example , those that result from photopigment regeneration ) , one can invoke slow parameter changes in the DA model . Here , ‘adaptation’ can be defined more easily . But , again , ultimately one would like to construct a richer model that incorporates dynamics over the longer time scales of interest . In this upgraded description , there will be no formal distinction between ‘adaptation’ and ‘response dynamics’ . In this sense , ‘adaptation’ is an elusive notion: once understood in terms of a system’s dynamics , it no longer stands as an independent feature [10] , [57] . Instead of speaking of adaptation , it may be more natural to characterize a neural system or a set of response phenomena by the time scales and non-linearities that govern the dynamics . In the case of the DA model , photoreceptors are described by the interplay of three time scales and a single , multiplicative non-linearity according to which gain and dynamics are modulated by a signal , . The DA model is a phenomenological ( or functional ) model . It came about as we were searching for a simple model that could capture data , and in particular the dynamical aspects of adaptation , quantitatvely . Somewhat to our surprise , we found that it reproduced a large quantity of observations collected over the past four decades . It also corrected systematic errors made by LN models—which , incidentally , fail to describe the dynamical aspects of adaptation—when it was checked against photoreceptor reponse traces that we recorded . Now , the photoreceptor is likely the best understood neural cell and the biochemistry of phototransduction is identified in some detail; it can be modeled as a relatively large set of coupled non-linear equations . A question , then , follows: Have simple , phenomenological models , such as the LN model or the DA model , any reason of being ? We believe that the answer is affirmative , and we explain here why . Biochemical models involve not only many coupled non-linear equations , but also a large set of numerical parameters , many of which cannot be measured directly . Thus , it is very difficult to explore the parameter space of these models and to extract from them generic behaviors and testable predictions . By contrast , a phenomenological model of a cell’s response can be tractable enough that generic behaviors and robust predictions be established . Phenomenological models are thus useful to identify ‘computational modules’ , which can be sought after in more complicated mechanistic models . For example , the DA model displays the computational power of the interplay of two time scales through a feedforward non-linearity . Phenomenological ( or functional ) models have proved fruitful in neuroscience . Besides the reasons just mentioned , this is because they embody what a post-synaptic neuron or , more generally , a neural circuit ‘cares about’ . While the study of the intricacies of the phototransduction cascade is eminently interesting , ultimately the input-output relation of photoreceptors is relevant to downstream visual processing , irrespective of biochemical details . Moreover , phenomenological models are useful in establishing connections between systems that share functional commonalities but may differ greatly in their mechanistic aspects . For example , the DA model is akin mathematically to models of signaling in non-neural cells [33]–[38] . Phenomenological models also come with a generalization power: they can be modified to describe other systems . We indeed expect that variants of the DA model will be useful to study the computational properties of visual or sensory cells other than photoreceptors , which do not rely upon any kind of phototransduction but which do display a similar phenomenology in their input-output relations . Finally , phenomenological models can be of use in analyzing mechanistic models . Here , we have used insights gleaned from the DA model—namely , that adaptation may result from feedforward coupling and that a simple non-linearity involving two time scales may be responsible for it—to examine the phototransduction cascade and to suggest a putative key step in the latter . The DA model is similar in spirit to the pioneering phenomenological models of photoreceptors by Fuortes , Hodgkin , Baylor , and Lamb [12] , [15] and by Carpenter and Grossberg [32] . The oldest model , put forth by Fuortes and Hodgkin [12] , is made up of a succession of linear filters followed by a feedback non-linearity . The cascade of filters in their model plays the role of the filter in the DA model , and the non-linearity governs adaptive phenomena as in the DA model . In another class of models , advanced later by Baylor , Hodgkin , and Lamb [15] , again a succession of linear filters controls the variation of an intermediate quantity ( presumed to be the concentration of some chemical ) which then translates into the membrane potential of the cell . But this quantity induces its own removal , through a feedback process on decay rates . These earlier phenomenological models and the DA model are similar in that they rely upon an initial linear filtering of the input and a subsequent non-linear transformation . The major difference between the two , however , is that earlier models come with a feedback non-linearity while the DA model comes with a feedforward non-linearity . The former involves higher powers of the model output , so that a linear analysis ( such as fitting an LN model ) would reveal output-dependent effective parameters . By contrast , the latter remains linear in the output; as a result , in a linear analysis effective parameters are independent of the output state of the system . Roughly speaking , in a feedback system the output can affect the earlier stages , while in a feedforward system it does not—only the input does . In the Fuortes-Hodgkin model [12] , for example , the role of our delayed signal , , is played by the cell output which enters the equation non-linearly; thus , adaptive properties depend upon the value of the output , whereas in the DA model they are affected only by the value of the input . Biochemical feedback loops in phototransduction are well-documented—so is a feedforward model bound to be useless ? There are at least two reasons for which a feedforward model applies well to this system . First , adaptation may be carried out in both the ( complicated ) feedforward part of the phototransduction cascade and in its feedback loops simultaneously ( see section ‘Putative Connection of the DA Model to the Biochemistry of the Phototransduction Cascade’ above ) . Second , even if feedback loops are essential to adaptive phenomena in photoreceptors , it is conceivable that they are well-approximated by a feedforward process for a range of inputs . In this approximation , one trades mechanistic details for computational simplicity . One may wonder why phototransduction requires several feedback loops ( illustrated in Fig . 9 ) and which aspect of visual computation they each relate to . One way to approach the problem is to identify a computation that a feedforward system cannot carry out . Somewhat surprisingly , we found that a feedforward system , such as the DA model , can reproduce sophisticated data with high accuracy . It is thus plausible that a non-linear feedforward system with several time scales mimics feedback quite well . Alternatively , it is possible ( though improbable ) that some of the feedback is necessary , not for functional computation , but for internal molecular bookkeeping or as a safety net when photoreceptors face extreme conditions . Carpenter and Grossberg have proposed several variants of phototransduction models [32] . One among these can be re-written in a way that makes the similarity with the DA model apparent . The important difference between the two , though , is that the Carpenter and Grossberg model is devoid of a delayed process , as opposed to the DA model which captures delayed effects with its term . Away from the specific realm of phototransduction , a number of studies of cellular signal transduction have introduced models that share similarities with the DA model; see Refs . [33]–[38] and references therein . In most cases , however , the decay part of the model equation contains only a non-linear term , while in the DA model it has both linear and non-linear components . One such example is referred to as the ‘perfectly adapting’ model in the cell signaling literature . One model of phosphorylation-dephosphorylation contains both linear and non-linear decay . But it is governed by a single time scale in the signal . By contrast , the interplay of different time scales , which appear through the and terms in the DA model , is central to its behavior . To our knowledge , the present work provides a novel application of dynamical systems ideas popular in signaling studies to transduction cascades in neurons , and offers detailed results on adaptation to stimuli with complicated correlation structures . We applied the DA model to turtle cone and salamander cone data , but we anticipate that it can be used to describe photoreceptors in other taxa since these exhibit a very similar phenomenology ( Table 1 ) . The trends we discussed in the context of experiments on the turtle cone are consistent across species , from invertebrates such as fly and Limulus to vertebrates such as salamander , mouse , and primate . In particular , modulation of both gain and dynamics is observed across taxa . Every studied species exhibits non-linear compression of the flash response in the dark as well as speed-up with flash intensity . Gain compression as a function of background light intensity is also apparent in all species , although in some cases it is best fitted with a modified , non-linear Weber-Fechner rule ( according to which the response is proportional to , the ratio of the flash intensity to the background intensity , raised to some power ) . Biphasic ( ‘differentiating’ ) flash responses in the presence of a bright background are observed widely , and indeed the DA model predicts a transition from monophasic impulse responses to biphasic impulse responses for increasing background intensity . Yet , it appears that the second , overshoot lobe may be less pronounced or even absent in some species , such as primates , as compared to the turtle data examined in detail here . Also , while much of the insect data does not present biphasic flash responses in bright backgrounds , the literature cited in Table 1 notes a small but distinct second lobe . ( Interestingly , however , laminar recordings in insects display perfect biphasic impulse responses that integrate to zero [60] . ) The DA model can account for such variations in the shape of the impulse response . In particular , the parameter sets the background intensity at which an overshoot lobe appears and the parameter sets the shallowness of the overshoot lobe; for large , the overshoot becomes very shallow and can be difficult to detect in the presence of noise . ( See Fig . 10A and B and captions for a more detailed discussion of this point . ) Since invertebrate and vertebrate phototransduction cascades are evolutionarily distinct [61] , one is led to think that the adaptation phenomenology summarized in Table 1 represents an adequate solution to the problem of encoding natural visual inputs [4] . Downstream visual neurons [1] , [2] , [5]–[8] , [10] , [62]–[67] and , indeed , neurons in the other sensory systems [68] display adaptive properties similar to those recorded in photoreceptors . A model in the spirit of the DA model may be suitable for these . What refinements or elaborations of the DA model would then be required—more complicated temporal filters ? a broader range of time scales ? a more involved form of the non-linearity ? several non-linear stages ? —is itself an interesting question .
Since the pioneering work of Hodgkin and Baylor [13] , [69] , standard functional forms have been used to fit the impulse response of visual neurons , and we found that these forms indeed appropriately fit all the data we examined . By convention , we require that the filters and each integrate to unity . For , we adopted the form ( 12 ) where specifies the time scale of the linear response , specifies its ‘rise’ behavior , and is the Heavyside function with if and if . This filter corresponds to a sequence of simple relaxation equations in time , as may occur in the phototransduction cascade . While other , more involved choices may yield a closer quantitative agreement with data , we found that a similar form for , with the added twist that it combines two time scales , is satisfactory . Specifically , throughout we used the form ( 13 ) according to which involves a fast component that responds on the time scales of the linear response , , and a slow component that responds on a somewhat longer time scale , . The prefactors , and , weigh the relative importance of the two components and ensure normalization to unity . Throughout , we integrated the DA model with standard techniques in Matlab ( Mathworks , Natick , MA ) . We fit our model parameters separately to each of the three data sets we used , from the experiments of Baylor , Hodgkin , and Lamb [13]–[15] , the experiments of Burkhardt [18] , and the experiments of Daly and Normann [16] . We used a gradient descent method in Matlab to find the parameter sets that yielded the least squared error from the experimental results . Fits performed with different initial conditions yielded similar parameter minima . In the case of the Baylor , Hodgkin , and Lamb data , the fit was performed to the voltage traces in Figure 2A ( extracted from Ref . [14] ) . All eight parameters of the model , , , , , , , , and , were varied in the minimization procedure . For the Burkhardt data , the optimized parameters were determined from the family of curves in Fig . 5D ( extracted from Ref . [18] ) . The parameters , , were varied; because these data represented not traces but amplitudes , the remaining parameters had little effect on the fit and were set to typical values before the fitting routine was applied . For the Daly and Normann data , the fit was performed on the flash response traces in Fig . 5A ( extracted from Ref . [16] ) . All eight parameters of the model , , , , , , , , and , were again varied to find the least squared error between the DA model responses and experimental flash responses . The value of was adjusted from experiment to experiment within the same data source , to match the scale . Nonetheless , remained in the vicinity of 2 mV⋅µm2⋅ms/photon ( see Table 2 ) , where we have assumed a cone cross-section of 1 µm2 . This value of yields a peak dark hyperpolarization of ∼15 µV/photon in agreement with experimental observations . The optimized parameter sets are recorded in Table 2 . For Fig . 2F , where the experimental flash intensity was unspecified , the flash intensity , rather than , was adjusted so as to obtain response strengths comparable to the data . Figure 10C displays the flash response for different values of the background light intensity , for each of the three parameter sets used in fitting the data . Again , we emphasize that the results are robust with respect to parameter changes and fits by eye resulted in similar parameters and goodness of fit . The values of and determine the location of the crossover to a non-linear behavior , as well as the relative strength of the effect , but have relatively little effect upon the shape of saturated flash responses ( see Fig . 10A and B ) . For Fig . 8 , the light intensity time series was extracted from van Hateren's recordings of naturalistic stimuli [50] and the goldfish cone response was digitized from Ref . [49] . In calculating the DA model output , the parameters , , were fit to the goldfish cone traces for Fig . 8A , where we used a mean light intensity of 1 . 5·105 photons/ ( ) [49]; the BHL time scales were used in the model as the low temporal resolution of the digitized trace did not allow for a more precise temporal fit . For Figs . 8B , C , and D , parameter set B was used together with a mean light intensity of photons/ ( ) . The gain was probed by superimposing ms flashes containing 100 photons on top of this fluctuating light background . Flash responses were found by subtracting the response to the naturalistic time series from the response to the same time series with superimposed light flashes . Salamander retinæ were exposed to whole-field flicker of time-varying intensity while a sharp electrode voltage recording was made of cone cells , following the protocol of Ref . [8] . Flicker was presented with a CRT at 67 Hz , with a mean light intensity of ∼ 10mW/m2 . Intensities were updated every 2 frames and chosen from a Gaussian distribution with standard deviation equal to 35% of the mean . All data integration and analysis were performed with custom-written routines in Matlab . ( We provide some of these codes as online supplementary material . ) LN model analysis . Best-fit linear filters were found using cross-correlation methods , as described in Ref . [8] . A 500 ms filter was used to produce the linear portion of the LN model output , followed by a third degree polynomial fit of that output to the experimental response . In order to compute the quantities in Figs . 6G–I , windows of 300 ms were selected every 100 ms during stimulus presentation . We computed the mean light intensity over each window as well as the ‘instantaneous gain’ . The instantaneous gain at a given time was defined as the slope of the scatter of the experimental response when it was plotted against the linearly filtered signal , with scatter points extracted from the trace over the 150 ms windows flanking the time in question ( Fig . 6G ) . We averaged the instantaneous gain over each of the 300 ms time windows to obtain an effective gain associated with a time window as a whole ( Fig . 6H ) . These 300 ms ( zero-padded ) windows were used to find the peak cross-correlation times of stimulus with response ( Fig . 6H ) and the linear filters displayed in Fig . 6I . Peak correlation time , for each average of cross-correlations , was defined as the average of the 10 times of maximum correlation . In Fig . 6I , we selected the time points with lowest , middle , and highest instantaneous gains , and plotted the filters corresponding to each of the three subsets of data . DA model analysis . Model parameters were fit to the salamander data ( Fig . 6B ) with a least squares minimization routine in Matlab and Eq . ( 2 ) was integrated with the use of standard methods in Matlab , with the functional forms described above . All parameters ( , , , , , , , and ) were varied , but and were imposed to take integer values . The parameters used to fit salamander data are recorded in Table 2 . Analysis of statistical significance . To assess the significance of the difference in slopes , we applied a Monte Carlo shuffle analysis , in which we ran the slope fitting routine 3000 times , each time offsetting the stimulus and response by a random temporal delay , using circular boundary conditions . The -value was calculated as the frequency with which a difference in slopes occurred with absolute value greater than the one measured in the absence of temporal shift . This same random shuffle method was used to assess the timing differences measured in Fig . 6I , using a random shift in the instantaneous gain value , so as to randomly select stimulus-response snippets . These snippets were used to estimate the peak cross-correlation ( as in Fig . 6H ) , and establish an estimated p-value for the difference . The DA model ( defined by Eqs . ( 2 , 3 , 4 ) above ) is solved exactly for any input , by ( 14 ) ( ( Eq . ( 7 ) above ) , where and are defined in Eqs . ( 3 , 4 ) . In the case of deterministic inputs such as flashes or steps , this expression can be evaluated readily analytically or numerically . In the case of random inputs , we evaluate this expression for a given instantiation of the noise and then take an average over instantiations . ( It is possible to calculate higher moment , such as the variance of _ , or the distribution of the response as a whole , in a similar manner , but we do not present the corresponding calculations here . In some instances below , we compute model photoreceptor responses with the simplifying assumption of small . In this limit , the DA model becomes an algebraic equation which can be solved immediately: ( 15 ) Results derived in this limit hold also in the case of bright backgrounds . In order to establish the notation , we now write down the most general input we shall consider here . It is made up of a constant light background , , a fluctuating ( random ) background , , and a flash of intensity presented at : ( 16 ) We assume that ( 17 ) where is a Gaussian random variable with temporal correlation ( 18 ) normalized such that ( 19 ) and is a deterministic envelope . We shall consider three different cases for the deterministic envelope: Note that , with the stimulus in Eq . ( 16 ) , the filtered quantities read ( 20 ) ( 21 ) where the kernels and are defined in Eqs . ( 12 , 13 ) above . The general solution of the DA model , Eq . ( 7 ) , can be rewriten as ( 31 ) or as ( 32 ) with ( 33 ) The average response , over instantiations of the flicker , is then given by ( 34 ) ( 35 ) Since all random variables in the problem are linear sums of Gaussian variables , we have ( 36 ) After replacing the variables and by their expressions in terms of inputs and filters , Eqs . ( 20 , 21 ) , and performing the Gaussian averages , we obtain the average response ( 37 ) where ( 38 ) ( 39 ) ( 40 ) ( 41 ) This average response includes a tonic component , the baseline response to the constant background , and a phasic component , the flash response . Because of the non-linearity in the DA model , both components are modulated by the flicker , as compared to the deterministic case . Hereafter , we examine this solution in two cases: flicker with constant variance and flicker with periodically varying variance . We answer the following question: How does flicker affect the phasic and tonic components of the model photoreceptor response ?
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Photoreceptors constitute the interface between the visual world and the cerebral world , as they convert light inputs into neural signals . This conversion is subject to continuous adaptation: response gain and time scale vary as a function of input history . This adaptation is ‘dynamical’ both because it depends upon the temporal structure of the stimulus and because it affects the kinetics of the response . Traditionally , theoretical studies of photoreceptors fall within one of two extreme approaches: either detailed modeling based upon the biophysics of phototransduction or functional modeling based upon phenomenological descriptions of photoreceptor response . While the former approach involves too many coupled equations and unknown parameters to allow for analytical treatments , building intuition , or predicting trends , instances of the latter approach , such as the simple linear-nonlinear model , fail to capture essential features of dynamical adaptation . Here , we develop understanding at an intermediate level . We define and discuss a phenomenological model which is simple enough to allow for full solutions and predictions , but embodies features of phototransduction well enough to capture a rich phenomenology . We demonstrate that our model reproduces data with high accuracy and can be used to make predictions on the response to sophisticated visual inputs such as natural stimuli .
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[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[] |
2013
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Dynamical Adaptation in Photoreceptors
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Glucose is a major energy source and is a key regulator of metabolism but excessive dietary glucose is linked to several disorders including type 2 diabetes , obesity and cardiac dysfunction . Dietary intake greatly influences organismal survival but whether the effects of nutritional status are transmitted to the offspring is an unresolved question . Here we show that exposing Caenorhabditis elegans to high glucose concentrations in the parental generation leads to opposing negative effects on fecundity , while having protective effects against cellular stress in the descendent progeny . The transgenerational inheritance of glucose-mediated phenotypes is dependent on the insulin/IGF-like signalling pathway and components of the histone H3 lysine 4 trimethylase complex are essential for transmission of inherited phenotypes . Thus dietary over-consumption phenotypes are heritable with profound effects on the health and survival of descendants .
Aging is an inevitable process that affects all organisms and a better understanding of the underlying biological mechanisms is relevant to human health [1] . In nature , organisms struggle against environmental conditions to survive and hopefully reproduce . This is an energetically costly and persistent process , thus nutrient availability greatly influences an organism's life history with profound affects on survival , reproduction and lifespan . The life history of most organisms naturally consists of periods of low nutrient availability and core mechanisms have evolved to deal with nutrient stress , namely starvation or near-starvation . Research into the genetic underpinnings of nutritional state on health and longevity is an active area of research , with the mechanisms of dietary restriction taking the lion's share of recent genetic discoveries [2] . Modern industrialized societies no longer live in fear of famine , but instead live in conditions of a near perpetual feast . Unfortunately , diets high in sugar are linked to numerous health problems in humans [3] . However , we wondered why animal species will over-consume resources if given the opportunity and hypothesized there may be an adaptive benefit to such behavior . Using Caenorhabditis elegans to investigate over-consumption phenotypes we discovered that exposure to high glucose concentrations at one generational time point , the parental generation , had persistent and heritable effects in descendent progeny . Glucose promotes resistance against cellular stress and neurodegeneration in parental and descendent progeny , while reducing lifespan in the parental generation only . Furthermore , we found that glucose mediated phenotypes are dependent on known metabolic genes including components of the Insulin/IGF-like pathway , the sirtuin sir-2 . 1 , and AMPK , while the transgenerational inheritance of glucose-directed phenotypes are dependent on histone methylation enzymes . Thus , dietary glucose can induce the transmission of heritable , cellular phenotypes with profound consequences on health and survival .
We first focused on reproduction since in C . elegans the average number of progeny is a strong physiological phenotype showing transgenerational inheritance [4] , and wild type N2 worms cultured under glucose enrichment ( GE ) conditions have reduced progeny numbers [5]–[7] . Consistently we observed that parental generation ( P0 ) N2 worms exposed to GE had reduced total progeny numbers compared to untreated controls [5] ( Figure 1A ) , and that this effect extended to the descendent F1 and F2 generations ( Figure 1B–D ) . Thus , glucose can induce a heritable , transgenerational phenotype on progeny from a single exposure of the P0 generation . The insulin/IGF-signalling pathway is an evolutionarily conserved network of genes regulating an organism's response to nutritional states and is a major conserved regulator of aging [2] , thus we investigated its contribution to the transgenerational effects of GE on progeny numbers . DAF-16 is a forkhead transcription factor and the major downstream regulator of the insulin/IGF-like signalling pathway [8] , [9] and we observed that GE did not further reduce progeny in daf-16 mutants at any generational time point ( Figure 1 ) . We then examined genes known to respond to nutritional status and interact with both daf-16 and the insulin/IGF-like signalling pathway including aak-2 , which encodes the alpha subunit of the AMP-activated protein kinase ( AMPK ) [10] , and sir-2 . 1 that encodes an orthologue of the histone deacetylase SIRT1 [11] . Similar to daf-16 mutants , we observed that mutation in either aak-2 or sir-2 . 1 likewise blocked the progeny reducing effects of GE . Finally , we examined hif-1 , the C . elegans orthologue of mammalian hypoxia induced factor 1 ( HIF1 ) [12] , a protein that regulates glucose metabolism and the cellular response to low oxygen conditions and observed that GE continued to reduce progeny numbers in the P0 and F1 generation of hif-1 mutants . Exposure to high dietary glucose reduces the lifespan of wild type N2 worms , as well as the long-lived phenotype of worms with hypomorphic mutations in the gene encoding the worm's sole Insulin/IGF-like receptor DAF-2 [5]–[7] . We confirmed that GE reduced the lifespan of N2 and daf-2 worms in the P0 generation but found no evidence that this was a transgenerational phenotype since the F1 and F2 descendent progeny had lifespans similar to untreated control worms ( Figure S1 , Table S1 ) . In total , these data suggest that specific components of the insulin/IGF-like signalling pathway regulate the negative effects of GE on reproduction in C . elegans and that the heritable effects on reproduction can be separated from lifespan . Despite the negative effects on fecundity and lifespan , we previously reported that GE strongly protected worms against environmental stress [5] . We tested for resistance to oxidative stress using juglone , a natural product from the black walnut tree that produces intracellular oxidative stress and decreases the survival of N2 worms [13] . Treating P0 N2 worms with glucose provided potent protection against oxidative stress induced lethality , and this protection persisted into the F1 generation of N2 worms even though these F1 animals were never exposed to glucose ( Figure 2A ) . Glucose-mediated resistance against oxidative stress was not transmitted further since the F2 generation of N2 worms was sensitive to juglone ( Figure 2A ) . Having observed that a carbohydrate supplemented diet augmented stress resistance phenotypes , we wondered if this was due to a contribution from the worms' bacterial food source and/or and if other dietary supplements like protein or fat would also promote stress resistance . We discounted possible bacterial effects since GE continued to promote resistance to oxidative stress for worms grown on heat-killed bacteria ( Figure S2A ) . Additionally , we observed no augmented resistance to oxidative stress for worms grown on plates supplemented with either methionine or oleic acid [14] ( Figure S2B ) suggesting the stress resistance phenotypes may be limited to dietary sugars . We then tested if insulin/IGF-like pathway genes were required for protection against juglone by glucose and observed that similar to the effects on progeny numbers , daf-16 , aak-2 and sir-2 . 1 were all required for the heritable protective effects of GE against oxidative stress ( Figure 2B–D ) . Consistent with the effects of GE on progeny and lifespan , hif-1 was not required for the glucose-mediated protection against glucose ( Figure 2E ) . To further confirm that these genes were required for transmission , we treated F1 animals with RNAi against daf-16 , aak-2 and sir-2 . 1 and RNAi knockdown of each of these genes blocked the heritable transmission of stress resistance ( Figure 2F ) . These data suggest that key components of the insulin/IGF-like pathway are required for the heritable , protective effects of glucose against oxidative stress . Recent work showed a possible hormetic protection in response to oxidative stress , as a mild induction of the cellular stress response could increase long term resistance and longevity [15] . To investigate the possible role of glucose as a stress inducer , we stained the worms with dihydrofluorescein , a marker of oxidative stress [16] . We observed comparable levels of fluorescence for N2 worms treated with glucose compared to untreated control worms ( Figure S2C ) suggesting that glucose does not induce a generalized oxidative stress phenotype . Aging is a risk factor for many diseases including late-onset neurodegenerative disorders [17] . To determine whether or not glucose could provide generational protection against age-dependent proteotoxicity we turned to a well-characterized C . elegans model of TAR DNA-binding protein 43 ( TDP-43 ) motor neuron toxicity [18]–[21] . TDP-43 is a conserved RNA/DNA binding protein with mutant variants being causative for amyotrophic lateral sclerosis [22] . C . elegans expressing mutant TDP-43 in motor neurons show adult onset , age-dependent paralysis and neurodegeneration phenotypes that are diminished by treatment with glucose [5] . We observed that P0 generation TDP-43 worms treated with glucose had reduced rates of paralysis ( Figure 3A ) and axonal degeneration ( Figure 3B ) compared to untreated controls , and this protective effect persisted into the F1 generation . Thus , GE can reduce genetically encoded proteotoxicity and this effect is heritable . We next confirmed that GE did not reduce the paralysis rate through a hormetic stress response as dihydrofluorescein levels were indistinguishable from mutant TDP-43 animals treated with glucose ( Figure S2C ) , but also because glucose-mediated neuroprotection was not lost after treatment with the antioxidant N-acetyl cysteine ( Figure S2D ) . Work from Brunet and colleagues demonstrated that components of the histone H3 lysine 4 trimethylation ( H3K4me3 ) complex are essential to the transmission of transgenerational effects on longevity [23] . Thus , we hypothesized that genes encoding the H3K methyltransferase set-2 , and the H3K4me3 complex component wdr-5 . 1 would be required for the heritable transmission of glucose phenotypes on neurodegeneration , stress resistance and fecundity . First , we observed that H3K4me3 methylation was increased in P0 animals compared to untreated controls ( Figure 4A ) . We observed this methylation mark in both young worms at the L3 larval stage and adults , thus suggesting that tissue heterogeneity or the presence of eggs in older animals do not contribute to this phenomenon . However , the increased H3K4me3 methylation observed in P0 animals was not transmitted to the F1 or F2 generations ( Figure 4A ) . These observations are in agreement with work from Brunet and colleagues [23] , where the H3K4me3 complex is associated with the transgenerational inheritance of longevity , but the H3K4me3 mark is not heritable . One interpretation is that H3K4me3 may be an indirect effect arising from other functions of the H3K4me3 complex in regulating global physiological changes . We further investigated the genetic requirements of set-2 and wdr-5 . 1 for the transmission of glucose-mediated phenotypes . First we observed that GE suppressed the rate of paralysis in mutant TDP-43; set-2 and mutant TDP-43; wdr-5 . 1 mutants in the P0 generation , but suppression of mutant TDP-43-induced paralysis was not transmitted to the descendent F1 generation ( Figure 4B and 4C ) . Accordingly , GE reduced axonal degeneration in P0 animals but failed to rescue the degeneration in F1 animals carrying set-2 ( ok952 ) or wdr-5 . 1 ( ok1417 ) mutations ( Figure S3A ) . These data suggest that set-2 and wdr-5 . 1 are required for the heritable transmission of stress resistance phenotypes , but are not necessary for the stress resistance itself . To confirm this we tested set-2 and wdr-5 . 1 mutants , subjected to GE , against oxidative stress and observed that GE continued to provide protection against juglone in the P0 generation , but this protection was lost in the F1 generation ( Figure 4D and 4E ) and this phenomenon was confirmed by RNAi treatment against set-2 and wdr-5 . 1 ( Figure S3B and S3C ) . Likewise with the negative effects on reproduction , set-2 and wdr-5 . 1 mutants treated with GE had reduced numbers of progeny in the P0 generation , but progeny numbers returned to normal levels in the F1 descendants and subsequent F2 and F3 generations ( Figure 4F and 4G ) . However , the requirement of set-2 and wdr-5 . 1 for the transmission of glucose phenotypes may be independent of H3K4me3 methylation status . We confirmed that set-2 and wdr-5 . 1 are indeed required for the increased H3K4me3 methylation induced by glucose of the P0 generation of animals , since this methylation mark was abolished by set-2 or wdr-5 . 1 mutations ( Figure S3D ) . Despite this , we observed that H3K4me3 methylation was not transmitted from P0 to F1 animals ( Figure 4A ) suggesting that this mark is not associated with the heritable transmission of glucose-induced phenotypes . Thus set-2 and wdr-5 . 1 may have activities independent of H3K4me3 methylation for the heritable transmission of glucose-associated phenotypes . The transgenerational inheritance of longevity requires a functional germline [23] . Thus , we investigated the role of the germline in transmission of stress resistance after GE by using feminized mutants fem-3 ( e2006 ) that are not able to produce mature eggs at restrictive temperatures [24] and pgl-1 ( bn102 ) that is not able to form a functional germline at restrictive temperatures [25] . Both strains showed increased resistance to juglone under GE conditions in the P0 and F1 generation at 15°C , but the transmission was lost at 25°C in the F1 generation , indicating that the glucose-induced transmission of stress resistance is dependent on a functional germline ( Figure 5 ) .
Numerous strategies have evolved to ensure reproductive success for organisms in the face of changing and challenging environments [1] . It is believed that extended lifespan phenotypes observed under dietary restriction conditions maximize an organism's survival until environmental conditions improve allowing for reproduction . We discovered a novel diet-influenced reproductive advantage; animals subjected to high dietary glucose are resistant to protein damaging stress , and this resistance is transmitted to their progeny . The trade-off for stress-resistant progeny is decreased lifespan and fecundity in the parental strain suggesting that this strategy may be adaptive under nutrient rich conditions . In the P0 generation , high dietary glucose leads to negative effects on lifespan and reproduction along with increased protection against protein-damaging stress . The resulting F1 progeny are resistant to stress via an epigenetic mechanism along with a small reduction in fecundity , but these animals do not suffer negative lifespan effects . This dietary induced adaptation may be favorable from an evolutionary standpoint where in times of plenty an epigenetic program is initiated to maximize the survival of progeny at the expense of less total progeny and a decrease in parental health . This mechanism is reminiscent of antagonistic pleiotropy , where fitness in early life is increased at the expense of negative effects on health and fitness in late life [2] . Here , excess energy from high dietary glucose may be used to maintain proteostasis under stress conditions [5] , but only the parental P0 hermaphrodites pay the price of shortened lifespan . The recently proposed theory of hyperfunction may help explain these contradictions [26] , where the continued activity of developmental and growth programs later in life leads to decreased fitness and longevity [2] . Indeed , the majority of genes regulating lifespan have metabolic functions essential for development or growth , and perhaps the continued activity of these networks driven by excess dietary glucose leads to negative phenotypes . If true , we would expect that only those animals directly exposed to glucose would suffer negative effects and this is seen by the large reduction of lifespan in P0 , but not F1 animals . While the reduced progeny phenotype persists for two generations , the stress resistance phenotypes last only until the F1 generation , suggesting maternal inheritance rather than a transgenerational inheritance mechanism . Furthermore , we observe increased H3K4me3 methylation only in the parental P0 animals exposed to glucose , but not in the descendent F1 and F2 progeny . Thus , while the stress resistance phenotypes are passed to the F1 generation , the glucose induced methylation mark is not , so perhaps maternally deposited RNAs contribute to the glucose-induced phenotypes of the F1 progeny . One interpretation might be that H3K4me3 occurs on specific loci , and thus is impossible to detect by western blot . Alternatively , the complex may initiate large-scale physiological changes , and H3K4me3 may be an indirect , non-heritable effect simply indicating open chromatin and increased transcription . Finally , it is possible that an additional methylation mark , like H3K36me3 is involved [27] in the transmission of glucose-associated phenotypes . In any event , both histone modifying enzymes and the insulin/IGF-like pathway are necessary for the transmission of glucose-induced phenotypes . This is likely an adaptive phenotype since while the dietary status of the P0 animals may be predictive for the immediate environment of F1 animals , this may not be the case for F2 generations and beyond . Of interest is the comparison of phenotypes of high dietary glucose conditions versus nutrient stress from reduced feeding . Worms experiencing dietary restriction are long-lived [28] , have reduced fertility [29] , show resistance to thermal stress [30] but are not resistant to genetically encoded neuronal proteotoxicity [5] . These observations suggest that the bulk of dietary-restricted animals' resources are used to maintain survival , minimizing reproductive potential until environmental conditions improve . In contrast , high dietary glucose may represent a nutrient rich environment , and these animals have the luxury to enlist genetic programs promoting survival of their progeny . Given that an organism's life history is greatly affected by nutritional status , an open question is how far do cellular changes go in response to nutritional state . The emerging field of transgenerational epigenetic inheritance is providing evidence that certain epigenetic modifications persist over several generations [31] . Furthermore , the investigation of heritable phenotypic effects from sustained dietary changes is a developing field that may have implications on human health [32]–[34] . The investigation of the biological consequences of early environmental influences has a long history [35] , and there are numerous studies into the effects of prenatal diet on the health of offspring , including mammals [36] , but the molecular mechanisms are not well known . Future studies in genetically tractable models like C . elegans will be a powerful approach to unravel the epigenetic mechanisms of nutrient stress on healthy aging .
Standard methods of culturing and handling worms were used . Worms were maintained on standard NGM plates streaked with OP50 E . coli . In some experiments D-glucose was added to NGM plates ( all products from Sigma-Aldrich ) . All strains were scored at 20°C unless indicated . Mutations and transgenes used in this study were: daf-16 ( mu86 ) , sir-2 . 1 ( ok434 ) , aak-2 ( ok524 ) , hif-1 ( ia4 ) , set-2 ( ok952 ) , wdr-5 . 1 ( ok1417 ) , fem-3 ( e2006 ) , pgl-1 ( bn102 ) and xqIs133[unc-47::TDP-43[A315T];unc-119 ( + ) ] . Some strains were provided by the C . elegans Genetics Center ( University of Minnesota , Minneapolis ) , which is funded by NIH Office of Research Infrastructure Programs ( P40 OD010440 ) . Mutants or transgenic worms were verified by visible phenotypes , PCR analysis for deletion mutants , sequencing for point mutations or a combination thereof . Deletion mutants were out-crossed a minimum of three times to wild type worms prior to use . Mutant TDP-43 animals were scored for paralysis and counted as positive if they failed to move upon prodding with a worm pick . Worms were scored as dead if they failed to move their head after being prodded on the nose and showed no pharyngeal pumping . For the paralysis tests worms were grown on NGM or NGM +2% glucose and transferred to NGM-FUDR or NGM-FUDR +2% glucose . For the F1 generation , L4 animals were transferred from NGM +2% glucose to NGM and their progeny used as the F1 generation . The same method was used for the F2 generation . For scoring of neuronal processes from mTDP-43 transgenics , animals were selected at days 1 , 5 and 9 of adulthood for visualization of motor neurons processes in vivo . Animals were immobilized in M9 with 5 mM levamisole and mounted on slides with 2% agarose pads . Neurons were visualized with a Leica 6000 and a Leica DFC 480 camera . A minimum of 100 animals was scored per treatment over 4–6 trials . The mean and SEM were calculated for each trial and two-tailed t-tests were used for statistical analysis . For oxidative stress tests , worms were grown on NGM or NGM with a dietary supplement ( glucose , oleic acid or methionine ) and transferred to NGM plates +240 µM juglone at adult day 1 . For the F1 , L4 worms from NGM +2% glucose plates were transferred on NGM and their progeny used as F1 generation . The same process was used for the F2 generation . Worms were evaluated for survival every 30 min for the first 2 hours and every 2 hours after up to 14 hours . Nematodes were scored as dead if they were unable to move in response to heat or tactile stimuli . For all tests worms , 20 animals/plate by triplicates were scored . Temperature sensitive mutant fem-3 ( e2006 ) or worms were maintained at 15°C and switched to 25°C at hatching and kept at this temperature until tested on juglone . Temperature sensitive pgl-1 ( bn102 ) were maintained at 15°C and switched to 25°C at the L4 larvae stage and kept at this temperature until tested on juglone . Worms were grown on NGM or NGM +4% glucose and transferred on NGM-FUDR or NGM-FUDR + glucose . For the F1 generation , L4 animals from the NGM +4% glucose plates were transferred to NGM plates and progeny used as the F1 generation on NGM-FUDR . The same process was used to prepare the F2 generation . 20 animals/plate by triplicates were tested at 20°C from adult day 1 until death . Worms were scored as dead if they didn't respond to tactile or heat stimulus . For scoring progeny , 10 L4 worms were grown on NGM or NGM +2% glucose and placed at 20°C . Over the next three days individual worms were transferred to new plates and the L1 larvae were scored for each plate . For the F1 generation , 10 L4 larvae from the P0 were transferred to new NGM plates without glucose and this process was repeated for the F2 and F3 generations . For visualization of oxidative damage in the transgenic strains the worms were incubated on a slide for 30 minutes with 5 µM dihydrofluorescein diacetate dye ( Sigma-Aldrich ) and then washed with 1× PBS three times . After the slide was fixed fluorescence was observed with the Leica system described above . Worms were collected in M9 buffer , washed 3 times with M9 and pellets were placed at −80°C overnight . Pellets were lysed in RIPA buffer ( 150 mM NaCl , 50 mM Tris pH 7 . 4 , 1% Triton X-100 , 0 . 1% SDS , 1% sodium deoxycholate ) +0 . 1% protease inhibitors ( 10 mg/ml leupeptin , 10 mg/ml pepstatin A , 10 mg/ml chymostatin LPC;1/1000 ) . Pellets were passed through a 271/2 G syringe 10 times , sonicated and centrifuged at 16000 g . Supernatants were collected . All supernatants were quantified with the BCA Protein Assay Kit ( Thermo Scientific ) following the manufacturer's instructions . Worm RIPA samples ( 50 µg/well ) were resuspended directly in 1× Laemmli sample buffer , migrated in 14% polyacrylamide gels , transferred to nitrocellulose membranes ( BioRad ) and immunoblotted . Antibodies used: rabbit anti-Histone H3 Total ( 1∶1000 , ab1791 Abcam ) , rabbit anti-Histone tri-methylated ( 1∶1000 , ab8580 Abcam ) , and mouse anti-actin ( 1∶5000 , MP Biomedicals ) . Blots were visualized with peroxidase-conjugated secondary antibodies and ECL Western Blotting Substrate ( Thermo Scientific ) . Densitometry was performed with Photoshop ( Adobe ) . RNAi-treated strains were fed with E . coli ( HT115 ) containing an Empty Vector ( EV ) , set-2 ( C26E6 . 9 ) , wdr-5 . 1 ( C14B1 . 4 ) , daf-16 ( R13H8 . 1 ) or aak-2 ( T01C8 . 1 ) RNAi clones from the ORFeome RNAi library and sir-2 . 1 ( R11A8 . 4 ) clone from the Ahringer RNAi library . RNAi experiments were performed at 20°C . Worms were grown on either NGM or NGM +2% glucose both enriched with 1 mM Isopropyl-b-D-thiogalactopyranoside ( IPTG ) . For paralysis and stress-resistance tests , survival curves were generated and compared using the Log-rank ( Mantel-Cox ) test , and 60–100 animals were tested per genotype and repeated at least three times .
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Nutritional state has major effects on health and longevity , and investigations into the mechanisms of dietary restriction have taken the lion's share of recent genetic discoveries . We used Caenorhabditis elegans to investigate the role of diet on nematode physiology and report the surprising finding that exposure to high glucose at one generational time point has heritable effects in descendent progeny . Glucose promotes resistance against cellular stress and neurodegeneration in parental and descendent progeny , while reducing lifespan only in the parental generation . Furthermore , we found that glucose mediated protection is dependent on well-known metabolic and stress response genes . Numerous strategies have evolved to ensure reproductive success in the face of changing and challenging environments . It is believed that extended lifespan phenotypes observed under dietary restriction conditions maximize an organism's survival until environmental conditions improve allowing for reproduction . We discovered a novel diet-influenced reproductive advantage; animals subjected to high dietary glucose are resistant to protein damaging stress , and this resistance is transmitted to their progeny . The trade-off for stress-resistant progeny is decreased lifespan and fecundity in the parental strain suggesting that this strategy may be adaptive under nutrient rich conditions .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"invertebrates",
"medicine",
"and",
"health",
"sciences",
"caenorhabditis",
"neurodegenerative",
"diseases",
"animals",
"animal",
"models",
"caenorhabditis",
"elegans",
"model",
"organisms",
"glucose",
"signaling",
"epigenetics",
"motor",
"neuron",
"diseases",
"research",
"and",
"analysis",
"methods",
"signal",
"transduction",
"cell",
"biology",
"neurology",
"genetics",
"nematoda",
"biology",
"and",
"life",
"sciences",
"molecular",
"cell",
"biology",
"cell",
"signaling",
"organisms"
] |
2014
|
Heritable Transmission of Stress Resistance by High Dietary Glucose in Caenorhabditis elegans
|
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